JASC: Journal of Applied Science and Computations ISSN NO: 1076-5131

Skeleton Age Analysis from and Metaphysic of Phalanges

Navjot Kaur#1, Kulvinder Singh Mann*2 #CSE Department, Guru Nanak Dev Engineering College Ludhiana, India *IT Department, Guru Nanak Dev Engineering College Ludhiana, India [email protected], [email protected]

Abstract— Computer systems have been broadly used in the field of Medical Science. From many years, assortment of studies has been done to determine different methods to identify the age of child using their bone characters. Bone age assessment is a technique that is very widely used by child development specialists and in the study of forensic science. The decision-making process in these assessment techniques, however, depends on the expert’s opinion; hence, the assessment results may differ from expert to expert. In the paper, we present the automatic bone age assessment using phalanges, metacarpal in addition to and . The motive of automatic bone age assessment with computers is to provide the decision process more clinical and accordingly allow more steady results to be obtained. So, for this feature extraction also play a pivotal role in assessment method in order to classify the test sample. In the proposed work, we find performance metric like accuracy, error and comparison with other technologies under MATLAB software.

Keywords— Bone Age, Decision Making, Feature extraction, PCA, ICA, Fuzzy Logic, Atlas, Region of Interest (ROI) .

I. INTRODUCTION

Bone age assessment can be used to determine the level of maturity of child’s bone. it helps to measure human growth as childhood, , young adult, middle adult, and senior citizen. These changes can only be seen through x-ray. There are a total of 206 different bones in human body.

In paediatric radiology, bone age assessment is considered as clinical procedure to estimate the growth rate of the children’s body. Left radiograph (x-ray) image is taken as input and then the bone age of children will be determined. There are many handful methods available to estimate the skeletal maturity of a children’s bone.

Bone age assessment study helps doctors to determine the growth and maturity of a child's skeletal system. The bone age estimation is generally done by a single x-ray image of the left hand, wrist, and . It is very simple, basic, safe and painless method that uses very few amount of radiation. The bone age is calculated in years. The fingers and wrist of the child’s radiographic images contain growth plates placed in growth zoning at both ends.

The special cells in growth plates will estimate and calculate the growth of the bones as well as fingers. Because of fewer minerals in x-ray images the growth plates can be located easily in x-rays. As a person’s body grows the growth plate will change its appearance in the x-ray images and become thinner, steadily the growth plates gets closed. A doctor can allocate bone age based on the appearance of the growth plates and bones. A child's skeletal maturity is allotted by using digital atlas which helps in determining standard x-ray images with the atlas which are more closely related to the appearance of the child's bones on the x-ray.

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A. Bone Development Skeletal maturity is an estimation of development of bone incorporating the size, shape and degree of mineralization of bone to explain its proximity to full maturity. The determination of skeletal maturity involves a rigorous examination of various factors and fundamental knowledge of the diverse processes by which bone develops. Longitudinal growth in the long bones of the extremities occurs through the process known as endochondral .

In contrast, the breadth of the bones increases by the growth of skeletal tissue emerged directly from fibrous membrane. The latter is the process through which ossification of the calvarium, the flat bones of the pelvis, the scapulae, and the body of the mandible occurs. Initially, calcification emergences near the middle of the shaft of long bones in a region called the primary ossification center. Although many flat bones, including the carpal bones, ossify completely from the primary core, all of the long bones develop through secondary cores that appear at the edge of the of the bone.

Fig 1 : Schematic Representation of Endochondral Bone Formation

The bone ossified from the primary center part is called as diaphysis, while the bone ossified from the secondary center called as epiphysis. As the secondary center is steadily ossified, the cartilage is substituted by bone until only a thin layer of cartilage the , split the diaphyseal bone from the epiphysis. The part of the diaphysis that is adjoining the epiphysis is called as the and depicts the growing end of the bone. As long as the epiphyseal cartilage plate remains, both the diaphysis and epiphysis expand to grow, but, eventually, the osteoblasts cease to replicate and the epiphyseal plate is ossified. At that time, the osseous structures of the diaphysis and epiphysis are fused and growth ceases [1].

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During the fetal phase of life, the fundamental interest in skeletal growth is associated with the diagnosis of prematurity. The ending of the embryonic period and the beginning of the foetus is marked through the event of calcification, which begins at 8 or 9 weeks. By the 13th fetal week, most primary cores of the tubular bones are well-developed and merged into diaphyses, and, at the time of birth, all diaphysis are entirely ossified, while many epiphyses are still cartilaginous. Ossification of the distal femoral epiphysis commences during the last two months of gestation, and the secondary core is found in most full term babies. Similarly, the ossification core for the proximal epiphysis of the generally starts appearing at the 40th week of gestation. On the other hand, the cores for the proximal epiphyses of the and may not be found in full term infants, but mainly appear in the first few months of life [2].

After birth, the epiphyses steadily ossify in a largely prescribed and predictable manner, and, at the time of skeletal maturity, fuses with the main body of the bone. Comparing the degree of maturation of the epiphyses to the normal age-related standards forms the basis for the estimation of skeletal maturity, the measure of which is commonly called “bone age” or “skeletal age”.

II. RELATED WORK

The hand bone development levels and methods were proposed in and as an Atlas by W. W..Greulich and S. I. Pyle [1] in 1959 from Stanford University Press, Stanford, California USA. Different Radiographs i.e. x-rays of left shoulder, elbow, hand, knee and hip were captured. These images were taken at the span of three months up to five years and then subsequently taken on yearly basis. The data collection step was executed from11931 to 1942 years covering around 1000 children radiographs as a source. Finally in the yearr1950, a research Atlas has been issued. For boys and girls different Atlas has been published since they both mature at different rates. These atlases are taken as the fundamental model for determining age related changes in the human bone architectural structure. Another atlas has been given by Tanner and Whitehouse in the form of TW: TW1, TW2, TW3 [2] in the yearr1962. Here the study was concentrated on the age determination but relies on the bone standard maturity. This method used bone site as ROIs for bone maturity . Each ROI is moreover divided into 3 parts i.e. epiphysis, diaphysis and metaphysic. Out of these three, the epiphysis ossifies from the age zero to teen age and later gets combined with diaphysis. So the age assessment of the TW and GP methods is only applicable till 19 years. Pal and king in 1983 [3], designed the algorithm using fuzzy set theory, and employed it to the X-ray image edge detection algorithm. They have utilized ambiguity functions and continuous use of contrast enhancers to separate areas inside the plane of attribute that can be used for extra extraction of features from film of X-ray. An algorithm for automatic threshold gray level using fuzzy exponent and fuzzy set entropy was also proposed. After a long span of time, a substitute named FELS (Fels Longitudinal Study) was proposed by W.M.Cameron et al. [4]. It is an automated system which scores or grades for each and every bone as an input for calculating the age. Contrary to the GP and TW methods, in this the grade distribution is calculated from the same age group as well. In this, More than 130 points are selected for each and every bone for evaluation. This method also forecast the error correction for determination. Even they have assumed that input images are noise free. But too an extend it is a complicated method as not available in the form of software.Next model was given by David J Michael and Alan C Nelson which is the first automated system known as HANDX (1989) for bone age assessment. In this, System is sub-divided into three basic stages pre-processing, segmentation and measurement. Histogram modification algorithm is used as a model for enhancing the image.

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In this algorithm, initial step is to create a histogram and by using Gaussian distribution function, it is segmented into 3 groups as background, soft-tissues and bone pixels [5]. Then to outline the bone shape, contour process is used by masking the segmented bone image on the binary image extracted from the input image. Next a computer aided algorithm (CASAS) had been proposed in 1992 by Tanner and Gibbons. It used nine prototype images for each and every bone, stating nine mature phases. Hence, each phase was expressed through the image template. To manually enlarge input x-ray image on each bone, camera was used and the bones were matched with 2 or 3 usually similar templates. Then, the schemes calculate multiple access of the association with each and every template in addition with partial stage. The connection with the pattern was a calculation of similarity. This algorithm was acknowledged with the paediatric field as a step in the correct direction. The incongruity of the scorer was extremely reduced to attain consistency and also to reveal continuity [6]. Cox and Drayer [7] presented a CAD system for estimating the age of bone which is dependent on radiographs Fourier analysis for producing TW2 criterion for ulna, radii plus short phalanx in 1994. This had used a matching of template of each bone for the scanned image of the radiograph. The computer has produced a stage for maturity of bone of individual and total bone score with bone age values. The bone age use to calculate by the computer managed system. Some of the diseases for human may also depend on the growth disorders. H. Frish, S Reidl and T. Waldhor from Austria [8] had evaluated age for both diseased and normal people by comparing with the manual GP and TW2 (RUS) methods using with Computer Aided Skeletal Age Scores (CASAS) system in the year 1996. A total of 100 images were analysed including 30 from Turner’s syndrome, 34 from familial short stature and 36 from growth hormone deficiency. Results showed that TW2 and CASAS with almost same age estimations. Due to disorders in bone shapes manual interruption (9% to 12.3 %.) was needed and thereafter the test was repeated in some cases for accuracy CASAS was further extended by developing a digital hand atlas by F.Cao et. al [9]. The system was designed for a project, to develop digital hand atlas for medical diagnosis and available over web. M. Fernandez et. al.(2003) [10], had proposed a technique for recording manual radiograph intended for assessment by utilizing the GP technique. It was the first stage in the comprehensive shape examination of the bones that executes registration of hand-by-hand. It contains two phases of registration: the initial step is for landmark process for creating a line representation of staff as well as several efficient landmarks and for matching template image landmarks to target image landmarks. The next stage was for intensity dependent fine registration process for matching the fingers widths of two figures. The exact outcome was obtained at extremely short computational loads [11]. Histogram based automated system was developed by Marjan Mansourvar et. al in 2012, initially user was provided with an option to feed an image over web link and then a histogram was generated using the ImageMagik software[12]. Thereafter the content based image retrieval system was used to find the most relevant images from the database by assigning the ranks to the similar images (ranks were assigned using similarity score). Age was estimated using the ratio between highest ranked age from the retrieved images to the total number of ranked images but only the middle is used for analysis with a success rate of 95% [13]. Recently in 2013 Shao-Yan Zhang and et. al. [14] had given a research article on the automated bone age assessment on Chinese population. It was purely based on the BoneXpert software system. They developed a bone age scale table named as BX-China05 by comparing the age chart given by GP.A technique of carpal region of interest estimation, segmentation of carpal bones, extraction of feature and usage of fuzzy classification for skeleton age estimation was proposed and tested on the two hundred five young children from the available data in the digital hand atlas.

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III. METHODOLOGY

Bone age assessment is to determine the degree of maturity of a child’s bone. The bone age estimation is generally done through a single x-ray of the left hand, wrist, and fingers. Bone age assessment uses Left hand wrist radiograph (x-ray) image as input and the bone age of children will be estimated. There are diverse methods available also. A. Hand Radiograph : This is the first step or the pre-requisite for bone age assessment. This includes left hand X-ray image or wrist X-ray image. The reason for using left x-ray image is that most of the people are right handed and due to which right hand is used a lot for different activities. B. ROI extraction: The analysis starts with a pre-processing function yielding epiphyseal/metaphyseal regions of interest (EMROIs).it also varies according to the technique followed as in TW1 wrist bones were used as ROI. But ROI of an epiphysis and metaphysic is used to estimate the bone age efficiently. C. Image pre-processing: It is also a crucial step. It is used to remove hand borders, eliminate unwanted noise using filters, and also removes non uniformity various in background images. It can be performed in two ways i.e. image segmentation and image enhancement. D. Image segmentation/ wavelet decomposition: Segmentation is done with the reference to the axis representation of the resized radiograph of the given image. Phalangeal region are segmented from the image by the axis representation and the texture features are examined. Segmentation and wavelet decomposition method is used for separating epiphysis and metaphysic from the soft tissue. The segmented results show that diameters of epiphysis and metaphysis change with different developmental bone age. E. Feature extraction/ feature selection: Once the radiographic image is pre-processed, features are extracted from the pre-processed image, which extracts 42 features from carpal features and phalangeal features. This process performed in the way to reduce the dimensionality of data. Features are analysed from the feature extraction process and 7 efficient features are selected for the bone age assessment methods. Principal component analysis (PCA) algorithm and independent component analysis (ICA) algorithm are used to select the features efficiently.

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Fig 2 :Methodology to be followed

IV. RESULTS AND DISCUSSION

The experiment is performed in the MATLAB R2015b. Following are the results that have been achieved for optimizing the results. The whole simulation performance is measured using various metrics as shown below:

Fig 3: Main Window Volume 5, Issue 8, August /2018 Page No:654 JASC: Journal of Applied Science and Computations ISSN NO: 1076-5131

All these images are x-ray images. These images are authorized images as taken from an international and educational research site known as ipilab. For these images the pre-processing step has been excluded as these images are authorized and contains no noise.

(a) (b) Fig 4: Showing (a) Setting lines for the bones of the input image (b) calculating RUS score

By calculations the RUS score evaluated was 1.5 years. When this calculated score is matched with the database information i.e. age , so result found was accurate. Therefore, this process was repeated for all the 40images present in the database and then its accuracy was found out.

` Fig 5: Age comparison graph

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As proposed work helps to find the age using different bones, a comparison is required that whether calculated age matches with the actual age. So above graph shows the minor difference between actual and calculated age.

Fig 6: Accuracy graph

Accuracy graph is plotted using number of images on its x-axis and percentage of accuracy on its y-axis. Proposed work accuracy remains in the range of 94 %- 99 % .

V. CONCLUSION

BAA is commonly utilized for accessing the enlargement rank of kids attesting of hormonal troubles part with genetic diseases. The purpose of maturation of skeletal is dependent ton examination of radiography of region of wrist bone. The work gives a novel BAA approach using AI technology.

The proposed system is aimed to conquer the limits of methods of traditional approach, used for estimating the age of human that is frequently rough. The approach gives prediction of hand as well as wristX-ray, until the various years and two metrics i.e. error rate and accuracy rate having average values 2.5 % and 97% respectively. A comparison is being done on the basis of accuracy between proposed work and other existing work. Future work may also include estimation of growth disorders and prediction of height as well.

ACKNOWLEDMENT Authors are highly thankful to the RIC department of IKG Punjab Technical University, Kapurthala, Punjab, India for the opportunity to conduct this research work.

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REFRENCES

[1] W.W Greulich and S.I. Pyle, “Radiographic Atlas of Skeleton Development of Hand Wrist”, 2 ed. Palo Alto , CA: Stanford univ. Press,1959,pp: 19 [2] J.M. Tanner, R.H. Whitehouse, W.A.Marshall, M.J.R. Healy and H.Goldstein, “Assessment of Skeletal Maturity and Prediction of adult height( TW2 Method )”, New York, NY:Academic, 1975, pp: 157 [3] S.K. Pal , and Robert A. King, “ On Edge Detection of X-Ray Images using Fuzzy Sets”, IEEE Trans on Pattern Analysis and Machine Intelligence, vol. 5, no.1, pp. 69-77, 1983 [4] WM. Cameron Chumlea, Alex F. Rouche, David Thissen, “The FELS method of accessing the skeletal maturity of the hand-wrist”, American J.of Human Biology, 1:175-183,1989,pp:175 [5] David j. Michael, Alan C. Nelson, “ HANDX:A Model- Based System for Automatic Segmentation of Bones from Digital hand Radiographs” , IEEE Trans. on Medical Imaging, vol 8, no. 1, 1989. [6] J.M.Tanner and R.D. Gibbons, “Automatic Bone Age Measurement using Computerized Image Analysis”, J. Ped. Endocrinology, vol. 7, pp. 44-49, 1993. [7] N.M.Drayer and L.A.Cox, “ Assessment of bone ages by the Tanner- Whitehouse method using s computer-aided system”, Acta Paediatric Suppl., pp.77-80, 1994 [8] H. Frisch, S. Riedl, T. Waldhor, “Computer aided estimation of skeletal age and comparition with bone age evaluations by the Greulich-Pyle and tanner-whitehouse”, Pediatr Radiol (1996) 26:226-231 [9] F. Caoa, H.K. Huanga, E. Pietkab, V. Gilsanzc, “Digital Hand Atlas and web-based bone age assessment system design and implementation”, Computerized Medical Imaging and Graphics 24 (2000) 297-307 [10] Miguel A. Martin-Fernandez, Marcos Martin-Fernandez, Carlos Alberola-Lopez,” automatic bone age assessment: a registration approach”, Medical Imaging 2003: Image Processing, Proceedings of SPIE, vol. 5032, pp. 1765-1776,2003. [11] Santiago Aja-Fernandez, Rodrigo de Luis-Garcia, Miguel Angel Martin-Fernandez, Carlos Alberola-Lopez, “A computational TW3 classifier for skeletal maturity assessment: A Computing with Words approach”, Journal of Biomedical Informatics , vol. 37, pp. 99-107, 2004. [12] Marjan Mansourvar, Ram Gopal raj, Maizatul Akmar Ismail, Sameem Abdul Kareem, Saravanan Shanmugam, Shahrom Wahid, Rochana Mahmud, RukainiHj, Abdullah, FarizaHanum Nasaruddin and Norsisma Idris, “Automated Web based system for bone age assessment using histogram technique”, Malaysian Journal of Computer Science, Vol. 25(3), 2012. [13] Markus Brunk, Heike Ruppertshofen, Sarah Schmidt, peter Beyerlein, Hauke Schramm, “Bone age classification using discriminative generalized Hough transform, Springer”, Bildverarbeitung für die Medizin 2011, pp. 284-288. [14] Zhang SY, Liu G, Ma CG, Han YS, Shen XZ, Xu RL, Thodberg HH, Automated “Determination of Bone Age in a Modern Chinese Population”, ISRN Radiology, Volume 2013, Article ID 874570

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