Efficient skeletal bone age estimation method using carpal and radius bone features

Transcription

1 474 Journal of Scientific & Industrial Research J SCI IND RES VOL 71 JULY 2012 Vol. 71, July 2012, pp Efficient skeletal bone age estimation method using carpal and radius bone features P Thangam 1 *, K Thanushkodi 2 and T V Mahendiran 3 1 CSE (PG) Department, Sri Ramakrishna Engineering College, Coimbatore , India 2 Akshaya College of Engineering and Technology, Coimbatore , India 3 EEE Department, Coimbatore Institute of Engineering and Technology, Coimbatore , India Received 30 January 2012; revised 08 May 2012; accepted 09 May 2012 This study presents design and implementation of an efficient skeletal bone age assessment (BAA) procedure based on features extracted from two dominant wrist bone structures (radius & carpal bones). System works according to renowned Tanner & Whitehouse (TW2) method, based on carpal and epiphyseal region of interest (ROI). It ensures accurate and robust BAA for both girls and boys (age range, 0-10 y). BAA procedure composes of two components, one dealing with carpal bones [Carpal ROI (CROI) sector] and the other with radius bone segments [Radius ROI (RROI) sector]. Input image is initially cropped to extract desired ROI. Anisotropic diffusion filter is employed to reduce noise before extracting features. This is followed by morphological feature extraction, during which two feature ratios (1 from CROI features and 1 from RROI features) are computed. Feature ratios are used to initially train the system and finally to estimate skeletal bone age. The system was trained with a data set of 120 radiographs (60 males & 60 females) and was tested on a set of 100 radiographs (50 males & 50 females). Keywords: Bone age assessment (BAA), Carpal region of interest (CROI), Morphological feature extraction, Radiograph, Radius region of interest (RROI) Introduction Bone age assessment (BAA) using a hand radiograph is an important clinical tool in the area of pediatrics, especially in relation to endocrinological problems and growth disorders 1. It is universally used due to its simplicity, minimal radiation exposure, and availability of multiple ossification centers for evaluation of maturity. Main clinical methods for skeletal bone age estimation 2 are Greulich & Pyle (GP) method and Tanner & Whitehouse (TW) method. GP method is faster and easier to use than TW method, but the latter is more accurate 3. TW method uses a detailed analysis of each individual bone, and score is correlated with bone age differently for males and females 4. In 1989, Michael & Nelson 5 developed a modelbased system for automatic segmentation of bones from digital hand radiographs (HANDX). In 1991, Pietka et al 6 conducted phalangeal analysis in several stages by measuring lengths of distal, middle and proximal *Author for correspondence saithangam@gmail.com phalanx, which were converted into skeletal age by using standard phalangeal length table proposed by Garn et al 7. In 1992, Tanner & Gibbons 8 introduced computerassisted skeletal age scores (CASAS) system. In 1993, Pietka et al 9 performed phalangeal and carpal bone analysis using standard and dynamic thresholding methods to assess skeletal age. Cheng et al 10 proposed methods to extract a region of interest (ROI) for texture analysis in 1994, with particular attention to patients with hyperparathyroidism. Drayer & Cox 11 designed a computer aided system to estimate bone age based on Fourier analysis on radiographs to produce Tanner & Whitehouse (TW2) standards for radius, ulna and short finger bones. Al-Taani et al 12 classified bones of handwrist images into pediatric stages of maturity using Point Distribution Models (PDM). Wastl & Dickhaus 13 proposed a pattern recognition based BAA approach consisting four major steps (digitization of hand radiograph, segmentation of ROI, prototype matching and BAA). Mahmoodi et al 14 used knowledge-based Active Shape Models (ASM) in an automated vision system to assess bone age. Pietka et al 15 conducted a computer assisted BAA procedure by extracting and using

2 THANGAM et al: AN EFFICIENT SKELETAL BONE AGE ESTIMATION METHOD 475 a) b) c) d) e) f) Fig. 1 Bone features: a) cropped RROI; b) pre-processed RROI; c) marked RROI; d) cropped CROI; e) pre-processed CROI; and f) marked CROI epiphyseal/ metaphyseal ROI (EMROI). Niemeijer et al 16 automated TW method by constructing a mean image and using a query image to assess age. Fernandez et al 17 described a method for registering human hand radiographs for automatic BAA using GP method, and proposed a fuzzy logic based neural network (NN) architecture for BAA 18. Garcia et al 19 presented a fully automatic algorithm to detect bone contours from hand radiographs using active contours. Lin et al 20 proposed a novel and effective carpal bone image segmentation method using GVF model, to extract a variety of carpal bone features. Tristan & Arribas 21 designed an end-toend system to partially automate TW3 BAA procedure, using a modified K-means adaptive clustering algorithm for segmentation, extracting up to 89 features and employing LDA for feature selection and finally estimating bone age using a Generalized Softmax Perceptron (GSP) NN, whose optimal complexity was estimated via Posterior Probability Model Selection (PPMS) algorithm. Zhang et al 22 developed a knowledge based carpal ROI analysis method for fully automatic carpal bone segmentation and feature analysis for BAA by fuzzy classification. Thodberg et al 23 proposed a Bone Xpert method, which divided the processing into three layers to reconstruct bone borders, to compute an intrinsic bone age value for each bone and to transform intrinsic bone age value using a relatively simple post-processing. Giordano et al 24 designed an automated system for skeletal bone age evaluation using DoG filtering and a novel adaptive thresholding. Hsieh et al 25 proposed an automatic bone age estimation system based on phalanx geometric characteristics and carpal fuzzy information. Liu & Liu 26 proposed an automatic BAA method with image template matching based on PSO. Giordano et al 27 presented an automatic system for BAA using TW2 method by combining Gradient Vector Flow (GVF) Snakes and derivative difference of Gaussian filter. A computerized BAA method 28 for carpal bones, by extracting features from convex hull of each carpal bone, has been described. This study presents design and implementation of an efficient skeletal BAA procedure based on features extracted from two dominant wrist bone structures (radius and carpal bones). Experimental Section Proposed BAA system comprises of two divisions, carpal region of interest (CROI) sector and radius region of interest (RROI) sector, where each sector involves three modules (ROI extraction & pre-processing, geometric feature extraction, and bone age estimation). Data Set and Pre-processing Database consist a total of 220 images (110 male and 110 female images). System was trained with 6 male and 6 female radiographs for each age group, thus with a total of 120 radiographs, 60 male and 60 female cases. Subsequently, 50 radiographs of males and 50 of females were used for testing. ROI are cropped and isolated for carpal and radius bones. Cropped ROI are pre-processed for noise reduction using anisotropic diffusion filter 29, and marked CROI and RROI for feature extraction (Fig. 1).

3 476 J SCI IND RES VOL 71 JULY 2012 Fig. 2 Carpal bones Table 1 Feature set Class Age MR ratio (male) MR ratio (female) MCROI (male) MCROI (female) A B C D E F G H I J Age class A B C D E F G H I J Table 2 Criteria selection Age value v (y) 1 month < v < 1 y 1 y < v < 2 y 2 y < v < 3 y 3 y < v < 4 y 4 y < v < 5 y 5 y < v < 6 y 6 y < v < 7 y 7 y < v < 8 y 8 y < v < 9 y 9 y < v < 10 y Feature Set Feature set consists of two geometric features [RROI ratio (RR) & CROI ratio (CR)]. RR is calculated using diameter measures marked (Fig. 1c). Diameter of epiphysis (E Diam ) and that of radius bone (R Diam ) were measured as width of epiphysis and width of radius bone respectively and used in calculating RR as RR = E Diam / R Diam. CR was computed as CR = Area Bone / Area Carpal, where numerator consists of bone area of single carpal bone under consideration and denominator consists of total bone area of cropped carpal ROI. Final CR is computed as average of CR of all carpal bones (Fig. 2) present. Bone Age Estimation Feature extraction is followed by either training phase or testing phase. In case of training, CR and RR are computed for all 6 cases for each age group (1-10 y) of male and female. Then, mean of all 6 CRs and RRs are computed for both male and female cases resulting in mean CROI ratio (MCR) and mean RROI ratio (MRR). Inputs to the system will be two ratios, and output will be skeletal class or category, to which radiograph is classified. Output class is chosen based on closest match 30 obtained for MCR and MRR in feature table (Table 1) as follows: i) MCR and MRR values are computed for input image; ii) computed ratios are subtracted from MCR and MRR values (Table 1) as per gender; iii) class with the minimum values for both differences is output as the final age class; and iv) selected class is then mapped onto skeletal bone age based on the criteria (Table 2).

4 THANGAM et al: AN EFFICIENT SKELETAL BONE AGE ESTIMATION METHOD 477 Accuracy, % Chronological age Fig. 3 Accuracy of feature extraction Range of values Classes precision % recall % specificity % accuracy % Fig. 4 Performance metrics graph Results and Discussion Performance of proposed system in estimating bone age was evaluated using a dataset consisting of 100 radiographs (50 for boys & 50 for girls). Ease and accuracy of feature extraction for CROI were slightly imprecise when compared to RROI (Fig. 3), because bones were comparatively interior. Also, accuracy of feature extraction from CROI tends to decline slightly as number of ossified carpal bones increases. On the other hand, accuracy of feature extraction from RROI improves as age increases. Accuracy of classification was measured in terms of four metrics (precision, recall, specificity and accuracy), which are measured by using a confusion matrix representation for each class (A J), consisting of true positive (TP), false negative (FN), false positive (FP), and true negative (TN) cases. From confusion matrix, performance metrics are calculated as Precision =TP / (TP + FP) Recall = TP / (TP + FN) Specificity = TN / (TN + FP) (1) (2) (3) Accuracy = (TP + TN) / (TP + TN + FP + FN) (4) System performance (Fig. 4) was validated by using diagnoses results obtained for data set from two radiologists. From metrics values (Table 3), minimum values were found as follows: precision, 80% for class H; recall, 70% for class J; specificity, 98% for classes G & H; and accuracy, 96% for classes H & J. Classes G and H found to downgrade the overall performance of classifier, because of no huge difference between measured features (diameter & area of bones) for those two age classes. Best performance was found in classes A and B, since there are very few ossified bones and good inter-class difference.

5 478 J SCI IND RES VOL 71 JULY 2012 Table 3 Classification metrics Class Confusion matrix precision% recall% specificity% accuracy% A B C D E F G H I J Conclusions An efficient system for BAA using features extracted from carpal and radius bones was developed. Proposed approach was found reasonably appropriate for age group (0-10 y) since main wrist bones (radius & carpal bones) that contribute toward BAA at that stage were considered. Proposed bone age estimation procedure using CROI and RROI features proved to be efficient for both male and female cases, when validated with the results obtained from two radiologists. Acknowledgements Authors thank Dr R Saravanan, MD (Ped.), KKCT Hospital, Chennai for providing database of hand radiographs, and Dr R Shankar Anandh, MD (RD) & Dr S Sanjitha, DMRD (RD), Barnard Institute of Radiology, Chennai for diagnosing hand radiographs and system validation. References 1 Gilsanz V & Ratib O, Hand Bone Age A Digital Atlas of Skeletal Maturity (Springer-Verlag, New York) 2005, Spampinato C, Skeletal Bone Age Assessment (University of Catania, Viale Andrea Doria, Italy) 1995, Bull R K, Edwards P D, Kemp P M, Fry S & Hughes I A, Bone Age Assessment: a large scale comparison of the Greulich and Pyle, and Tanner and Whitehouse (TW2) methods, Arch Dis Child, 81 (1999) Tanner J M & Whitehouse R H, Assessment of Skeletal Maturity and Prediction of Adult Height (TW2 method) (Academic Press, London) 1975, Michael D J & Nelson A C, HANDX: A model-based system for automatic segmentation of bones from digital hand radiographs, IEEE Trans Med Imag, 8 (1989) Pietka E, McNitt-Gray M F & Huang H K, Computer-assisted phalangeal analysis in skeletal age assessment, IEEE Trans Med. Imag, 10 (1991) Garn S M, Hertzog K P, Poznanski A K & Nagy J M, Metacarpophalangeal length in the evaluation of skeletal malformation, Radiology, 105 (1972) Tanner J M & Gibbons R D, Automatic bone age measurement using computerized image analysis, J Ped Endocrinol, 7 (1994) Pietka E, Kaabi L, Kuo M L & Huang H K, Feature extraction in carpal-bone analysis, IEEE Trans Med Imag, 12 (1993) Cheng S N C, Chen H, Niklason L T & Alder R S, Automated segmentation of regions on hand radiographs, Med Phy, 21 (1994) Drayer N M & Cox L A, Assessment of bone ages by the Tanner-Whitehouse method using a computer-aided system, Acta Paediatric Suppl, 406 (1994) Al-Taani A T, Ricketts I W & Cairns A Y, Classification of hand bones for bone age assessment, in Proc Third IEEE Int Conf on Electronics, Circuits, and Systems, ICECS 96 (IEEE Conf Publ, USA) 1996, Wastl S & Dickhaus H, Computerized Classification of maturity stages of hand bones of children and juveniles, in Proc 18th IEEE Int Conf EMBS (IEEE Conf Publ, USA) 1996, Mahmoodi S, Sharif B S, Chester E G, Owen J P & Lee R E J, Automated vision system for skeletal age assessment using knowledge based techniques, in IEEE Conf Publ, issue 443 (IEEE Conf Publ, USA) 1997, Pietka E, Gertych A, Pospiech S, Cao F, Huang H K & Gilsanz V, Computer-assisted bone age assessment: Image preprocessing and epiphyseal/ metaphyseal ROI extraction, IEEE Tran. Med Imag, 20 (2001)

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