An Automatic System for Skeletal Bone Age

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<p>SUBMITTED TO THE MEMEA 2009 SPECIAL ISSUE OF IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT</p> <p>1</p> <p>An Automatic System for Skeletal Bone Age Measurement by Robust Processing of Carpal and Epiphysial/Metaphysial BonesDaniela Giordano, Member, IEEE, Concetto Spampinato, Giacomo Scarciofalo and Rosalia Leonardi</p> <p>Abstract This paper presents an automatic system for bone age evaluation according to the clinical method of Tanner and Whitehouse (TW2), based on the integration between two systems: the rst processes the ngers bones (EMROI - Epiphyses/Metaphyses ROI), whereas the second one the wrist bones (CROI - Carpal ROI). The system ensures an accurate bone age assessment for the age range (0-10) years for males and (0-7) years for females. For both approaches novel segmentation techniques are proposed. In detail, for CROI analysis the bones extraction is carried out by integrating anatomical knowledge of the hand and trigonometric concepts, whereas the TW2 stage assignment is achieved by combining the Gradient Vector Flow (GVF) Snakes and derivative difference of Gaussian (DrDoG) lter. One of the main difculties for bone age assessment based on carpal bones is that Trapezium and Trapezoid, which are among the biggest bones in the wrist, are often fused even in very young patients. This problem is overtaken by a very effective algorithm that checks the compactness of the identied bones and separates them by using a curvature function. For EMROI analysis image processing techniques and geometrical features analysis, based on difference of Gaussian (DoG) are proposed. The system was evaluated on a set of 106 X-Rays, reaching performances of about 90% success rate in bone stages assignment. The system is very reliable and outperforms other effective methods. Moreover the mean error rate is of about 0.46 0.37 years that is comparable with clinicians reliability, for whom the error has been estimated to be 0.33 0.6 years. Index Terms Skeletal Bone Age, Tanner Whitehouse Method (TW2), X-Ray Image Processing, Trapezium/Trapezoid Fusion Management, Image Segmentation.</p> <p>I. I NTRODUCTION The determination of the skeletal maturity or bone age, plays a very important role both in diagnostic and in therapeutic investigations of endocrinological problems, growth disorders of children and even genetics disordersD. Giordano, C. Spampinato and G. Scarciofalo are with the Department of Informatics and Telecommunication Engineering, University of Catania, Catania, 95125 Italy e-mail: {dgiordan, cspampin, gscarci}@diit.unict.it R. Leonardi is with the Department of Orthodontics, Policlinico Gaspare Rodolico - University of Catania Via S.Soa 78 - 95123 Catania - Italy e-mail: rleonard@unict.it</p> <p>SUBMITTED TO THE MEMEA 2009 SPECIAL ISSUE OF IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT</p> <p>2</p> <p>[1]. There are various indexes describing the chronological development of humans, e.g. height [2] and dental age [3]; however, bone age measurement is widely used for its feasibility and reliability in diagnosing hereditary diseases and endocrine disorders [4] and it can serve as an indication of the therapeutic effect of treatment [5] because of the discriminant nature of development stages of the non dominant hand. Bone age evaluation is usually performed by radiological examination of the skeletal development of the left-hand, and then is compared with the chronological age. A discrepancy between these two values surely indicates abnormalities. This examination is widely used due to the advantages of simplicity, a minimum radiation exposure, and the availability of multiple ossication centers for the evaluation of the bones maturity. There is no standard clinical procedure in bone age assessment, even if the most used methods are: 1) the Greulich and Pyle (G&amp;P) method [6] and 2) the Tanner and Whitehouse (TW2 or TW3) methods [7]. Both methods rely on X-Ray images taken from the left hand, but there are several differences between the two methods. The G&amp;P method is the most often used approach (by 76% of radiologists) especially in the Netherlands [8]), mainly because the G&amp;P method is faster and easier to use with respect to the TW2 or TW3 methods, since it involves only the comparison of the whole hand with a reference atlas. The main shortcoming of G&amp;P approach is the variability of the analysis performed by various observers with different levels of training, since studies have shown interobserver differences varying from 0.07 to 1.25 years and intraobserver differences varying in average from 0.11 to 0.89 years [9]. The average time of single case reading depends on radiologistss clinical experience and falls in the range 2 to 5 minutes. The Tanner-Whitehouse methods (especially used in United States) differ from the G&amp;P method because instead of using the hand as a whole, they are based on a set of bones, whose standard maturity varies according to age population. In detail, the TW2 method ROIs (Region of Interest) considered for the bone age evaluation are located in the main bones, including EMROI (Ephiphysial/Metaphysial ROI), CROI (Carpal ROI), radio and ulna. The development of each ROI is divided into discrete stages and each stage is given a letter (A, B, C, D, , I). A numerical score is further associated with each stage of each bone. By adding the scores of all ROIs, an overall maturity score is obtained. The TW2 method has been modied throughout the years, evolving in the TW3 method, which maintains the description for the bones stages but calibrates the scoring method on North American children [7]. Although the TW2 method yields a more accurate estimation than the G&amp;P method (the average interobserver spread for TW2 is 0.74 years against the 0.96 years for the G&amp;P method [10]), it is less used because of its high complexity, yet its modular structure makes it suitable for automation. The automation of the bone age assessment has been tackled with two main approaches: image processing and segmentation techniques for geometrical features extraction and knowledge based methods. Concerning the image processing methods, the rst semi automated system has been developed by Michael [11]. The author claims that the system was able to automatically segment the bones in a hand radiograph but large scale tests demonstrated that the system was not reliable when hand bones are fused. Manos [12] developed a segmentation method for the wrist using region growing and merging. The technique that is used is basically a bottom up approach. A standard thresholding technique for carpal ROI extraction is proposed by Pietka in [8] but no assessment was carried out and moreover the used thresholding is quite obsolete. One</p> <p>SUBMITTED TO THE MEMEA 2009 SPECIAL ISSUE OF IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT</p> <p>3</p> <p>of the most known methods for the automation of TW2, by using EMROI analysis, has been proposed in [13], where the regions of interest are processed by an ad hoc feature extraction function. This method shows an high accuracy measured independently at three stages of the image analysis: detection of phalangeal tip, extraction of the EMROIs, and location of diameters and lower edge of the EMROIs. This is the rst work where the EMROI bones are segmented instead of nding the position of their edges. This system has been extended by assessing the degree of epiphysial fusion by wavelet transform [14]. Other approaches based on ACM (Active Contours Models- snakes) [15] have also been proposed. Garcia in [16] proposed a fully automatic system for detecting bone contours based on prior knowledge to locate initially the snakes (ACM). The ACM models have been used even for 3-D wrist detection as shown in [17]. Niemeijer [18] introduced a bone age estimation method that uses ASM (Active Shape Models) but presents the problem of initial conditions, i.e., appropriate positioning of the models on the image. In [19] carpal bones are segmented by using gradient vector ow GVF - Snakes [20] after a suitable preprocessing phase via anisotropic diffusion. All the above works only make use of computed features, but there are other methods that assess the development stage with knowledge based techniques, taking as input the computed features. The methods proposed by Mahmoodi in [21] and in [22] employs decision theoretic approaches based on Bayesian Networks; fuzzy systems have been used in [23] and [24]. In [25] the authors describe a system that implements the TW2 method using a neural network architecture. Each bone complex is localized on the image, and preprocessed using either a Gabor transform or a multi-scale Difference of Gaussian ltering. The main problem of all the above methods is that they rely only on EMROI analysis, which is a interesting and an relatively easy task from the image processing eld side, but is not reliable for a complete bone age assessment, because it doesnt facilitate evaluation the bones growth. This would require a more complete analysis including the CROI processing. Not many approaches have been proposed for the CROI processing and most of the approaches for CROI extraction and analysis use classical image segmentation, such as region growing, or edge detection techniques for features extraction and not for bone evaluation such as [8] and [26]. An approach where a radius ulna TW3 bone age assessment is carried out is proposed in [27] where bone age is estimated with the help of a Generalized Softmax Perceptron (GSP) Neural Network (NN) whose optimal complexity is estimated via the Posterior Probability Model Selection (PPMS) algorithm. A very recent method is the BoneXpert proposed by Thodberg in [28], where a model for the bones reconstruction and an unied modeling of TW and G&amp;P bone age are proposed. However, BoneXpert lacks in the X-ray preprocessing capabilities, in fact it rejects images with poor image quality or abnormal bone structure, thus making the analysis in some case manual. Another shortcoming of all the above methods is that they depend strongly on the user settings, for example on hand orientation. The recognition and separation of carpal bones from the image is a very difcult task, because 1) of the presence of several overlapping regions of interest, 2) of ROI having completely different degree of calcication and overlapping with soft tissue and 3) of the borders of individual carpal bones hardly visible. A method that tried to integrate the CROI analysis and EMROI analysis was proposed in [29], but it is largely a manual method, since just single parts</p> <p>SUBMITTED TO THE MEMEA 2009 SPECIAL ISSUE OF IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT</p> <p>4</p> <p>of the system are automatized. In this work we propose a fully automatic system for bone age evaluation, according to the TW2 method, based on the integration between EMROI and CROI analysis and ensuring accurate bone age assessment for the age range (0-9) years for males and (0-7) years for females. Indeed medical studies [30] indicate that the analysis of carpal bones do not provide accurate and signicant information for patients older then 9-12 years; but, on the other hand, over that range the bone evaluation is not very reliable and sometime is not required. The main difculty in CROI integration is the partial or total fusion between the Trapezium and the Trapezoid, which has been solved in our work with a novel and effective segmentation technique in conjunction with the GVF- Snakes and ASM for carpal bone labeling and classication. This method allows both ASM and ACM to be independent from the initial conditions because the snakes are applied only after detecting and managing the fusion between Trapezium and Trapezoid. The remainder of the paper is organized as follows. Section II presents an overall description of the system. Section III shows the preprocessing system that aims 1) to remove labels from the input X-Ray and 2) to rotate the hand when it is necessary. Section IV describes how the different features of the EMROI system are extracted and measured by image processing techniques. In section V a novel segmentation technique for CROI fusion management and features extraction by using GVF-snakes is presented. Section VI illustrates the methods used for evaluating the performance of the system. Finally, section VII presents the results of the evaluation and the conclusions. II. P ROPOSED S YSTEM OVERVIEW The proposed system is based on three stage model. The rst stage (preprocessing) enhances specic radiological areas, segments out predened anatomical regions, rotates the hand when it is needed. The second stage delivers quantitative parameters or measures for the automatically extracted CROIs and EMROIs. The nal stage performs the decision making process (i.e. stage assignment and overall bone assessment). Due to various factors, the hand X-rays images are often poor in contrast, and image features in the cross sectional part are often obscured and degraded by artifacts. In order to obtain accurate feature information, we apply image preprocessing methods to remove artifacts and degradations such as blurring and noise. Moreover, a preprocessing phase for background removal and hand rotation is needed. For both EMROI and CROI analysis, novel segmentation techniques are proposed. In detail, for CROI analysis active contour model (ACM) based on GVF-Snakes and ASM, according to [31], and derivative difference of Gaussian (DrDoG) techniques are proposed. For EMROI analysis, image processing techniques and geometrical features analysis, based on difference of Gaussian (DoG), are introduced. An innovative method, which is inspired by a method proposed in a motion detection system [32], for carpal bones separation is also presented. The ow diagram of the proposed system is shown in g. 1, whereas g. 2 shows the fteen bones taken into account: eight phalanges (EMROI) and seven carpal bones (CROI). III. P REPROCESSING S YSTEM The preprocessing stage is a very important task in order to standardize X-ray images, because there are many irregularities present in the radiograph, the bone contours are not well dened and there is a large variability</p> <p>SUBMITTED TO THE MEMEA 2009 SPECIAL ISSUE OF IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT</p> <p>5</p> <p>Fig. 1: General Procedure for Skeletal Bone Age Assessment.</p> <p>Fig. 2: EMROIs and CROIs.</p> <p>between radiographs. The proposed preprocessing systems includes: 1) background suppression, 2) radiological markers removal and 3) hand rotation. The rst two steps aim at removing the background and the radiological markers on the X-ray radiograph. Since the difference in gray levels between bone and non bone pixels (labels and soft tissue) is not consistent across the image, a simple thresholding on the original image is not always successful. To overcome this problem, for background separation we use an algorithm that operates only over the background pixels, identied as...</p>

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