skeletal bone age assessment from elbow radiographs

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Abstract - The purpose of this work is to design and implement

a computerized system for skeletal Bone Age Assessment (BAA)

during puberty period, based on the Sauvegrain method. The

system ensures accurate and robust estimation of bone age for

the age group 10-13 years for girls and 12-15 years for boys. A

Gaussian filter is employed for noise removal, Canny edge de-

tector for edge detection, and thereafter Boundary fill algo-

rithm for segmentation. From the segmented apophysis ROI of

the olecranon, 8 discriminating features are extracted, which

are fed in to an ID3 decision tree classifier to output the age

class to which the radiograph is categorized, which is mappedonto the final bone age. The system was trained with 80 radio-

graphs, 40 radiographs of girls in age group 10-13 years and 40

radiographs of boys in age group 12-15 years. After training,

the system was tested on 100 radiographs (50 for girls and 50

for boys). The performance of the system was evaluated with

the help of radiologist expert diagnoses. The results show that

42 out of 50 were correctly classified for girls (84%) and 40 out

of 50 were correctly classified for boys (80%), with a minimal

error rate of 0.66% for girls and 0.68% for boys. The system is

very reliable, yielding excellent results with minimum human

intervention. The performance of the system is excellent during

puberty when compared to the conventional BAA systems.

Keywords: Bone Age Assessment (BAA), Feature extraction, radio-graph, ID3 Classification.

I. INTRODUCTION

Bone age assessment is an important clinical tool in the

area of pediatrics, especially in relation to endocrino-

logical problems and growth disorders[1]. Based on a

radiological examination of development of certain

parts of the skeleton, bone age is assessed and com-

pared with the chronological age. A discrepancy be-

tween these two values indicates abnormalities in skel-

etal development. The procedure is often used in the

management and diagnosis of endocrine disorders and

also serves as an indication of the therapeutic effect of

treatment. It indicates whether the growth of a patient

is accelerating or decreasing, based on which the pa-

tient can be treated with growth hormones. BAA is uni-

versally used due to its simplicity, minimal radiation

exposure, and the availability of multiple ossification

centers for evaluation of maturity. Many different

methods are used to assess skeletal maturity. Each

method makes use of different parts of the skeleton.

Acheson proposed the Oxford method [2] based on the

pelvis, Pyle and Hoerr [3], on the knee, Hoerr et al., onthe foot [4], Tanner et al.[5], and Greulich and Pyle[6],

based on the hand and wrist, and the Sauvegrain meth-

od [7], based on the elbow. Of all the above, the main

clinical methods for skeletal bone age estimation are

the Greulich & Pyle (GP) method and the Tanner &

Whitehouse (TW) method. GP is an atlas matching

method while TW is a score assigning method [8]. GP

method is faster and easier to use than the TW method.

In GP method, a left-hand wrist radiograph is com-

pared with a series of radiographs grouped in the atlas

according to age and sex. The atlas pattern which su-

perficially appears to resemble the clinical image isselected. TW method uses a detailed analysis of each

individual bone, assigning it to one of eight classes re-

flecting its developmental stage. This leads to the de-

scription of each bone in terms of scores. The sum of

all scores assesses the bone age. This method yields the

most reliable results. Cundy et al.[9] demonstrated that

the Greulich and Pyle atlas had a large inter-observer

error, which is problematic in the assessment of skele-

tal maturity during pubertal growth. Little et al.[10]

stated that the use of the Greulich and Pyle atlas could

not improve accuracy in the prediction of limb-length

inequality. Hence, there comes a necessity for alterna-tive methods for skeletal BAA. Next to the above wrist

x-ray methods, the elbow based methods are of much

significance, especially during the spurt of puberty.

The elbow is characterized by clear developmental se-

quences of its ossification centers beginning at nine

years of age in girls and eleven years in boys. Fusion of

the elbow growth centers is complete at thirteen years

in girls and fifteen years in boys. During the critical

pubertal period, morphological changes of the olecra-

non apophysis (shown in Fig. 1) of the elbow are very

characteristic. Alain et al. [11] conducted a study todetermine the accuracy of the Sauvegrain method in

2005, and proved that certain elbow growth centers

showed intermediate development morphology, which

failed to correspond to the scores assigned by

Sauvegrain method. They also suggested intermediate

scores for such cases.

The purpose of this work is to propose and develop a

system to estimate the bone age during puberty, based

on the ossification of the olecranon apophysis in the

elbow, since the interpretation of skeletal age per-

formed on the olecranon apophysis alone is a simpli-fied but very practical method in clinical practice [12].

Skeletal Bone Age Assessment from elbow Radiographs

1P.Thangam, 2K.Thanushkodi, 3T.V.Mahendiran, 4P.S.Sujith1Assistant Professor, Sri Ramakrishna Engineering College, Coimbatore, Tamilnadu, India.2Director, Akshaya College of Engineering and Technology, Coimbatore, Tamilnadu, India.

3Assistant Professor, Coimbatore Institute of Engineering and Technology Coimbatore, Tamilnadu, India.4PG Scholar, Sri Ramakrishna Engineering College, Coimbatore, Tamilnadu, India.

e-mail: [email protected]

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Applications IIII J JJ JSSSSAAAA AAAA

Volume 2, Issue ICTM 2011, February 2012, ISSN Online: 2277-2677 30

ICTM 2011|June 8-9,2011|Hyderabad|India



Fig. 1. Lateral view of elbow joint [13]

II. MATERIALS AND METHODS

The proposed work consists of the following mod-

ules, as depicted in Fig. 2.

• Image Pre-processing & Segmentation

• Feature Extraction

• ID3 Classification

2.1 Image Pre-processing& SegmentationThe input image is enhanced by image smoothing to

reduce the noise within the image or to produce a less

pixilated image. A Gaussian filter is employed here to

reduce noise and smoothen the image, followed by Can-

ny edge detector to detect bone edges. The Canny edge

detector [14],[15] is a significant and widely used contri-bution to edge detection techniques. This detector is op-

timal for step edges corrupted by white noise [16]. The

Canny edge detector first smoothes the image to elimi-

nate noise. It then finds the image gradient to highlight

regions with high spatial derivatives.

Segmentation using Boundary fill algorithm:

The boundary fill algorithm fills a region with the

given fill color until the given boundary color is found.

The 8-point technique is used here to color a region. The

algorithm is used recursively; it returns when the pixel

to be colored is the boundary color or is already the fill

color [17]. Fig. 3 provides a snapshot of the input, edge

detected and the segmented images for three classes.

2.2 Feature ExtractionFeature extraction is performed as a way to reduce the

dimensionality of the data [18,19]. A long list of candi-

date features was calculated in order to form a powerful

input vector. The features attempted to describe the mor-

phology of the outline shape of the bones (ROIs). Once

extracted and optimized, the vector would be used to

train and validate the classifier. For the olecranon, thedegree of maturity is based on the extent of ossification

of its apophysis, shown in Fig. 4. So, we consider the

following features to be extracted from the olecranon

apophysis, namely:

1. Presence – whether the apophysis of the bone is pre-

sent or absent

2. Circularity – whether the apophysis is circular in

shape or not

3. Multiplicity – whether the apophysis is single or

multiple

4. Proximity – whether both the nuclei of apophysisare closer or not

5. Merger – whether the apophysis nuclei have merged

or not.

6. Diameter – whether the diameter of the apophysis is

smaller than that of the ulna or not.

7. Fusion – whether the apophysis has fused with the

ulna or not.

Each of the above features is extracted from the respec-

tive ROI

Fig. 2. Steps in Bone Age Estimation

(a) (b) (c)

(a) Fig. 3. (a) Input (b) Edge detected and (c) Segmented images for

the Sauvegrain stages 2, 4 and 6.5 respectively.

SKELETALBONEAGEASSESSMENTFROMELBOWRADIOGRAPHSInternational Journal of Systems , Algorithms &






Fig. 4. Ossification stages of Olecranon apophysis [7].

2.3 ClassificationThis module is responsible for translating the features

into corresponding skeletal bone age. The selected fea-

tures take positions in the ID3 decision tree based on the

order of ossification of the bones, which influence their

contribution towards the bone age estimation procedure.

2.3.1 ID3 classifier:

Studies reveal that there may be discrepancies in the re-

sults of classifications by different radiologists or at

sometimes even between the classifications by the same

radiologist in different moments [22]. This is because

some stages are mistaken with the nearby stages easily.

So it becomes difficult to strictly define the features.

Instead, they have a certain degree of overlapping be-

tween them, since the growth pattern is gradual. Also the

features can fall into only any one of the categories (i.e.)

mutually exclusive. So the ID3 classifier is more suita-

ble for our system. ID3 is the third series of “Iterative

Dichotomiser” procedures, hence named so. They are

intended for use with nominal inputs only and they have

their number of levels equal to the number of input vari-

ables. The algorithm continues until all nodes are pure or

there are no more variables on which to split [23].Choosing the root is an important issue to be considered

for efficient classification [24]. The features with greater

discrimination power are to be placed above those with

low values in the decision tree. The inputs to the ID3

tree (in Fig. 5) will be the 8 features modeled as Boolean

values, and the output will be the skeletal class or cate-

gory. The selected class is then mapped onto the skeletal

bone age, based on the criteria shown in Table 1, which

is different for girls and boys.

2.3.2 Training Sets:

The ID3 decision tree was trained with 10 male and 10female radiographs for each age group, thus with a total

of 80 radiographs, 40 male and 40 female cases for the

age group 10-13 years for girls and 12-15 years for boys.

After the decision tree was built and fine-tuned, 50 radi-

ographs from girls and 50 from boys were used to test it.

The results showed that 42 out of 50 were correctly clas-

sified for the girls (84%) and 40 out of 50 were correctly

classified for the boys (80%). The classification error

was to assign a later class or a previous class.

III. RESULTS AND DISCUSSION

The performance of the computerized system in

estimating the bone age was evaluated using a dataset

consisting of 100 digital radiographs (50 for boys and

50 for girls). The quality of the image is a vital factor of

influence in the estimation process. So much considera-

tion was imparted on obtaining quality radiographs.

Also particular attention was dedicated to design and

implement robust techniques for preprocessing and reli-

able feature extraction. During the preprocessing, the

noise caused due to radiation and other external factors

were eliminated. From the segmented ROI, 8 morpho-

logical features were extracted and modeled into formalfeatures (Boolean values) suitable to be fed into the

classifier. Finally, the ID3 classifier was used to esti-

mate the final bone age. The accuracy of the classifier

was measured in terms of two metrics, namely the

Classification Rate CR and Average Relative Error

ARE . Classification rate, CR is defined as the ratio of

the number of correctly classified images N CC to the

total number of test images input to the classifier, N IP.

Incorrectly classified images include the images withunder estimated or over estimated bone age when com-

pared with the bone age estimated by a radiologist ex-

pert. The accuracy of classification is given in Table 2.

Average Relative Error, ARE is the average of the rela-

tive errors between the estimated bone age and the pre-

dicted bone age by the expert. It is given by,

(2)

The ARE values of the classifier are provided in Table

3.TABLE I

CRITERIA SELECTION

TABLE IICLASSIFICATION RATE

TABLE III

AVERAGE RELATIVE ERROR

IP

CC

N

N CR =

∑=

=n

i ien

ARE 1

1

SKELETALBONEAGEASSESSMENTFROMELBOWRADIOGRAPHSInternational Journal of Systems , Algorithms &






SKELETALBONEAGEASSESSMENTFROMELBOWRADIOGRAPHS

International Journal of Systems , Algorithms &IIII

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Fig. 8. ID3 Decision Tree

IV. CONCLUSION

In this paper, we have proposed an efficient bone age

assessment approach to estimate the skeletal bone age

from elbow radiograph. The method is advantageous in

that it requires only small amount of data to train the

classifier and from the results it is evident that the per-

formance of the system is excellent during pubertywhen compared to the conventional BAA systems. The

system was tested on a set of 100 radiographs (50 from

girls and 50 from boys), achieving a success rate for

bone age estimation of 84% for girls and 80% for boys,

with a minimal error rate of 0.66% for girls and 0.68%

for boys. Future work will be focused on extending the

system to work on the age group below 10 years,

broadening the system to include the further elbow

bones such as Trochlea, Epicondyle etc. and integrat-

ing the system with the clinical PACS.

ACKNOWLEDGEMENTSThe authors would like to thank Dr.R.Saravanan, MD

(Pediatrics), KKCT Hospital, Chennai for providing

the database of hand radiographs and Dr.R.Shankar

Anandh, MD(Radio Diagnosis) and Dr.S.Sanjitha

DMRD(Radio Diagnosis) of Barnard institute of Radi-

ology, Chennai for their valuable help in diagnosing

the radiographs and validating the system.

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