V.KARTHIKEYAN PUBLISHED ARTICLE AA

International journal of Digital Signal and Image Processing (IJDSIP)Vol. 1, No. 1(September 2013) 39

www.arpublication.org

Estimation of Skeletal Maturity by Tanner and

Whitehouse Method

V.Karthikeyan1, V.J.Vijayalakshmi

2, P.Jeyakumar

3

1Department of ECE, SVS College of Engineering, Coimbatore, India

[email protected] 2Department of EEE, Sri Krishna College of Egg & Tech., Coimbatore, India

[email protected] 3 Department of ECE Karpagam University, Coimbatore, India

[email protected]

Abstract

The proposed paper present the development procedure for segmenting wrist bones

commencing left hand wrist radiographs, which might exist more worn in estimating the

skinny adulthood or bone age. Bone Age evaluation is a process used in the organization

and analysis of endocrine disorders. It as well serves as a suggestion of the therapeutic

consequence of behavior. It is of a great deal consequence in pediatric medicine in the

finding of hormonal development or yet genetic disorders. The input radiographs are first

preprocessed to remove noise using a Gaussian filter and then grayscale converted. Edge

detection is done using canny edge detector. For Carpal Region of Interest analysis, bone

removal is approved out by integrating anatomical information of the hand and

trigonometric concepts; while a Tanner and Whitehouse method -phase task is achieved

by combining the inclined vector flow Snakes and the imitative distinction of Gaussian

filter. For Metaphyses Region of Interest study, image-processing methods and

geometrical characteristic investigation, based on the dissimilarity of Gaussian, are

proposed. The Region of Interest can be further used in feature extraction and

classification to estimate the bone age. The Iterative Dichotomiser 3 classifier is used to

classify the bones. The structure is validated by means of a statistics set of 50 images, 25

boys and 25 girls, and the results are discussed.

Keywords: Bone Age Assessment (BAA), Tanner and Whitehouse method (TW),

Region of Interest (ROI), Iterative Dichotomiser 3 (ID3) classifier

1. INTRODUCTION

Bone age assessment using a hand radiograph is a significant medical instrument in the

region of pediatrics, particularly in relation to endocrinological problems and expansion

disorders. A particular analysis of skinny age informs the clinician of the relative adulthood of a

patient at a exacting time in his or her time and integrated with additional clinical finding, divides

the usual from the relatively advanced or retarded [1]. The bone age of kids is in fact influenced

by sexual category, contest, diet category, livelihood environments and communal resources, etc.

Based on a radiological assessment of skinny growth of the left-hand wrist, bone age is assessed

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and compared by means of the chronological age. A difference is Tanner and Whitehouse. These

Tanner and Whitehouse o values point out abnormalities in skeletal growth. The process is

frequently used in the organization and diagnosis of endocrine disorders and too serves as a sign

of the therapeutic result of action. It indicates whether the expansion of a patient is accelerating or

falling, based on which the enduring can be treated with increase hormones. Bone Age evaluation

is commonly used due to its effortlessness, least emission contact, and the ease of use of

numerous Classification centers for assessment of adulthood.

2. BACKGROUND OF BONE AGE ASSESMENT

The major clinical methods for skinny bone age opinion are the Greulich & Pyle (GP)

technique and the Tanner & Whitehouse (TW) method. GP is an atlas matching method at the

same time as Tanner and Whitehouse is a score assigning method [2]. Greulich & Pyle method is

faster and easier to use than the Tanner and Whitehouse method. Bull et.al. Performed a large

scale comparison of the GP and Tanner and Whitehouse method and concluded that Tanner and

Whitehouse method is more reproducible of the Tanner and Whitehouse o and potentially more

accurate [3].

Fig. 1 Bones of hand and wrist for Bone Age Assessment

In GP system, a left-hand wrist radiograph is compared by means of a sequence of

radiographs grouped in the atlas according to age and gender. Tanner and Whitehouse system

uses a thorough study of every persons bone (shown in Fig. 1), conveying it to single of eight

classes reflecting its developmental stage. This leads to the account of every bone in terms of

scores. The amount of all scores assess the bone age. Fig. 2 shows the advance of the phalanx

bone into stages (A, B, C, D, I) as:

• Phase A – absent

• Phase B – single place of calcium

• Phase C – center is dissimilar in look

• Phase D – Most width is partially or more the

Width of metaphysis

• Phase E – Edge of the epiphysis is hollow



• Phase F – epiphysis is as broad as metaphysis

• Phase G – epiphysis caps the metaphysis

• Phase H – synthesis of epiphysis and metaphysis has

begun

• Phase I– epiphyseal synthesis concluded.

By adding the scores of every Region of Interests, and largely adulthood gain is obtained.

This score is connected with the bone age in a different way for males and females [4].

3. SYSTEM DESIGN

3.1 Image Preprocessing

Image preprocessing is performed in Tanner and Whitehouse o steps: Image smoothing

and Grayscale conversion. Image smoothing is done to reduce the noise within the image or to

create a fewer pixilated image. Most smoothing methods are based on low pass filters. In our

system, we encompass to diminish noise through by means of a Gaussian filter. Gaussian filter

reduces the amount of upper frequencies comparative to the lower frequencies, but at the rate of

additional calculation time. But the speeding up of smoothing is achieved by splitting 2D

Gaussian G(x,y) into Tanner and Whitehouse o 1D Gaussians G(x)G(y) and carrying out filtering

in 1D, primary row by row and subsequently column by column. Grayscale alteration is

completed as follows. Colors in the image are converted to a shade of gray by manipulative the

effectual brightness or luminance of the color and by means of this to make a shade of gray that

matches the preferred brightness.

3.2 Edge Detection

Edge takes place wherever near is a discontinuity in the strength task or an extremely

vertical concentration gradient in the image. Using this statement, if one get the copied of the

concentration value transversely the image and locate points where the derivative is highest, then

the edge might be located [5]. We contain the use of canny edge detector to sense the edges. The

Canny operator performs a 2D spatial gradient dimension on an image. Typically it is used to

locate the estimated complete gradient magnitude at every point in an input grayscale image. The

Canny edge detector uses a couple of 3 x 3 convolution masks, one estimating the gradient in the

x-direction (columns) and the other estimating the gradient in the y-direction (rows). A

complication mask is usually a great deal lesser than the real image. As an effect, the mask is

slide above the image, manipulating a square of pixels at a time.

Gx Gy

Fig.2 3 x 3 convolution masks

-1 0 +1

-2 0 +2

-1 0 +1

+1 +2 +1

0 0 0

-1 -2 -1

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3.3 Epiphyses Metaphyses Region of Interest Extraction

To extract the features needed for stage assignment to each Epiphyses Metaphyses Region Of

Interest, we improve the quality of the threshold image by filling the holes and by better defining

the contours of the previous thresholded image with a segmentation algorithm, based on the

approach proposed by Pappas and Jayant [7], which uses Gibbs arbitrary fields for a priori

likelihood modeling mutual with a Gaussian model for the conditional probabilities. Since human

bones have shapes that are often not convex, the aforementioned method allows us a more

accurate analysis of the bones extracted, thus avoiding the use of approximation due to the

convex hulls as in [8]. To describe each convex hull for each EMROI detected we extract the

features shown in fig. 6 that are compared with the same features extracted from the TW2 stage

classification model. The final result is the classification stage of all the extracted EMROIs and a

final stage computed as the mean of all the stages.

Fig. 3 Feature vectors for each Epiphyses Metaphyses Region of Interest.

To describe each detected Epiphyses Metaphyses Region of Interest, we extract and measure

some geometrical features (Fig. 2), inspired by [9], that are compared with the same features

extracted from the Tanner and Whitehouse 2-stage classification model. According to this

comparison, a Tanner and Whitehouse 2 stage is assigned to the considered bone. The final result

is the Tanner and Whitehouse 2-score assignment for the valuation of the skeletal bone age. More

in detail, we store for each stage and for each bone a vector of features (Fig. 2)

Fstagebone

[d meta, dist _ m _1, dnv1, dnv5, dhepi, area1... area6] (1)

If numeral of predicting attributes is unfilled, subsequently go back the solitary node tree

Root, with label equal to the majority frequent value of the aim characteristic in the examples. If

not start where dmeta is the width of the metaphysis, dnv1, dnv5 are the heights of different lines

that divide the epiphysis width in six equal parts, and area1, and area6 are the areas of the six

identified parts. Finally, dhepi is the distance between Tanner and Whitehouse & metaphysis and

the diaphysis. The stage assignment, after the Epiphyses Metaphyses Region of Interest analysis,



is done by simply calculating the minimum Euclidean distance between Tanner and Whitehouse

and the extracted features of the bone under analysis and all the Fbone stage stored as reference.

3.4 Carpal Region of Interest Extraction

The first step in Carpal Region of Interest analysis is the extraction of the carpal bones from

the entire hand, performed by a suitable application of the wedge functions. The top-right point of

the Carpal Region of Interest is found by detecting the soft tissue junction is Tanner and

Whitehouse Then second finger and the thumb. Starting from this point, it is easy to find the other

point. In order to better identify the single bones, a Derivative Difference of Gaussian filter has

been applied. The principle of this anisotropic filter is to smooth out noise locally by diffusion

while at the same time preventing diffusion across object details. This filter allows us to better

differentiate carpal bones from the background in performing effective dynamic thresholding.

Afterward, the single carpal bones are extracted by using a method that is able to identify the

image edges and to fill the closed ones. More in detail, the image contours are detected through a

canny edge detector followed by a fifth-order filter. Finally, a filling algorithm is applied to the

extracted Carpal Region of Interest Region of Interest.

(a) (b)

Fig. 4 - (a) Original Carpal ROI (b) Enhanced ROI by applying DrDoG.

(a) (b) (c)

Fig. 5 – (a) Output of DrDoG filter, (b) Bone contour detection after canny edge detection, (c)

Correctly regions extracted

The first step in the CROI analysis is the extraction of these bones from the entire hand that has

been performed by a suitable application of the wedge functions. Once the carpal regions have

been extracted, it is necessary to enhance them in order to facilitate the bone extraction. In order

to better differentiate carpal bones from the background and obtain a performing and dynamic

thresholding, we have used an anisotropic diffusion filter based on the derivative difference

between two Gaussian functions (DrDoG) that is different than the one used in the EMROI

processing. The principle is to smooth out noise locally by diffusion while at the same time

preventing diffusion across object boundaries. This filter allows us to suppress the noise and at

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the same time to preserve image edges and details as shown in fig. 4 a & 4 b. Afterwards, to

identify the contours we use a threshold through a canny edge detector followed by a fifth order

filter, see fig.5a,5b &5c .

3.5 Overview of Iterative Dichotomiser 3 classifier

ID3 builds a result tree from a permanent set of examples. The resultant tree is used to

categorize prospect samples. The instance has more than a few attributes and belongs to a class

(like yes or no). The leaf nodes of the resultant tree hold the class forename while a non-leaf node

is a conclusion node. The decision node is a characteristic test with every division (to another

decision tree) being a probable value of the trait. Iterative Dichotomiser 3(ID 3) uses in sequence

gain to assist it make a decision which feature goes into a result node.

4. RESULTS AND DISCUSSION

The system for classifying wrist bones from left hand wrist radiographs was tested with 50

left hand wrist images (25 males and 25 females). The quality of the segmentation was influenced

by the image quality. For radiographs over exposed to radiation, further preprocessing was

required, to achieve good results. The use of image pre-processing techniques such as image

smoothing and gray scale conversion improved the quality of the digitized radiograph. The noise

caused due to radiation and other external factors were eliminated. Canny edge detector identified

the boundary of the bones or Region of Interests. Then, Tanner and Whitehouse 2 method was

applied to assess the bone age from the radiograph. The classification was regarded as accurate if

the sum of over selected and under selected pixels were less than 25. The classification process

was accurate by 0.96 for males and 0.98 for females. From the preprocessed bones, the selected

Region of Interest can be used for feature extraction and thereafter in bone age estimation.

5. CONCLUSION

Iterative Dichotomiser 3 classifier was used to classify bones from left hand wrist radiograph

images, which can be further used for skeletal bone age assessment. The input image was first

pre-processed to remove noise and was grayscale converted to improve image quality. Canny

edge detector was used for edge detection and the threshold and Derivative Difference of

Gaussian was used for feature extraction. The system was tested with 50 left hand wrist images

(25 males and 25 females). The accuracy of the classification was influenced by the resultant

image. For radiographs over exposed to radiation, further preprocessing was required. Future

work would regard combining the Iterative Dichotomiser 3 classifier and the PSO algorithm to

produce a better optimization for the bones.

REFERENCES

[1] Vicente Gilsanz, and Osman Ratib, Hand Bone Age – A Digital Atlas of Skeletal Maturity,

Springer-Verlag, 2005.

[2] Concetto Spampinato, “Skeletal Bone Age Assessment”, University of Catania, Viale Andrea

Doria, 6 95125, 1995.

[3] R.K.Bull, P.D.Edwards, P.M.Kemp, S.Fry, I.A.Hughes, “Bone Age Assessment: a large scale

comparison of the Greulich and Pyle, and Tanner and Whitehouse (TANNER AND

WHITEHOUSE 2) methods”, Arch. Dis. Child, vol.81, pp. 172-173, 1999.



[4] J.M.Tanner, R.H.Whitehouse, Assessment of Skeletal Maturity and Prediction of Adult Height

(TANNER AND WHITEHOUSE 2 method), Academic Press, 1975.

[5] M.C.Cooper, “The tractability of segmentation and scene analysis”, International Journal of

Computer Vision, vol.30, pp.27-42, 1998.

[6] Rafael C.Gonzalez, Richard E. Woods, Digital Image Processing, Third Edition, Pearson, 2009.

[7] T. Pappas and N. Jayant, “An adaptive clustering algorithm for image segmentation,” in Proc.

ICCV, 1988, pp. 310–315.

[8] D. Giordano, C. Spampinato, G. Scarciofalo, and R. Leonardi, “Automatic skeletal bone age

assessment by integrating EPIPHYSES METAPHYSES REGION OF INTEREST and CARPAL

REGION OF INTEREST processing,” in Proc. Int. Workshop MeMeA, May 2009, pp. 141–145.

[9] E. Pietka, S. Kurkowska, G. Arkadiusz, and F. Cao, “Integration of computer assisted bone age

assessment with clinical PACS,” Comput. Med. Imaging Graph., vol. 27, no. 2/3, pp. 217–228,

Mar./Jun. 2003.

Authors

Prof.V.Karthikeyan has received his Bachelor’s Degree in Electronics and

Communication Engineering from PGP college of Engineering and Technology in

2003, Namakkal, India, He received Masters Degree in Applied Electronics from KSR

college of Technology, Erode in 2006 He is currently working as Assistant Professor in

SVS College of Engineering and Technology, Coimbatore. She has about 8 years of

Teaching Experience

Prof.V.J.Vijayalakshmi has completed her Bachelor’s Degree in Electrical &

Electronics Engineering from Sri Ramakrishna Engineering College, Coimbatore,

India. She finished her Masters Degree in Power Systems Engineering from Anna

University of Technology, Coimbatore, She is currently working as Assistant Professor

in Sri Krishna College Of Engineering and Technology, Coimbatore She has about 5

years of teaching Experience.

Mr P.Jeyakumar Currently pursuing his Bachelor’s Degree in Electronics

Engineering in Karpagam University, Coimbatore, Tamil Nadu, India.