ABSTRACT: This paper presents the design and implementation of the tetrolets based system for automatic skeletal Bone Age Assessment (BAA). The system works according to the renowned Tanner and Whitehouse (TW2) method, based on the carpal and phalangeal Region of Interest (ROI). The system ensures accurate and robust BAA for the age range 0-10 years for both girls and boys. Given a left hand-wrist radiograph as input, the system estimates the bone age by deploying novel techniques for segmentation, feature extraction, feature selection and classification. Tetrolets are used in combination with Particle Swarm Optimization (PSO) for segmentation. From the segmented wrist bones, the carpal and phalangeal ROI are identified and are used in morphological feature extraction. PCA is employed as a feature selection tool to reduce the size of the feature vector. The selected features are fed in to an ID3 decision tree classifier, which outputs the class to which the radiograph is categorized, which is mapped onto the final bone age. The system was evaluated on a set of 100 radiographs (50 for girls and 50 for boys), and the results are discussed. The performance of system was evaluated with the help of radiologist expert diagnoses. The system is very reliable with minimum human intervention, yielding excellent results.
Keywords: Bone Age Assessment (BAA), TW2, radiograph, Particle Swarm Optimization (PSO), Tetrolets, ID3, Classification.
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