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Comparison of Different Feature Extraction

Techniques in Content-Based Image Retrieval for CT

Brain ImagesWan Siti Halimatul Munirah Wan Ahmad 1 and Mohammad Faizal Ahmad Fauzi 2

Faculty of Engineering, Multimedia University

Cyberjaya, [email protected]@mmu.edu.my

AbstractContent-based image retrieval (CBIR) system helps

users retrieve relevant images based on their contents. A reliable

content-based feature extraction technique is therefore required

to effectively extract most of the information from the images.

These important elements include texture, colour, intensity or

shape of the object inside an image. CBIR, when used in medical

applications, can help medical experts in their diagnosis such asretrieving similar kind of disease and patients progress

monitoring. In this paper, several feature extraction techniques

are explored to see their effectiveness in retrieving medical

images. The techniques are Gabor Transform, Discrete Wavelet

Frame, Hu Moment Invariants, Fourier Descriptor, Gray Level

Histogram and Gray Level Coherence Vector. Experiments are

conducted on 3,032 CT images of human brain and promising

results are reported.

I. INTRODUCTIONThe advancement in computer technologies produces huge

volume of multimedia data, specifically on image data. As a

result, studies on content-based image retrieval (CBIR) has

emerged and been an active research nowadays. CBIR systemis used to find images based on the visual content of the

images, and the retrieved results will have visually similarappearance to the query image. In order to describe the imagecontent, low level arithmetic features are extracted from the

image itself [3]. Numerous elements such as texture, motion,colour, intensity and shape have been proposed and used toquantitatively describe visual information [4]. The imagefeatures that were generated using specific algorithms are thenstored and maintained in a separate database.

A number of previous works have been done addressing

different techniques of the image elements for image retrieval.In 2002, Nikolou et al. [8] has proposed a fractal scanning

technique to be used in colour image retrieval with DiscreteCosine Transform (DCT) and Fourier descriptors as feature

extraction techniques. Qiang et al. [10] has developed aframework of CBIR based on global colour moments in HSVcolour space. Later in 2006, a user concept pattern learning

framework has been presented by Chen et al. [9] for CBIRusing HSV colour features and Daubechies wavelettransformation. The works on the CBIR for medical

applications are rarely found before; however it is gaining alot of attention recently due to large number of medicalimages in digital format generated by medical institutions

everyday. In 2003, Zheng et al. [7] has developed a content-

based pathology image retrieval system based on imagefeature types of color histogram, texture representation byGabor transform, Fourier coefficients, and waveletcoefficients. Recently, Rahman et al. [13] has proposed aCBIR framework which consists of machine learning methodsfor image prefiltering, statistical similarity matching and

relevance feedback scheme for medical images. The featuresare extracted using colour moment descriptor, gray-level co-occurrence matrix as texture characteristics and shape features

based on Canny edge detection.In this paper, detail comparison on the accuracy of different

feature extraction techniques are discussed and experimented

on medical images. The motivation is to get the best techniqueto be used in further medical image retrieval application. Thetechniques are from texture, colour and shape elements; where

texture techniques are Gabor Transform and Discrete WaveletFrame, colours are Gray Level Histogram and Gray Level

Coherence Vector, and shape methods are Hu MomentInvariants and Fourier Descriptors.

This paper is organized as follows. The next section brieflydescribes the feature extraction techniques used in thecomparison, and then followed by review of medical images

used in the experiment in Section III. Experimental setup isdiscussed in Section IV, followed by the results anddiscussions in Section V. Finally the conclusion is presentedin Section VI.

II. REVIEW OF FEATURE EXTRACTION TECHNIQUESA. Gabor Transform (texture)Gabor transform is a technique that extracts texture

information from an image. The one used in this research is atwo-dimensional Gabor function proposed by Manjunath andMa [1]. Expanding the mother Gabor wavelet forms a

complete but non-orthogonal basis set. The non-orthogonalityimplies that there will be redundant information betweendifferent resolutions in the output data. This redundancy has

been reduced by [1] with the following strategy: Let Ul and Uh

denote the lower and upper frequency of interest, Sbe the totalnumber of scales, andKbe the total number of orientations (ortranslations) to be computed. Then the design strategy is to

ensure that the half-peak magnitude support of the filter

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responses in the frequency spectrum touch each other asshown in Fig. 1, forS= 4 andK= 6. The Gabor transform is

then defined by:

= 111111 ),(),(),( dydxyyxxgyxIyxW mnmn (1)

where * indicates the complex conjugate and m, n are integers,m = 1,2,,Sand n = 1,2,,K. The Gabor transform therefore

produce SxKnumber of the output images, and energy within

each image is used as feature, resulting in SxKdimension offeatures where S=6 andK=4.

=

=

=

1

0

1

0

),(M

m

N

n

jiWy (2)

Fig. 1. Frequency spectrum of 2D Gabor transforms

B.Discrete Wavelet Transform (texture)Discrete Wavelet Frame (DWF) [2] is an overcomplete

wavelet decomposition in which the filtered images are notsub-sampled. This results in four wavelet coefficient imageswith the same size as the input image. The images are from

low-low (LL), low-high (LH), high-low (HL) and high-high(HH) channels. The decomposition is then continued on theLL channels just as normal wavelet transform, but since the

image is not sub-sampled, the filter has to be upsampled byinserting zeros in between its coefficients. The number ofchannels generated for the DWF is 3l+1, where l is the

number of decomposition levels. The energy within each

channel is used as feature. With l=3, 10-dimension of featurevector is produced.

C.Hu Moment Invariants (shape)For this shape representation, invariant moments used are

based on one that was derived by Hu [11]. Hu defined sevensuch moments that enables moment calculations which areinvariant under translation and changes in scale and rotation.It includes skew invariant which can distinguish mirrorimages of otherwise identical images. The seven moments are

used as features, hence producing 7-dimensional featurevector.

D.Fourier Descriptor (shape)Fourier Descriptors (FDs) is a powerful feature for

boundaries and objects representation. Consider an N-pointdigital boundary; starting from an arbitrary point (x0, y0) and

following a steady counterclockwise direction along theboundary, a set of coordinate pairs (x0, y0), (x1, y1),,(xN-1,yN-1) can be generated. These coordinates can be expressed ina complex form such as

1,...,2,1,0),()()( =+= Nnnjynxn (3)

The discrete Fourier transform (DFT) ofz(n) gives

=

=

1

0

10,2

exp)()(N

n

NkN

knjnzka

(4)

The complex coefficients a(k) are called the FourierDescriptors of the boundary. 64-point Discrete FourierTransform (DFT) is used which results on 64-dimension offeature vector.

E. Gray Level Histogram (intensity)Colour histograms are the most common way of describing

low-level colour properties of images. Since medical imagesare only available in grayscale, a simpler histogram calledgray level histogram (GLH) is used to describe intensity ofgray level colour map. A GLH is presented by a set of bins,

where each bin represents one or more level of gray intensity.It is obtained by counting the number of pixels that fall intoeach bin based on their intensity [6]. Fig. 2 shows an exampleof GLH for different images using 64 bins histogram.

Fig. 2. Example of gray level histogram distribution

with number of bins = 64

F. Gray Level Coherence Vector (intensity)Gray Level Coherence Vector (GLCV) is another technique

for extracting intensity features of an image. The idea of usingit is somewhat similar to Colour Coherence Vector (CCV)

proposed by G. Pass et al. [5]. This technique incorporatessome spatial information about an image, where each pixel isclassified in a given bin as either coherent or incoherent. A

pixel is coherent if it belongs to a large connected group ofsimilar pixels; otherwise it is incoherent. The first process isto discretize the gray colourspace, where only n distinct

numbers of gray colours (or bins) are used in the image.The next process is to categorize the pixels within a bin as

either coherent or incoherent, by comparing the bin size with apredefined threshold value . Value of and n used in [5] is300 and 64 accordingly. In this experiment, the bin size is alsoset to 64 and several values of were tested, and the optimal

value of is found to be 2600. All tested images in our imagedatabase contains 262,144 (512x512) pixels, so coherentregion is set to be approximately 1% of the image. From =2600, the average of coherent pixels for all images in our

database is about 70%; and from bin size of 64, number offeatures produced are 128-dimensional, where 64 for eachcoherent and incoherent vectors respectively.

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III.MEDICAL IMAGESMedical image collection used in this experiment is

provided by Putrajaya Hospital, Malaysia. It consists of 3,032

computed tomography (CT) images of human brain in theDICOM image format. The images are of 512x512resolutions, scanned from 95 patients with each patient having

scans ranging from 15 to 56. To quantitatively evaluate the performance of the texture- and intensity-based featureextraction techniques, the images are divided into 4 differentclasses according to visual similarity, called general image

classification. The ability of the system to retrieve imagesfrom the same class to the query images indicates the accuracyof the feature extraction techniques. To evaluate the performance of the shape-based techniques, differentclassification is used, called shape image classification. Theclassification is based on the head contour obtained by

segmenting the head from its background using fuzzy C-means clustering algorithm. Note that the shape-based featureextraction techniques employed will only search for similar

shape of the head itself, and not the shapes of different object

inside it. Visually the shape of the head can also be classifiedinto 4 different classes. Some examples of the images for both

the general and shape classifications are shown in Table I andII. From general image classification, 638 of 3,032 images inthe database belong to Class 1, 808 from Class 2, 1134 from

Class 3 and 452 images are from Class 4. For shape imageclassification, 293 belong to Class 1, 1012 from Class 2, 981from Class 3 and 746 from Class 4.

TABLE I

GENERAL IMAGE CLASSIFICATION

Class 1 Class 2 Class 3 Class 4

Totalimages

per class

638 808 1134 452

ExampleA

ExampleB

TABLE II

SHAPE IMAGE CLASSIFICATION

Totalimages

per class293 1012 981 746

Example

A

ExampleB

IV.EXPERIMENTAL SETUPThe retrieval system consists of 2 stages, namely the offline

feature extraction stage, and the online retrieval stage. During

the offline stage, the six feature extraction techniques areapplied to all 3,032 images in the database. Different lengthsof feature vectors are generated according to the techniques

used (Table III). These vectors are stored in separate featurevector databases according to the different techniques. During

the online stage, the feature vector of the query image iscomputed using one selected technique and is compared to allfeature vectors in the feature vector database of that technique.Distance metric is used to compute the similarity betweenfeature vectors of the database image. Small distance impliesthat the corresponding image is similar to the query image and

vice versa. Images are then retrieved based on increasingdistance. The flow of this process is shown in Fig. 4.

TABLE III

DIFFERENT LENGTH OF FEATURE VECTORS

Technique FV Length

Gabor Transform 24

Discrete Wavelet Frame 10

Hu Moment Invariants 7

Fourier Descriptor 64

GrayLevel Histogram 64

GrayLevel Coherence Vector 128

Fig. 3. Offline feature extraction stage

Fig. 4. Online stage of retrieval process

Measuring dissimilarity between images is of centralimportance for retrieving images by content. In this work, L1and L2 metrics, as well as the normalized version of those are

considered with respect to the suitable extraction technique.L1 metric is also known as Manhattan distance, calculated by

taking the absolute differences between the feature vectors;whereas L2 metric is known as Euclidean distance, calculated by examining the root of squared differences between thefeature vectors. Normalized Euclidean and Manhattan metrics

are computed by dividing the feature vectors difference with astandard deviation of that particular feature over the entiredatabase. The four distance metrics are given below:

Euclidean = =

n

k

jkik xx1

2)( (5)

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Manhattan = =

n

k

jkik xx1

)( (6)

Normalized Euclidean = =

n

k k

jkik xx

1

2)(

(7)

Normalized Manhattan = =

n

k k

jkik xx

1

)(

(8)

where k is the standard deviation of the kth feature in the FV

database.After the performance of each individual technique is

obtained, the best technique among the intensity, texture andshape features is chosen for experiment. The selectedtechniques are combined to see if the retrieval performancecan be further improved. This can be achieved by adding-up

the dissimilarity measures of the combined techniques withoutaffecting the relative distances between the query image andthe database images of each technique.

V. RESULTS AND DISCUSSIONSIn the initial setup of the experiment, eight images wereselected (two from each class) to test all the techniques with

all distance metrics to find the most suitable metric to be used

for each technique. The result is summarized in Table IV. Itwas found that different feature extraction techniques givedifferent performance for each distance metrics. Gabor

transform shows the best results using normalized Manhattanmetric, Discrete Wavelet Frame performs best usingnormalized Euclidean metric, Fourier descriptor presents high

accuracy using Manhattan metric, while Hu momentinvariants, gray level histogram and gray level coherencevector show high accuracy when using Euclidean distancemetric.

TABLE IVRETRIEVAL ACCURACY FORALL FEATURE EXTRACTION TECHNIQUES

TESTED USING ALL DISTANCE METRICS

Technique

Average retrieval accuracy of 8

query images for TOP 50 (%)

E M NE NM

Gabor 58.25 58 59.25 59.5

DWF 43.25 44.5 67.75 66.25

Hu moment 62 55.75 51 45.25Fourier Desc. 89.75 91.75 90.75 88

GLH 71.25 70.75 69.25 71.5

GLCV 74 73.25 25 25

E=Euclidean, M=Manhattan, NE=Normalized Euclidean

and NM=Normalized Manhattan

To evaluate the performance of each feature extractiontechnique, all 3,032 CT Brain images are used as query one-by-one to check if similar images from the same patient and

class are retrieved successfully. It is easier to do analysis bychecking the similarity per patient instead of all images fromthe database because the total number of images to beconsidered is then smaller. This operation involves hybrid-based image retrieval from our previous work in [12] wherePatientID is used as input in text-query and is combined with

CBIR. As an example, Patient14 (ID 156027) has 25 scans 6

of them belong to Class 1, 5 to Class 2, 10 to Class 3 and 4 toClass 4. First image from Class 1 is selected as the query

image, keyword 156027 is used with field Patient ID, andsystem will retrieve all 25 images of Patient14 according toincreasing distance. Perfect retrieval for this query image

would be the retrieval of the other five images from Class 1(excluding the query image itself) within the top 5 rankedimages, followed by images from other classes. The average

recognition rate is used to evaluate the retrieval accuracy, andits calculation is as shown in (9). For example, if there are Nimages in Class 1 for a particular patient, then the averagerecognition rate is computed as the number of images from

similar class within the topNretrieved images.

Averagerecognition =

rate

No. of images found from the sameclass within top N retrieved images (9)

No. of images per class, N

Retrieval process is performed to all CT brain images from95 patients in image database using the six feature extraction

techniques with suitable distance metric as discussed previously. Table V summarizes the retrieval accuracy for

each class of all texture and intensity techniques, and Table VIfor the two shape techniques. From Table V, recognition rateof all techniques for Class 1 and Class 3, and to some extentClass 4, is satisfactory but not for Class 2. The reason is thatclassification was done visually based on human vision and

some ambiguity are present where images from Class 2 canalso be classified as Class 1 or Class 3. Hence, it affects theoverall accuracy of Class 2 images. Overall, the average

recognition rate per patient is recorded to be above 70% fortexture and intensity extraction techniques. From Table VI,the accuracy of shape classification of both techniques varies

according to classes. Retrieval for Class 3 recorded highestaccuracy. However the accuracy is substantially lower

compared to the other four techniques from Table V becauseshape features are represented based on the contour of thesegmented object in the image and it depends a lot on thesegmentation accuracy itself. This problem can be fixed with better segmentation and shape extraction techniques todistinguish images of human brain in CT scans.

TABLEV

PERCENTAGE OF RETRIEVAL ACCURACY FORTEXTURE AND INTENSITY

TECHNIQUES

Technique% Average for 95 patient per class % Average

per patientClass 1 Class 2 Class 3 Class 4

Gabor 78.45 59.48 84.43 76.78 74.51

DWF 80.72 64.91 84.44 78.98 77.05GLH 86.98 55.31 88.26 60.88 72.02

GLCV 85.48 56.11 87.49 62.84 72.21

TABLEVI

PERCENTAGE OF RETRIEVAL ACCURACY FORSHAPE TECHNIQUES

Technique% Average for 95 patient per class % Average

per patientClass 1 Class 2 Class 3 Class 4

Hu moment 52.75 43.14 67.1 32.22 48.53

FourierDescriptor

47.55 67.05 75.35 74.28 67.39

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The other observation recorded is that the averagerecognition rate varies from patient to patient, depending on

the difficulty level in visually classifying the images into a particular class. Certain patients recorded a low averagerecognition rate because some of its images can be visuallyclassified into 2 classes, hence, effecting the recognitionmeasurement. The average recognition rate for all 95 patientsusing all techniques is presented in Fig. 5.

The experiments were conducted using Matlab 7.3 on anIntel Core Duo 2.0GHz processor with 1GB memory. Averagetime taken for each technique to complete retrieval process issummarized in Table VII. For texture image element, average

time recorded for both techniques are the same, but whenreferring to the retrieval accuracy, DWF gives better results.As for gray level intensity, the histogram technique performs

the retrieval process much faster than the coherence vectortechnique, even though both techniques result similar retrievalaccuracy. Between the two tested shape features, Hu momenttechnique can execute the retrieval up to three times fasterthan Fourier Descriptor (FD). However, after considering thepoor retrieval accuracy, FD is chosen for further analysis in

experimenting combination of feature extraction techniques.

TABLEVII

AVERAGE RETRIEVAL TIME FOREACH TECHNIQUE

Technique Average time taken

Gabor Transform 5s 6sDiscrete Wavelet Frame 5s 6sGray Level Histogram 11s 12s

Gray Level Coherence Vector 19s 20sHu moment 3s 4s

Fourier Descriptor 10s 11s

Since the combination of feature extraction techniques

involved summation operation of the techniques dissimilaritymeasures, the distance for each technique cannot be toodominant compared to others, so a small modification isneeded. For gray level histogram (GLH), the distance is very

large which ranges from 107

up to 109

because the featurevector consists of number of pixels in a specific bin. Tonormalize the GLH features, total number of pixels in each

bin is divided by the total number of pixels for all bins. ForFD technique, it was found that using Manhattan distance

metric produce a very small measurement, hence NormalizedEuclidean is used as a replacement because it generates

second highest accuracy in Table IV. There is no change inDWF technique. Results for the combination of techniques areshown in Table VIII. From the table, it can be seen that thecombination of DWF and FD techniques give the highestaverage retrieval rate. The pattern of accuracy per class isequivalent to the one in Table V, where Class 1 and Class 3

give better results, as well as Class 4, but a bit low for Class 2.Combining DWF with either GLH or FD performs retrievalfaster compared to the other combinations. Obviously moretime is needed to compute combination of all three techniques.

It is also interesting to note that combining all threetechniques does not further improve the retrieval accuracy, infact it performs worst than all the combination-of-two

techniques. This shows that we cannot simply bundle togethera lot of feature extraction methods in order to get higheraccuracy.

TABLEVIII

PERCENTAGE OF RETRIEVAL ACCURACY FORMULTI-TECHNIQUES

Techniques

% Average for 95 patient perclass

%Average

perpatient

AveragetimetakenClass 1 Class 2 Class 3 Class 4

DWF + GLH 88.04 57.93 88.19 66.37 77.81 10s 11s

DWF + FD 82.03 64.94 84.46 78.98 80.60 11s 12s

GLH + FD 86.97 55.31 88.26 60.89 75.34 15s 16s

DWF + GLH

+ FD69.17 61.37 87.96 72.93 74.01 18s 19s

To ease the work of testing and analyzing the images, a

graphical user interface (GUI) was developed using Matlabenvironment. It consists of two main panels which are QueryPanel (left side) and Result Panel (right side). The

development of this system is meant for flexible hybridretrieval system, so in the Query Panel, the type of retrievalcan be selected content-based (CBIR), text-based (TBIR) or both (Hybrid). Accuracy of the system can be analyzed

visually by looking at the Result Panel. Fig. 6 shows anexample of retrieval results obtained by texture extractiontechnique of DWF. As can be seen, visually similar scans are

retrieved accordingly.

Fig. 5. Average recall rate for 95 patients

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Fig. 6. GUI for image retrieval system

VI.CONCLUSIONSAn efficient content-based image retrieval system requires

excellent content-based technique to effectively use most ofthe information from the images. In this paper, a study hadbeen carried out on six feature extraction techniques from the

texture, intensity and shape image to acquire detailcomparisons on retrieval accuracy for each techniqueelements on medical images. The experiment was performed

on 3,032 CT human brain images from 95 patients, visually

divided into four classes and each image is tested in order toget the average recognition rate. Technique with the highestaccuracy among different approach is combined. Reportedresults show that the best texture extraction technique isDiscrete Wavelet Frame (DWF); for intensity is Gray Level

Histogram (GLH) and for shape feature is Fourier Descriptor(FD). For the combination of techniques, DWF and FDcombination gives the most excellent result. These techniquescan be used in medical applications to provide a reliable

image retrieval system.Our current work is using these promising techniques to

retrieve medical images based on region of interest, instead of

the whole image. A block-based algorithm has beendeveloped based on a simple gray level histogram with image partitioning algorithm. We are integrating the block based

method with the DWF, GLH and FD techniques.

ACKNOWLEDGEMENT

The authors would like to acknowledge Putrajaya Hospital,Malaysia, for contributing the medical images used in thisstudy. This work is funded by the Ministry of Science,Technology and Innovation Malaysia under the Science Fundgrant (ID: 01-02-01-SF0014).

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Detection Diatoms