A review of techniques and applications of lesion identification in MRI scans

8/14/2019 A review of techniques and applications of lesion identification in MRI scans

1/31

1

A review of te chniques and a pp lications of lesion ident ificat ion in MRI scansDaryl Shepp ard

Abstract Magn et ic Resonance Imaging (MRI) is the favou red technique for the ident ification o f lesions over othe r available me tho ds d ue to its capab ility to b e used in a wide varietyof examinations as well as the fact it is non- invasive and doesnt make use of non -ionizing rad iation (Stamatakis & Tyler, 2005).

It d oes however; presen t t he med ical professiona l with the challeng e o f providing aconstan t and reliable me tho d o f iden tification of lesion areas that is repeata ble

across different op erato rs as well as the need in som e m ed ical cond itions to p rovidefast and accurate d iagn osis of the affected areas to d ete rmine an app ropriate courseof treatment .

This pap er will review e xisting resea rch surround ing this pro blem. It will not focusspecifically on any lesion t ype o r application to any part icular d isease bu t will loo k atthe p rob lem in b road te rms. Three ma in areas will be covered ; section 1 will discussmanual segmentat ion techniques an d issues surroun ding this approach, section 2 willdiscuss the de velopment o f auto mat ic segme ntat ion t echniques and section 3 willfocus on n ew techniques and app roaches which show som e pro mising results andmay be the atte ntion of further future research.


2/31

2

1. IntroductionMagn et ic Reso nance Imaging was first

discovered in the 1950s and u sed initially inthe field of spe ctroscopy.

It was not until the 1970s when workundertaken by Lauterbur expanded the useof Magnetic Resonance Imaging intomedical applications which then enabledexaminations of the human body in vivo(Liney, 2005).

The technique produces the MR imagethrough the detection of the presence ofhydrogens (protons) within the body. TheMRI machine subjects these hydrogens to alarge magnetic field which partiallypolarizestheir nuclear spins. The spins are thenexcited using tuned radio frequencyradiation. Radio frequency radiation is thendetected from them as they relax from this magnetic interaction.

The frequency of the signal from the proton is proportional to the magnetic fieldapplied during the radiation process. Using these signals, a map of the body areascanned is then produced which forms the magnetic resonance image. (Nave)

While the use of MRI scans has provided good insight into the pathology of thehuman body, for the identification of lesions it presents some areas of concern.

Broadly speaking there are two main categories of lesions that are of interest tomedical professionals;

white matter lesions (WML), resulting in blood-brain barrier damage(Calabrese, et al., 2008)

gray matter lesions (GML), resulting in demyelination of nerve fibres(Calabrese, et al., 2008)

These lesions point towards a number of different medical conditions and theiridentification is often paramount to determine treatment for the patient as well ascritical in monitoring the effects of drug therapy in clinical trials. (Van Leemput, Maes,Bello, Vandermeulen, Colchester, & Suetens, 2000)

Figure 1 Examp le o f a no rmal b rain MRI imag e (McGillUniversity, 2 006 )


3/31

3

The process of segmenting the MRIscan of patients with WML is difficultbecause the characteristics of WML aresimilar to those of gray matter.Techniques such as intensity based

statistical classification potentially mayclassify some WML as gray matter andsome gray matter as WML. (Warfield, etal., 1995)

To furthe r highlight the subtletiesinvolved in t he process req uired tosegment lesions from an MRI scan, takethe imag es d isplayed in Figure 1 andFigure 2 as an example.

These are simulated images gene ratedfrom the online BrainWeb resource(McGill University, 2006). Both imagesshow a T1 MRI scan taken in 5 mm slices(slice 21 d isplayed).

The differences bet ween the two image s are very subtle and ide ntifying the lesionwould be a d ifficult t ask taking into account the po ten tially large num ber of imag es

with in a stand ard MRI scan. Add itionally, an o pe rato r examining a large numbe r of images in a given work day may eventually start to misidentify some of the lessapparent lesions. Add to this a level of complexity introduced due to varying typeand qua lity of imag es u nde r review.

Due t o t he fact tha t MRI techniques were well estab lished prior to any concept of anauto mated met hod for analysis, the first techniques developed to assess MRI scanswere of course m anua l. These consisted of trained o pe rators following a p rede finedmeasurement scale a s will be d iscussed in m ore de tail late r in t his pape r.

With advancemen ts in t he areas o f com put er assisted analysis and its ap plication tothe med ical profession, a numb er of techniques have bee n de veloped whichauto mate the work of the trained op erato rs. These include two main cate go ries; fullyauto mated or hybrid app roach which still req uires som e involveme nt with a t rainedoperator

A large amo unt of the literatu re I covered througho ut t he cou rse of this reviewcovered the app lication of lesion segm entation in relation to its ap plicationspecifically to the disease of Multiple Sclerosis. Certa inly ot her lesion -causingdiseases have been covered such as Alzheimers and stroke. It should also be noted

Figure 2 Example of a brain MRI image showing MSlesions. (McGill University, 2006)


4/31

4

that in the research covered, the specificity of th e app lication to the disease MultipleSclerosis by no m eans invalidat es the a pp lication of the lesion segm entationtechnique to that d isease.

It is notab le, and p ossibly can be drawn as a cause resulting from the ob servationabo ve, the majority of the lesions seg men tation te chnique s also focus on thesegme nta tion o f WML with po tential app licability to t he segmentation of GML. Withan initial be lief that Multiple Sclerosis is prima rily a disease of the white ma tter(Kutzelnigg & Lassmann , 2005) this may have resulted in a d ispro po rtionate focus onthe segmentation of WML over GML.

This dispropo rtion would seem to have been the focus of som e att ent ion at leastwith in th e last ten years which has drawn conclusions tha t Multiple Sclerosis also hasan impact o n th e cause of lesions within gray matt er structures (Kidd , Barkhof,McConnell, Algra, Allen, & Revesz, 1999). Demyelination has also been notedprom inen tly in the g ray mat ter o f deep cerebral nuclei and the cereb ral cortex.(Kutzelnigg & Lassm ann, 2005).

Where possible the ap proach taken in this review has been t o look at the p roblem of lesion segm entat ion divorced from any specific disease o r specific lesion type. Myob servations throu gho ut the course o f this review have primarily revealed that theissue o f segm entat ion exists across most applications o f MRI techno log y. That saidhowever, the focus of the source material used within this review has a narrow focus

towards specific applications. It is conceivable that future developments within thefield of MRI techno logy may address som e o f these issues by prod ucing imag es thatmo re clearly ide nt ify the areas of inte rest. However, until tha t stage lesionsegm entation will be a necessary area o f research and development .


5/31

5

2. Manual Segmentation

2.1 IntroductionThe concept behind m anua l segmentat ion is fairly simp le; provide a rating system ,usually numeric, and an accurate description that enables a similar result acrossdisparate operators and applications.

Over time and in the absence of any automate d quantitative m ethod ology to assessMR imag es, op erato r observation t echniques develop ed .

2.2 White Matter Lesions on CT and MRIOne such technique (van Swieten, Hijdra, Koudstaal, & van Gijn, 1990) focused onwhite ma tte r lesions within CT and MRI scans iden tifies in addition to the p rop osedscale, three key observations that could be applied effectively to any manual ratingsystem. They are:

1. The scale used should incorporate ana tom ical distribut ion and severity and provide clear definitions for each of the different categoriesAny scale use d sho uld be app licable to a given anatom ical area an d p rovide a

mea sure of the severity of the lesion be ing examined

2. Simple.Given this is a quantitative measure involving operator observation, a granularapp roach to rating would increase the likelihood of variation b etweenop erators. As such, the scale needs t o rema in re lat ively simp le with clearlyde fined categories that m ost reaso nab ly trained op erato rs can read ily iden tifyagainst.

3. The scale shou ld be assessed for reliability against an inter-observer study.The key mechanism involved within this type of approach is a human element.As a result, a numbe r of facto rs can po tent ially be involved which may bias theresult. Aspects such as ope rator t raining, timeframe, equipm ent / imag e q ualitymay all play a part in producing different results across different operators.While the hum an element canno t be reduced en tirely, it can be mitigat ed b ystudies tha t p rovide a sta tistical measure of the accuracy of observationsmad e ag ainst th is scale.

While the paper did cover specifically the application of this scale to white matter

lesions within CT or MR image s, the obse rvat ions abo ve and the principles ou tlined


6/31

6

in the scale could be read ily applied with only minor modification and tuning to mo stlesion g rading requirement s.

This system identified three severity categories and associated definitions

Grade Description0 No lesion or on ly a sing le one1 Multiple focal lesions2 Multiple confluen t lesions scattered throu gho ut the white

matterTable 1 Three grade rating system, (van Swieten, Hijdra, Koudstaal, & van Gijn, 1990)

During t he stud y conducted for this pape r, examinations were unde rtaken on b othCT and MRI scans.

For the MRI scans, twenty four images were obtained from a study of elderlyhyperintensive patients. The results from the MRI portion of the study werecalculated using kappa stat istics with a weighte d value of 0.78.

While t his would seem a reasonab le o utcom e, the conclusions draw within this pape rraise two m ain q uestions:

1. Is the sam ple size o f 24 sufficien t t o d raw this conclusions2. The o nly mea sure of success of this me tho do log y is a me asure gene rated

using kap pa stat istics. The ut ility of th is measu re for t his type o f analysis isseen as con troversial with op inions d iffering as to its app licability (Ueb ersax,2002).

2.3 ARWMC ScaleAnother manual segmentation technique (Wahlund, et al., 2001) takes a very similarappro ach to tha t identified above. This technique , the ARWMC (Age Related White

Matter Change) scale uses two four point scales divided across two different regionsof the b rain.

As you can see from the scales identified in Tables 2 and 3, the three keyobservations identified above are present within this scale; anatomical and severitymeasurements have b een iden tified , the scale is simp le and (as the study indicates)provides good inter-rater reliability.


7/31

7

Grade Description0 No lesions (includ ing symmetrical, well-d efined caps or bands)1 Focal lesion s2 Beg inning confluence of lesions

3 Diffuse involvement of the entire reg ion , with or witho ut involvem ent of Ufibres

Table 2 White Matte r Les ion Scale f rom AWRMC scale

Grade Description0 No lesions1 1 focal lesions ( 5 mm) 2 > 1 focal lesions3 Con fluent lesionsTable 3 Basal Ganglia Les ion Scale from AWRMC scale

The ob servations of th is stud y were cond ucted across bo th MRI and CT image s. Theresults of this study indicated good inter-rater reliability of each of the scans. Itshould b e note d t hat similar statistical measures were used to reach th is conclusionand therefore the same issues as identified by (Uebersax, 2002) could potentiallyapply.

2.4 IssuesManual segmentation was essentially born from necessity. MRI and other scanningtechno log ies p rovided insigh t into a reas of the hum an b od y where in vivoexamination had never been ab le t o be pe rforme d p reviously. While t echniques weredeveloped to app ly this type of meth od ology in a consistent and scientific manne r,som e sho rtfalls could rea listically never really be ad eq uat ely addressed . These issuesinclude;

1. Generally a high-level of expertise will be required

In essence; this process will on ly involve two elements; the rat er and t heimages. There is little additional assistance provided to complete this task

2. The process is tim e and labour consum ing Each image needs to be carefully examined in great detail. With thisrequirement and the large num ber of imag es involved in a given MRI scan,this is a large amo unt of work to complete

3. The process is subjective and therefore not reproducible While stat istically, man ual segmentat ion met hod s have proven to be more orless reliable, the sub jective nature of assessmen t cannot be eliminated ent irely(Stokking, Vincken, & Viergever, 2000)


8/31

8

While t he future direction of lesion segm enta tion rests with b ett er and mo re efficientautom ated processes, it should b e no ted that manual segment ation processes stillhave a p lace as viable to ols to validat e new met hod ologies. A num ber o f stud ies suchas those co vered in late r sections with in t his review (Anbeek, Vincken, van Osch,

Bisschops, & van der Grond, 2004); outline steps taken to perform manualsegmentation as part of the validation of the proposed automated techniques. Thishighlight s the nee d t o m aintain expertise within this area of study.


9/31

9

3. Automatic Segmentation

3.1 IntroductionThe fund ame ntal flaw in th e m anua l segmen tation app roach is the inconsistency of the huma n op erato r. A num ber of factors nee d to be t aken into accoun t which mayresult in errors during an assessment. These include:

Training level; each op erator m ay be at a varying level of expe rience andexpertise

Time constraints; a manual segmentation approach will take time, with alarge number of MRI scans to assess an op erato r may no t have sufficienttime to make an ade quate ident ification

Large lesions; if a lesions is large eno ugh to be spread over a num be r of different image slices, th is may lead to the full extent of a given lesion notbe ing accurate ly assessed

To th is end, studies have b een d evoted to prod ucing au tomat ed metho ds for thesegmentation of MRI scans.

Auto mat ic procedures will remo ve a nu mber of human related issues and prod uce amo re consistent result across any num ber of op erato rs.

A number of different me tho do log ies have been developed to achieve this. Whileeach takes a unique app roach, there are also a num ber of common element s that aregenerally present within each; uniformity correction, a m ethod used to correct forany inho mo geneities that are presen t within the scan; pat ient movement, correctionfor any inconsistencies introd uce due to the movemen t of the pa tient during thescan; isolate brain tissue; minimize the size of the p roblem by ensuring th at the on lyareas of the scan that are examined are the required areas and no t areas of non-interest such as cerebrospinal fluid (CSF) or skull

Add itionally, two main ap proaches can be identified across the different t echniques;fully automated segme ntation, a process able t o b e p erformed by an op eratoruntrained in image segm entation and analysis; and partially auto matedsegm entation, a process still requiring so me imag e seg mentat ion and analysisdecision making by a skilled operator.


10/31

10

3.2 k-Nearest Neighbour Technique

3.2.1 IntroductionA met hod ology used in a numb er of different lesion segm entation techniques is that

of the k-Nearest Neighbour classification.

Used within the problem of lesion segmentation, this classification algorithm makes ade termination of the classification o f a g iven voxel based upon the classification o f itsneighbouring voxels and a predefined learning set of voxels provided to the systemprior to a segmentation a ttempt. (Statso ft Inc., 1984-2008).

The k-Nearest Neighbo ur (k-NN) algo rithm is an e xamp le o f a type of auto matedmachine-based learning where a g iven o bject is labe lled based up on t he frequencyof that label in comp arison to its neighb ours (Columb ia University, 2007); (van denBosch, 2009).

During the course of this review, I found three different approaches which make useof this classification methodology.

3.2.2 Probability MapsIn th e ap plication we see dem onstrated here (Anbeek, Vincken, van Osch, Bisschop s,& van d er Grond , 2004), the learning elemen t is und ertaken b ased upo n a feature sspace.

This specific imp lemen tat ion of this algo rithm m akes use of five d ifferent t ypes of MRI including: T1-weighted (T1-w), Inversion Reco very (IR), Proton Density-Weighted(PD), T2-Weighted (T2-w) and Fluid Attenuation Inversion Recovery (FLAIR)

The implementat ion of the k-NN algo rithm for this study det ermines a feature spacebased upon voxel intensity features and spatial information. The result of thisme tho d is the g ene ration of an imag e (probab ility map ) represen ting the p robabilityon a per voxel basis of a given voxel being part of a WML. (Anbeek, Vincken, van

Osch, Bisschops, & van der Grond, 2004).

These prob ability maps were t hen evaluate d using two me tho do log ies; binarysegm entation and direct probab ility evaluation .

For the binary segm entation evaluation, varying thresho lds were app lied to thepro bability map to create different segmentat ions of the WMLs. From this a ROCcurve analysis was taken from the True Positive Fraction (TPF) as a function of theFalse Positive Function (FPF). (Anbeek, Vincken, van Osch, Bisschops, & van derGrond , 2004)


11/31

11

In ad dition to this, each b inary segmentat ion were evaluate d u sing three differentsimilarity measu res; Similarity Ind ex (SI), a m easure for the correctly classified lesionarea; Overlap Fraction (OF), a measure of the correctly classified lesion area relativeto only the reference WML area; Extra Fraction (EF), a measure of the area falsely

classified as lesion relative to the reference WML area (Anbeek, Vincken, van Osch,Bisschops, & van der Grond, 2004).

These m easures were de fined by

=2

2 + +

=+

=+

(Anb eek, Vincken , van Osch, Bisschop s, & van de r Grond , 2004)

For the pro babilistic evaluation, each result was ana lysed using p robab ilistic versionsof the similarity measures. These measures; the probabilistic similarity index (PSI),probabilistic overlap fraction (POF) and the probabilistic extra fraction (PEF) arede fined by:

= 2 , =11 , =1 +

=, =1

1 , =1

=, =0

1 , =1

(Anb eek, Vincken , van Osch, Bisschop s, & van de r Grond , 2004)

The p reparat ion p rocess app lied t o each o f these imag es include d th ree step s.

Step 1 - Inhomogeneities correction ; this step involved the ap plication o f a process inwhich the intensity histogram of each given image is transformed into a standardhistog ram (Nyul & Udupa , 1999). This is do ne in a two stage pro cesses. Stag e 1 is thetraining stage where param ete rs of the stand ardizing t ransforma tion are learnedfrom a set of images. This stage identifies specific landmarks of a standard histogramand is estimated from a g iven set of volume images. (Nyul & Udu pa, 1999). Stag e 2 is

the transformation stage . The image intensity scale is com put ed by mapping the


12/31

12

landm arks determined from the image histog ram to those o f the standardhistog ram. (Nyul & Udup a, 1999).

Step 2 - Correction for difference due to patient m ovem ent; all patient imag es were

reg istered b y rigid reg istrat ion (translation and rotation) (Anbeek, Vincken, van Osch,Bisschops, & van der Grond, 2004).

Step 3 - Reduce am ount of data to be investigated; this stag e uses a technique calledMBRASE (Morp ho log y-based Brain Seg me ntation). This is a seg mentation pro cessthat uses a reg ion -ba sed growing technique. A seed pixel is selected in the g ivenimag e and neighbouring pixels are add ed p rogressively based upo n the ir meet ingset crite ria such a s maintaining a p articular intensity range (Stokking , Vincken, &Viergever, 2000)

3.2.3 ResultsThe end result of this process is a probability map which maps against each voxel thepro bability of it b eing a lesion . Add itionally it a lso p rovide s spa tial and volumet ricinformation about the identified WML.

This classification me tho d resulted in a high d eg ree o f accuracy for a rang e o f different lesion sizes. While focussing on WML for th is study, the a uth ors d oacknowledge the furthe r possible utility of the m etho d in the iden tification of oth erlesion types.

Possibly a d isadvantage t hat this method clearly brings is the need for multiple typesof MRIs to b e conducted. It has bee n observed gene rally in the o the r pap ersreviewed that an o bjective is to stand ardise the app roach to lesion seg mentat ion anduse MRIs that would ha ve already been taken for other d iagnostic reasons rather tha trequiring image s to be taken for this specific pu rpo se. However, th is is obviouslyneede d to be weighed aga inst the relative success of this technique by com parisonto othe rs and the situation required.


13/31

13

3.2.4 Brain Atlas Method Another study utilizing the k-Nearest Neighbour technique (de Boer, et al., 2009)takes an app roach that utilizes the registration of brain-atlases. This method is a twostaged approach using T1-weighted and FLAIR MRI.

This study identifies a fully automated methodology for the segmentation of CSF,gray matt er and white mat ter and WML. The techn ique out lines:

The use o f atlas reg istration to auto mat ically train a k-nearest neighbourclassifier

Automatic WML segmentation

Twelve brain atlases were acqu ired for this stud y. These at lases were sou rced fromthe Rot terda m Scan Study; a large p op ulation -based imaging stud y condu ctedbe tween 1995-1996 consisting of approximately 1700 subjects who u nde rwent MRIscan which was th en manua lly segm ented (de Leeuw, et a l., 2001). This followed withthe acquisition of test data t aken from the Rott erdam Scan Stud y conducted 2005-2006. This study involved 215 subjects.

The seg mentat ion p rocess consisted of two main stag es, brain tissue segm entation;ide ntifying gray matter, white matt er and CSF, and WML lesion segm entat ion ; thefinal stage of the process.

In the first stage (brain t issue seg mentat ion ), the CSF, gray mat ter and white mat ter

are au tomat ically seg mented using the trained k-Nearest Neighbour classifier withthe T1-weighted image. The training samples for the k-NN classifier are obtainedfrom the subject via atlas-based reg istration using e ithe r one o r more reg istrations of atlases to the sub ject.

In the second stage (WML lesion segmentation), a process of thresholding is appliedto o btain the segm enta tion of the WML. Initially WMLs present in the imag e aremisclassified at gray matter with a halo of white matter (de Boer, et al., 2009). Fromthis image, a histogram is then created of all voxels in the image classified as gray

mat ter. Within this histog ram, the h ighe st peak correspond s to the t rue gray matte rvoxels with the intensities corresponding to the WML voxels located to the right of this peak. The histogram is the n smoo thed by a convolution with a Gaussian kernelmaking it possible to estimate FLAIR intensity corresponding to the centre of thegray matte r peak by the histog ram bin containing th e mo st true po sitive gray matte rvoxels (de Boer, et al., 2009).

3.2.4.1 ResultsThe final analysis of the results from this study showed a high degree of accuracythat was validated by a separate and indep end ent m anual segm entation process.


14/31

14

By com parison to the previously identified me tho d a s well as the m ethod out lined insection 3.2.5, this app roach requires the least num be r of MRI image s which wouldpresen t an ad vantage to time, cost and p ossible dual use of scans.

3.2.5 k-Nearest Neighbour and TDS + This stud y expan ds o n p revious work cond ucted by the auth ors. In the previous work,the au thors develope d and validated a tem plate-driven segm entation meth odo logycombined with heuristic partial volume correction algorithm (TDS + ). In this study, thework has been expand ed upo n to develop an auto mated three-channel TDS (3ch-TDS + ) MRI segme nta tion p ipe line for the ide ntification o f MS lesion subtypes (Wu, etal., 2006).

There are five stag es involved in this metho do logy which ut ilise Prot on Density, T2and contrast- enhanced T1-weight ed imag es. These are d escribed a s follows.

3.2.5.1 Segmentation of the Intracranial Cavity Masks of the Intracranial Cavity were g enerat ed from the Proto n Density and T2imag es. This was d one utilising an e xtraction proced ure com bining non -pa ramet ricintensity-based statistical (Parzen windows) segm entation and automatedmo rpho log ical ope rations (Wu, et al., 2006). Parzen windows are similar to k-NN. Thekey difference b eing that k-NN will loo k at k closest po ints t o the de signa ted trainingda ta whereas with a Parzen window, a fixed distance is conside red (Vawter).

Further segmentat ion o f mat erial not of inte rest was undertaken by superimp osingthe masks onto the Proto n Density, T2 and contrast T1 imag es.

3.2.5.2 Image CorrectionOnce the Intracranial Cavity masking was complete , EM segm entat ion was app lied toprovide inho mogeneity correction and intensity normalisation. The EM segmen tercomp ensated for intra/ inte r-scan inten sity inho mo geneities and norma lised the scanintensities.

3.2.5.3 k-Nearest Neighbour SegmentationThe k-Nearest Neighb our segm entation ap proach selected was develope d based onFriedmans k -NN a lgorithm (Friedm an et al., 1975; Warfield, 1996) (Wu, et al., 2006).

Two stage s were involved with th is pro cess. In a similar fashion as with ot her k-NNbased approaches, a learning phase was initially required. For this implementation,two randomly chosen (from the full set of scans used within this study) were selectedas calibration scans. The information o bta ined from this process was then app lied tothe remaining scans in th e stud y.


15/31

15

3.2.5.4 TDS + TDS + (Template Driven Segmentation and partial volume artefact correction) wasapplied to correct misclassifications after the k-NN segmentation process. Thisimp roved lesion classification by pro viding a p riori ana to mical pro babilities.

3.2.5.5 Refinin g Black Holes Segmentation The black holes in the MRI image previously identified in the k -NN segmentationstage do not include areas of the white matte r that are hypo inten se with respect tohealthy white matt er but isointense with resp ect to g ray matt er (Wu, et al., 2006). Toaddress this, an additional classification step is taken to refine the black holes toinclude subtly hypo inte nse signals. To this en d, a mo re sensitive k-NN classifier isobtained by adding training points from mildly T1-hypointense WM regions (Wu, etal., 2006).

3.2.5.6 ResultsThe results of this study when com pared to m anua l tracing d emonstrate d that the k-NN segme ntat ion was able to identify most o f the lesions.

Most notable is that three types of misclassifications were apparent. These included;misclassification of choroid plexus and other enhancing vascular structures asenhan cing lesions, misclassifications o f subt le signa l abno rmalities of the whitema tte r as gray matte r and misclassification of pixels on the co rtical surface as whitematter lesions (Wu, et al., 2006).

With these issues ide ntified , it would ge nerally appear t hat furthe r examination of this technique is required. The authors outline in their discussion on these findingsvarious mo difications and other enhancements applied to the original metho do logy.


16/31

16

3.3 Gray Matter AtrophyAn a lgo rithm developed (Nakamura & Fisher, 2009) focuses o n the measurement of gray matt er at rop hy in MS pat ien ts. While not specifically loo king at lesion load , th isapp roach could be used in det ermining WML load as damage to the white matte r

has be en sho w to be associated with upstream gray matte r atrop hy (Sepu lcre, et a l.,2009).

This algorithm (Nakamura & Fisher, 2009) approaches the problem by thecombination of intensity, anatomical and morphological probability maps. It usesanalysis from FLAIR and T1-weighted images as well as brain atlas information.

The intensity based probab ility map is gene rated with a m od ified fuzzy c-means(FCM) clustering m ethod to generate prob ability map s for each t issue type.

(Nakamu ra & Fisher, 2009). During the course o f this study, the FCM was app lied tothe T1-weight ed imag es.

The anatomy-based probability map was derived from the Harvard Brain Atlas, a 3-Ddigitized a tlas of the h uman b rain designed for use with MR image sets (Kikinis, et al.,1996). The process at this stage involved converting the atlas to a general GMprob ability map and then app lying mo rpho log ic ope rations and Gaussian filters tosmoo th the result. The converted m ap is then aligne d with each p atients MRI using a12 DF affine transformation (Nakamura & Fisher, 2009).

The individualized morphological probability map is created from morphologicalmo de ls of the cortical and deep GM.

The final stage of this process creates a combined probability image which is aprod uct o f all of the GM probability maps. The binary GM mask is the n g ene rated bysetting a thresho ld o f 0.5 on the comb ined prob ability map. The n ormalized Gmvolume is defined as:

=

(Nakamura & Fisher, 2009)

Four d ifferent tests were d evelop ed to validat e t he results of this method. Theseinclude d; segmentat ion of simulated MRI dat a and com parison to correct results,segm entation o f real MRI dat a and comp arison to manual tracing results,segmentation of scan-rescan images to determine the reproducibility of the methodand segm entation of the same image with simulated MS lesions to de termine theeffects of lesions on the results.


17/31

17

Simulated MRI data was used to determine the accuracy in terms of volumetric errorsand similarity indices by com paring the segm ented tissues ma sks to the go ldstand ard t issue masks. The e valuation were conducted aga inst th e results using thesimilarity index defined as

=2

2 + +

(Nakamura & Fisher, 2009)

MRIs from t hree MS patients and three normal cont rols were used to evaluate thesegme nta tion accuracy of the algorithm in rea l MRIs. Each image was processedthrough the algorithm and then the GM was manually traced in a separate p rocess.Analysis was conducted on each o f these re sults.

For a separate study, MRIs were obtained from nine MS patients. Each of the imageswere analysed with the reproducibility of the algorithm evaluated by calculating thecoefficient of variation of GM volumes calculated from repeated images of eachpatient. (Nakamura & Fisher, 2009).

The final test measured the effect of WML in the FLAIR images. To achieve this test,masks of segme nte d MS lesions were simulate d with in the MRI image s. This test wasconducted over 18 MS pat ients.

The results of each of these tests are detailed in full within the study (Nakamura &Fisher, 2009). This particular meth od olog y brings with it a numbe r of advant agesover other stud ies.

The req uirements for this me tho do log y are similar to tho se requ ired for patientsundertaking normal MRI procedures. This makes this process greatly applicable tomany standard MRI tests in retrosp ect witho ut the need t o spe cialised image s to b etaken for the purpo ses of applying this me tho do log y only.

3.3.1 ResultsStatistically the results from this methodology appear to be promising. Additionally, anum ber of ob servations were made t hat p rovide ad ditional benefit to the use of thismethodology.

Comparison to other GM segmentation methodologies has identified an advantageover other me tho do log ies such as SPM (Ashburner) and partial volume mode l(Shat tuck). The similarity index for t his methodo log y was 0.938 com pared to theother methodologies reporting 0.932 and 0.893 respectively. (Nakamura & Fisher,2009).


18/31

18

Statistically this methodology doesnt correlate t he m easurem ent o f GM volumesstrongly to lesion volume in comparison to methodologies such as SPM. Thiseliminates the nee d for any form o f manual correction to correct segm entation errorsbe tween the GM and lesions volume s.

An interesting p oint to no te with this study (which is further expande d upo n insection 3) is the application o f an ind irect measure to achieve a re sult. That is, themeasurement o f one element that is known can also provide informa tion in rega rdsto an oth er element t hat is not known. This may not seem the m ost d irect appro achto achieving the desired seg mentat ion , however it may provide an e asier measure o rat least confirmation o f a known measure.

While t his stud y focuses o n an ap plication to MS, an application to a rang e o f me dical cond itions such as schizop hren ia, HIV de ment ia and Alzheimers diseasecould also be applicable. (Nakamura & Fisher, 2009).


19/31

19

3.4 Measuring the Whole Brain StructureAn app roach taken within a numb er of metho do log ies covered has be en to loo k atsegm entation issue from the pe rspective o f the ent ire brain structure and then divideand segm ent into its respective classifications of mat ter.

This approach differs in the manner that it doesnt initially focus on the immediateide ntification o f GML or WML bu t ad dresses each com po nent o f the b rain. From thismacro scale analysis, it wou ld be po ssible to identify each comp onent of the braineventually eliminating everything o the r than the area o f interest by a p rocess of eliminat ion if nothing else. This metho do log y wou ld be particularly ben eficial inapp lication to longitudinal studies where m easurem ents o f the cou rse of the stu dycould very easily ident ify areas o f change.

One such ap plication o f this me tho do log y (Iosifescu, et al., 1997), imp lements thisapp roach using an atlas imag e and elastic matching from auto mat ically segm entedMRI scans.

3.4.1 Automated SegmentationThe first stage in th is me tho do log y is to pe rform the initial segm entation o f theimag es. For this stag e, a segm entation me tho do log y selected was that pub lished b yWells and co-workers (1996). (Iosifescu, et al., 1997). This was a two stage processinitially segmenting the image into white matter, gray matter and CSF. The secondstage then further segmented the image into cortical gray matte r, subcortical graymatter, white matter and CSF. This methodology used a priori knowledge of tissueproperties and intensity inhomogeneities to correct for intensity differences in MRIdata. (Iosifescu, et al., 1997).

3.4.2 Image CorrectionThe ne xt stage in this me tho do log y was to m atch the atlas brain imag e on to th epat ient brain imag e. This was unde rtaken with a linea r registration program designedto correct for d ifferences in size, rot ation and t ranslation between the two images.(Iosifescu, et a l., 1997). The linear reg istrat ion pe rform ed an alignme nt of the two

dat a sets t hroug h a comb ination o f energy minimisation reg istration techniques. Theout come of this stage was an atlas brain imag e linea rly registered o nto the p atientbrain imag e. (Iosifescu, et al., 1997).

3.4.3 Elastic MatchingThe p rocedures used to elastic match the source and target data (segmen ted atlasand segmented patient image) was Denglers regularisation procedure (Dengler et al.1988; Schmidt and Dengler, 1989).


20/31

20

This process used a procedure that warped the atlas image onto the patientsimag e. Due to the nature of the two imag es, a simp le un iform g lob al displaceme nt(translation, rotation or scaling) would not work. (Iosifescu, et al., 1997)

3.4.4 Application to Lesion Segmentation and IdentificationAs identified earlier, this technique is not specifically aimed at the segmentation andide ntification of e ithe r WML or GML. However, it would app ear to have the capabilityof being app lied t o this problem.

The results from th is study det ermined that t he m ethod ology outlined is able tome asure the volumes o f brain structures with a very high level of accuracy. (Iosifescu,et al., 1997).

This capab ility could b e u tilised to assist with th e iden tification o f lesion areas by thelesion itself having an impact on overall brain structure volume. Over a long-termstudy, this could be used to t rack the d evelop men t of target ed lesion areas.

In th e current implemen tation o utlined in th is study, some key disadvantage s arehowever identified. It was found that the most accurate m atching was don e withlarge reg ularly shap ed ob jects. This limitat ion would result in the application o f th ismetho d to some b rain areas being less that op timum due to size.

Certainly, for g ene ral application to the issue o f lesion segm entation some

mod ification or de velopm ent to this metho do logy would ne ed to be undertaken.


21/31

21

3.5 Artificial Neural Networks (ANN)The main ob jective o f an autom ated lesionsegm entation m etho do log y is basically just t hat;auto mat ion, removal of as much interaction and

manual processing as possible and the redu ction o f the human- error element of any process.

This study (Goldberg -Zimring , Achiron, Miron,Faibel, & Azhari, 1998) has app lied the use of artificial neural networks to t ry and achieve t hisob jective. The app roach und ertaken he re has

achieved autom atic de tection of white m atte r MSlesions in axial prot on de nsity, T2-weighted,

gado linium enhan ced and fast FLAIR bra in MRimages.

The general process consists of three stages. Firstly, detection and contouring of allhyperintense signal reg ion s within th e imag e. Secon dly, eliminat ion of false p ositivesegm ent s by size, shape inde x and anatom ical location and thirdly, the use of anartificial neu ral network (ANN) for final rem oval and different iation from true MSlesions. (Goldberg -Zimring , Achiron, Miron , Faibe l, & Azhari, 1998).

This methodology outlines four basic assumptions with its processing.1. In PD, T2-weighted , gad olinium enhan ced, and FF-MR image s, MS lesions

appe ar much brighter than the rest of the b rain2. Non-MS regions in the brain, which also produce high signal intensity,

(especially in T2-weighted MR image s) such as bloo d vesse ls, andcereb rosp inal fluid within the ventricles, have either a relatively very small orvery large (in the case o f the ventricles) area

3. MS lesions have a relatively circular shape4. Most of the MS lesions occur in the periventricular white matter area, and are

rarely seen in cort ical regions o n MR image s. Furthermo re, they are typicallylocat ed asymme trically relative to the brain

(Goldberg-Zimring, Achiron, Miron, Faibel, & Azhari, 1998).

Based u po n the se four assumpt ions, it was det ermined th at a b rain region would bea p ossible cand ida te for an MS lesion if it h as a relatively high signal inte nsity, isrelatively circular in shape , its size is with in a prede fined rang e and its locat ioncomplies with assum pt ion numb er four. (Goldberg -Zimring, Achiron , Miron , Faibel, &Azhari, 1998)

The algorithm itself is applied in three stages (as indicated above).

Figure 3 the original ProtonDensity imag e prior to p roce ss

being conducted (Goldberg-Zimring, Achiron, Miron, Faibel, &Azhari, 1998)


22/31

22

3.5.1 Detection and Contouring of all HyperintenseSignal Regions within the ImageNormalisation of the imag e t akes place within th is stagewith t he a pp lication of an adap tive threshold algorithm.The o utp ut from this stag e is a set o f closed conto ursdescribed b y arrays of conto ur dat a points (see Figure 4) .

3.5.2 Partial Elimination of Artefacts (FalsePositives)The out pu t o f this stage is displayed in Figure 5. Area,perimete r and shap e index of each of the contouredregions from the previous stage is calculated using thefollowing formulas.

Area: A cross-sectional area bo und ed by a closedcontour can be estimated by greens Theorem in theplane. (Goldberg -Zimring , Achiron, Miron, Faibe l, &Azhari, 1998)

= 12 (Goldberg -Zimring , Achiron, Miron , Faibe l, & Azhari, 1998)

Perimeter: The p erimet er was estimat ed usingthe following

= ( 1 ) 2 + ( 1 ) 2 =1 (Goldberg -Zimring , Achiron, Miron, Faibe l, &Azhari, 1998)

Shape Index: The resemb lance o f eachsegment ed shape to a circular shape wasevaluating using the shape inde x app lied byGibson et al. (Goldberg-Zimring, Achiron, Miron,Faibel, & Azhari, 1998).

=4

2

(Goldberg -Zimring , Achiron, Miron , Faibe l, & Azhari, 1998)

Figure 4 the processed Prot onDensity image after the first stage

of the algorithm. Note theprese nce o f artefacts (Goldbe rg-Zimring, Achiron, Miron, Faibel, &Azhari, 1998)

Figure 5 the process image afterrem oval o f the artefacts (Goldbe rg -Zimring, Achiron, Miron, Faibel, &Azhari, 1998)


23/31

23

3.5.3 Final Removal of Artefacts by ANN The Artificial Neura l Network (ANN) is ap plied at thefinal stage to rem ove the remaining artefacts.

An ANN is a comp ute r algo rithm that atte mp ts todescribe the biolog ical behaviour of brain neurons(Goldberg-Zimring, Achiron, Miron, Faibel, & Azhari,1998). This methodo log y has selected the Back-Propaga tion ANN which uses a form o f supervisedlearning in a t raining pha se.

During this training phase a set o f inpu t p atte rns closeto the desired ou tpu t is ente red into ANN. The ANNthen adjusts its synap tic weight ing to atte mp t toclosely match the targeted outp uts.

For t his imp lem entat ion a set of 40 positively ident ified MS lesions and 40 po sitivelyidentified artefacts were taken from across 20 imag es. Once the training wascomp lete , the trained ANN was used for the final sorting of the selected imag es.

3.5.4 ResultsA fully automated a lgo rithm for the d ete ction and segm entation o f MS lesions is of course a very desirable to ol for this funct ion . The ANN produ ces a significant result

over othe r autom ated algorithm s cover so far; nam ely it does have the po ten tial forlearning based upo n p revious e xpe rience. The mo re information p rovided d uring thetraining phase will ultimately prod uce a be tte r tool.

With this imp lementa tion however, a n umb er o f limitations can b e ob served . Theassumptions identified abo ve produce constraints tha t may no t be suitab le for allpossible MRI scans. It makes the assumption that the MS lesions being examined arebrighter than the brain (Goldberg-Zimring, Achiron, Miron, Faibel, & Azhari, 1998).This wou ld certainly limit the use of th is to ol in a nu mb er o f circumstances. Asidentified earlier in this paper, the involvement of GML within MS which in morerecent years has been identified as playing a role within MS would n ot be seen bythis method .

While this implementation would seem to have some significant limitations, thisme tho d does dem onstrate the utility of ANN in terms of the lesions seg mentat ionprob lem. Further stu dy into this method ology may ide ntify possible futureapplications for MS and other relevant medical conditions.

Figure 6 the final Proton Densityimage after remo val of all artefactsand final tuning stage (Goldberg-Zimring, Achiron, Miron, Faibel, &Azhari, 1998)


24/31

24

4. New Techniques and areas of furtherstudy

4.1 IntroductionTraditiona l approaches to this problem have seen advancement from fully manualprocesses relying o n judgm ent by trained op erato rs to the introduction o f either fullyor pa rtially auto mat ed t echniques.

There h ave also been some ap proaches that have taken d ifferent directions with theresolution of this prob lem. Som e stud ies have be en undertaken which loo k at theprob lem o f segm entation with the empha sis on de termining what is known andeasily iden tifiable and using that to assist in the de termination o f the a reas or itemsof inte rest in the scan.

4.2 Brain AtrophyA stud y cond ucted to d etermine if White Mat te r Hype rinte nsities (WMH) wererelated with sub cortical bra in a trophy (Wen, Sachd ev, Chen, & Anstey, 2006) hasprovided some evidence to suggest that the brains WMH load can be correlated

with a trop hy in ot her regions o f intere st such as gray matt er volume red uction.

This study doesnt draw any direct conclusions on any causality to this observation;however it does raise an inte resting line of reasoning for future stud y or conjecture.

A more recent study (Bendfeldt, et al., 2009) has looked to establishing a strongerlink be tween WML and changes o f gray matte r volumes b y means o f voxel-ba sedmorphometry (VBM).

In this stud y, two hypotheses are raised;1. Regiona l gray matte r volume reductions o ccur p redo minantly in p atients with

increasing WML volume s2. Patients with both increasing T1 and T2 lesion burden would show volumetric

GM reductions th at are qualitatively similar but even mo re p rono unced.(Bendfeldt, et al., 2009)

The results o f this study draw a conclusion that sugg ests that g ray matte r volumereductions are d irectly related to increase white ma tte r lesion volumes.

Based on the results o f these two studies, a simple bu t p ote ntially effective a pp roachto t he p roblem o f lesion seg mentat ion m ay be to app roach the matte r with no t so


25/31

25

much ide nt ifying what is unknown, bu t iden tifying what is kno wn and workingbackwards from there. It should be noted that both stud ies do indicate t hat furtherlong term follow-up stud ies are req uired to furthe r support th is conclusion .

4.3 Physical Impairment as a MeasureThis study (Charil, et al., 2003) looks at identifying a link between lesion location andneurological disability in Multiple Sclerosis. The author acknowledges initially thatthere is generally only weak correlation between disability and the volume of whitema tte r lesions (Charil, et al., 2003); however the study was able to determine som ecorrelation be tween lesion location and cognitive dysfunction.

The study consisted of a large sample of 452 relapse-remitting MS patients. From

each of the patients a Proton Density, T1 and T2 MRI image were obtained.Disabilities were measure using the Functional System Scale (FSS) and ExpandedDisability Status Scale (EDSS).

The EDSS scale t akes a mea suremen t rang ing from 0 (normal) throu gh to 10 (deathdu e t o MS). The FSS scale loo ks at spe cific functional system s and includes p yramida l,cereb ellar, brainstem, sensory, bo wel and bladd er, visual, and mental. They aregraded from 0 (normal) throu gh to 5 or 6 (maximal impairment ).

4.3.1 Image CaptureUnlike other methodologies covered within this review, this particular techniquedoesnt present a unique and specially developed image processing technique. Thetechnique used within this stud y to analyse the image s from e ach pa tient in thestudy was INSECT (Intensity Normalised Stereot axic Environment for Classificat ion o f Tissue), a fully aut om atic system for the mass quan titative ana lysis of MRI da ta with afocus o n the de tection of Multiple Sclerosis lesion s (Zijde nbox, Forghan i, & Evans,1998).

4.3.2 Data AnalysisSpearmans rank correlation coefficient was used to calculate the correlationsbe tween the to tal lesion load and each disability score (Charil, et a l., 2003). Two maincorrelation m easures were taken; correlation b etween to tal lesion load and disability,and correlation be tween lesion location and t he rate of disease p rogression .

4.3.3 ResultsThe analysis of the results from this study dem onstrat ed that a relationship b etweenlesion site and type of disability does exist. It also offers an explanation for the poorrelationship be tween lesion load and disability shown in previous stud ies b eing a

result o f lesions within re stricted sites in the white ma ter (Charil, et al., 2003).


26/31

26

4.3.4 Impact on General Lesion IdentificationWhile the results of this study d id st atistically prove a link be tween lesion site an dtype o f disability, it also p resen ts some drawbacks from the pe rspective of utilisingth is as a m easure for ide nt ification so lely for the iden tification o f lesion load. The

me asure taken for disability (the EDSS and FSS scales) are b ot h undertaken m anu ally.While the scale in quest ion and the scope o f stud y is broad er than could becomp ared to the man ual segme ntat ion systems covered earlier in this stud y, it stilldo es involve po tent ial for hum an interpretat ion and e rror.

4.4 ConclusionThis section of the review has looked at t wo te chnique s that have applications toindirectly be u sed to add ress the prob lem o f lesion seg men tation.

Currently, while sho wing some merit, neither ap peared to be ent irely suitab le at the irrespective current stag es of developm ent to be used to add ress the problem as awhole. Both would however show some suitability for a subject of further study andresearch.


27/31

27

5. ConclusionsThis review has ident ified a rang e o f method ologies utilised to add ress the issue o f

lesion segmentation within MR images.

While de termining the m ost viable and ap prop riate m etho do log y is outside thescope o f this pape r, a few observations can reasonably be drawn from the materialreviewed.

The req uirement of type and numb er of MR image s neede d for each me thod ologyvaried. To ensure a methodology remains flexible to the majority of circumstances itwould be a clear advantage to ensure that the methodology doesnt require anything

over and above the type o r numb er of imag es that wou ld no rmally be t aken insuppo rt of patient treatment

App roaches identified that take mo re novel approaches may provide furthe r scop efor study in the future. Given some of the complex issues involved in segmentationof lesions across the g ray and white m atte r as well as the segm entation of othe rmat ter cont ained within the MRI and also t aking into account the fact tha t allme tho do log ies do p resent (however small) some aspect of error, an ap proach thatuses other measures to enhance trad itional me tho do log ies may provide a ssistance toreduce t he level of error t o further insignificant levels. Two key areas identified herewere the u se of cogn itive and p hysical de ficit and the m easurem ent of othe r brainmat ter to help de fine a reas of inte rest. While b ased upo n the mate rial reviewed,neither appears to b e sufficient to stand as viab le lesion segm entationmetho do log ies by themselves, using t hem in conjunction with o ther me tho do log iesmay be an app roach to follow.


28/31

28

BibliographyAnbeek, P., Vincken, K. L., van Osch, M. J., Bisschops, R. H., & van der Grond, J. (2004).Auto mat ic segm entation of d ifferent -sized white mat ter lesions by voxel prob abilityestimation. Medical Im age Analysis , 8 (3), 205-215.

Ben dfeldt , K., Kuste r, P., Traud, S., Egger, H., Winklho fer, S., Mueller -Len ke, N., et al.(2009). Association of reg ional gray matt er volume loss and p rog ression of whitematter lesions in multiple sclerosis - A longitudinal voxel-based morphometry study.

NeuroImage , 45 (1), 60-67.

Calabrese, M., Filippi, M., Rovaris, M., Mattisi, I., Bernardi, V., Atzori, M., et al. (2008).Morpholog y and evolution o f cortical lesions in multiple sclerosis. A long itud inal MRIstudy. NeuroImage , 42 (4), 1324-1328.

Charil, A., Zijdenbos, A. P., Taylor, J., Boelman, C., Worsley, K. J., Evans, A. C., et al.(2003). Stat istical mapp ing ana lysis o f lesion location and neurolog ical disability inmultiple sclerosis: application to 452 patient data sets. NeuroImage , 19 (3), 532-544.

Cocosco, C. A., Kollokian, V., Kwan, R. K., & Evans, A. C. (1997). BrainWeb: OnlineInte rface to a 3D MRI Simulate d Brain Database. NeuroImage , 5 , 425.Collins, D. L., Zijdenbos, A. P., Kollokian, V., Sled, J. G., Kabani, N. J., Holmes, C. J., et al.(1998). Design and const ruction o f a realistic digital brain phant om. IEEE Transact ionson Medical Im aging , 17 (3), 463-468.

Columbia University. (2007, May 23). Tutorial - Classification . Retrieved June 4, 2009,from Workbench: http://wiki.c2b2.columbia.edu/workbench/index.php/Tutorial_-_Classification

de Boer, R., Vrooman, H. A., van der Lijn, F., Vernooij, M. W., Ikram, M. A., van derLug t, A., et al. (2009). White m att er lesion extension to aut om atic brain tissuesegm entation o n MRI. NeuroImage , 45 (4), 1151-1161.

de Leeuw, F. E., de Groot, J. C., Achten, B., Oudkerk, M., Ramos, L. M., Heijboer, R., etal. (2001). Prevalence o f cereb ral white matt er lesions in elderly pe op le: a po pu lat ionbased m agn etic resoance imaging study. The Rott erdam Scan Study. Journal of

Neurology, Neurosurgery and Psychiatry , 70 (1), 9.

Goldb erg-Zimring , D., Achiron, A., Miron, S., Faibel, M., & Azhari, H. (1998).Automated detection and characterisation of multiple sclerosis lesions in brain MRimages. Magnetic Resonance Imaging , 16 (3), 311-318.


29/31

29

Iosifescu, D. V., Shenton, M. E., Warfield, S. K., Kikinis, R., Dengler, J., Jolesz, F. A., et al.(1997). An au tomated registration a lgo rithm for mea suring MRI subcortical bra instructures. Neuroimage , 6 (1), 13-25.

Kidd, D., Barkhof, F., McConnell, R., Algra, P. R., Allen, I. V., & Revesz, T. (1999).Cortical lesions in multiple sclerosis. Brain , 122 (1), 17-26.

Kikinis, R., Shenton, E. M., Iosifescu, D. V., McCarley, W. R., Saiviroonporn, P., Hokama,H. H., et al. (1996). A digital brain atlas for surgicle planning, model-drivesegment ation, and t eaching. IEEE Transactions on Visualisation and Com puter Graphics , 2 (3), 232-241.

Kutzelnigg , A., & Lassm ann, H. (2005). Cortical lesion s an d brain atro phy in MS. Journal of the Neurological Sciences , 233 (1-2), 55-59.

Kwan, R. K., Evans, A. C., & Pike, G. B. (1996). An extensible MRI simulator for post-processing evaluation. Visualisation in Biom edical Com puting (VBC'96). Lecture Notes

In Computer Science , 1131 , 135-140.

Kwan, R. K., Evans, A. C., & Pike, G. B. (1999). MRI simulation-based evaluation of imag e-p rocessing and classification met hod s. IEEE Transactions on Medical Im aging ,18 (11), 1085-1097.

Liney, G. P. (2005). Magnetic Resonance Im aging (MRI) . Ret rieved May 23, 2009, fromMRI Physics Lectures: http://www.hull.ac.uk/mri/lectures/gpl_page.htmlMcGill University. (2006, June 12). BrainWeb: Simulated Brain Database . RetrievedJune 9, 2009, from McConnell Brain Imaging Center:ht tp :/ /www.bic.mni.mcgill.ca/ bra inweb /

Nakamura, K., & Fisher, E. (2009). Segmentat ion of b rain magnet ic resoance image sfor measurem ent of gray matt er atrophy in m ultiple sclerosis patients. NeuroImage ,44 (3), 769-776.

Nave, R. (n.d.). Magnetic Resonance Imaging . Retrieved May 23, 2009, from MagneticResona nce Imag ing: h ttp :/ / hyperphysics.phy-astr.gsu.edu/ hbase/ nuclear/ mri.htm lNyul, L. G., & Udupa, J. K. (1999). On standardizing the MR image intensity scale.

Magnetic Resonance in Medicine , 42 (6), 1072-1081.

Sepulcre, J., Goni, J., Masdeu , J. C., Bejarano, B., de Men d izab al, N. V., Toledo, J. B., etal. (2009). Con tribut ion of white matter lesions to g ray matt er at rop hy in multiplesclerosis. Archives of Neurology , 66 (2), 173-179.


30/31

30

Stamatakis, E. A., & Tyler, L. K. (2005). Identifying lesions on structural brain images -Validat ion of the met hod and app lication to neu ropsycho log ical pat ient s. Brain and

Language , 94 (2), 167-177.

Statsoft Inc. (1984-2008). k-N earest Neighbors . Ret rieved May 30, 2009, fromElectronic Textbook Statsoft: http://www.statsoft.com/textbook/stknn.html

Stokking, R., Vincken, K. L., & Viergever, M. A. (2000). Automatic morphology-basedbrain segme ntat ion (MBRASE) from MRI-T1 d ata. NeuroImage , 12 (6), 726-738.

Uebersax, J. (2002, July 20). Kappa Coefficients: A Critical Appraisal . Ret rieved May 20,2009, from http://ourworld.compuserve.com/homepages/jsuebersax/kappa.htm

van d en Bosch , A. (2009). K-nearest neighbor classification. Ret rieved June 4, 2009,from Videolectures.net: http://videolectures.net/aaai07_bosch_knnc/

Van Lee mput , K., Mae s, F., Bello, F., Vandermeu len, D., Colchester, A., & Suete ns, P.(2000). Auto mated segm entat ion of MS lesions in MR. NeuroImage , 11 (5).

van Swieten, J. C., Hijdra, A., Koudstaal, P. J., & van Gijn, J. (1990). Grading whitema tte r lesion s on CT and MRI: a simp le scale. Journal of Neurology, Neurosurgery,and Psychiatry , 53 (12), 1080-1083.

Vawter, N. (n.d.). Parzen Windows . Ret rieved June 14, 2009, from Parzen Windows:http:/ /web.media.mit.edu / ~ nvawter/ projects/rhythmClassification /c05.htm l

Wahlund, L. O., Barkhof, F., Fazekas, F., Bronge, L., Augustin, M., Sjogren, M., et al.(2001). A new rating scale for age-related white matter changes applicable to MRIand CT. Stroke , 32 (6), 1318-1322.

Warfield, S., Dengler, J., Zaers, J., Guttmann, C. R., Wells III, W. M., Ettinger, G. J., et al.(1995). Auto matic ide ntification o f grey mat te r structures from MRI to imp rove th e

segm entation of white matt er lesions. Journal of Im age Guided Surgery , 1 (6), 326-338.

Wen, W., Sachdev, P. S., Chen, X., & Anstey, K. (2006). Gray matter reducation iscorrelated with white mat ter hyperintensity volume: A voxel-based mo rphomet ricstudy in a large ep ide miolog ical samp le. NeuroImage , 29 (4), 1031-1039.

Wu, Y., Warfield, S. K., Tan, I. L., Wells III, W. M., Meier, D. S., van Schijndel, R. A., et al.(2006). Automated segmentation of multiple sclerosis lesion subtypes withmu ltichannel MRI. NeuroImage , 32 (3), 1205-1215.


31/31

Zijdenbox, A., Forghani, R., & Evans, A. (1998). Automatic Quantification of MSLesions in 3D MRI Brain Data Sets: Validation of INSECT. In A. Zijdenbox, R. Forghani,& A. Evans, Medical Image Computing and Computer-Assisted Intervention - MICCAI '98 (p. 439).

ImagesFigure 1 Example of a normal brain MRI imageImage ge nerated from BrainWeb , ht tp :/ /www.bic.mni.mcg ill.ca/bra inweb/ . (McGillUniversity, 2006)

Figure 2 Example of a brain MRI image showing MS lesions.Image ge nerated from BrainWeb , http :/ /www.bic.mni.mcg ill.ca/bra inweb/ . (McGill

University, 2006)

Figure 3 the original Proton Density imag e p rior to process being cond uctedImage taken from (Goldberg-Zimring, Achiron, Miron, Faibel, & Azhari, 1998)Used with pe rmission, Assoc. Prof. Haim Azhari D. Sc., Technion Israe l Inst itute of Techno logy, Israe l.

Figure 4 the processed Proto n Density imag e after the first stage of the algo rithm .Note the presence of artefactsImage taken from (Goldberg-Zimring, Achiron, Miron, Faibel, & Azhari, 1998)Used with pe rmission, Assoc. Prof. Haim Azhari D. Sc., Technion Israe l Inst itute of Techno logy, Israe l.

Figure 5 the process image after removal of the arte factsImage taken from (Goldberg-Zimring, Achiron, Miron, Faibel, & Azhari, 1998)Used with pe rmission, Assoc. Prof. Haim Azhari D. Sc., Technion Israe l Inst itute of Techno logy, Israe l.

Figu re 6 the final Proton Density image after rem oval of all arte facts and final tun ing

stageImage taken from (Goldberg-Zimring, Achiron, Miron, Faibel, & Azhari, 1998)Used with pe rmission, Assoc. Prof. Haim Azhari D. Sc., Technion Israe l Inst itute of Techno logy, Israe l.

Documents

A review of techniques and applications of lesion identification in MRI scans