This document summarizes a research paper on estimating skeletal maturity through the Tanner and Whitehouse bone age assessment method. The paper presents a system for segmenting wrist bones from left hand radiographs. The system first preprocesses images using Gaussian filtering and grayscale conversion. Canny edge detection is then used to extract regions of interest, which are classified using an Iterative Dichotomiser 3 classifier. The Tanner and Whitehouse method is applied to assess bone age. The system was tested on 50 images with 96% accuracy for males and 98% accuracy for females.

1. International journal of Digital Signal and Image Processing (IJDSIP)Vol. 1, No. 1(September 2013)
39
Estimation of Skeletal Maturity by Tanner and
Whitehouse Method
V.Karthikeyan1, V.J.Vijayalakshmi2, P.Jeyakumar3
1
Department of ECE, SVS College of Engineering, Coimbatore, India
Karthick77keyan@gmail.com
2
Department of EEE, Sri Krishna College of Egg & Tech., Coimbatore, India
Vijik810@gmail.com
3
Department of ECE Karpagam University, Coimbatore, India
Jeyak522@gmail.com
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
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3. International journal of Digital Signal and Image Processing (IJDSIP)Vol. 1, No. 1(September 2013)
•
Phase F – epiphysis is as broad as metaphysis
•
•
41
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.
-1
0
+1
+1
+2
+1
-2
0
+2
0
0
0
-1
0
+1
-1
-2
-1
Gx
Gy
Fig.2 3 x 3 convolution masks
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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,
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5. International journal of Digital Signal and Image Processing (IJDSIP)Vol. 1, No. 1(September 2013)
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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]
[2]
[3]
Vicente Gilsanz, and Osman Ratib, Hand Bone Age – A Digital Atlas of Skeletal Maturity,
Springer-Verlag, 2005.
Concetto Spampinato, “Skeletal Bone Age Assessment”, University of Catania, Viale Andrea
Doria, 6 95125, 1995.
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.
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7. International journal of Digital Signal and Image Processing (IJDSIP)Vol. 1, No. 1(September 2013)
[4]
[5]
[6]
[7]
[8]
[9]
45
J.M.Tanner, R.H.Whitehouse, Assessment of Skeletal Maturity and Prediction of Adult Height
(TANNER AND WHITEHOUSE 2 method), Academic Press, 1975.
M.C.Cooper, “The tractability of segmentation and scene analysis”, International Journal of
Computer Vision, vol.30, pp.27-42, 1998.
Rafael C.Gonzalez, Richard E. Woods, Digital Image Processing, Third Edition, Pearson, 2009.
T. Pappas and N. Jayant, “An adaptive clustering algorithm for image segmentation,” in Proc.
ICCV, 1988, pp. 310–315.
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.
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.
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