Tetrolets-based System for Automatic Skeletal Bone Age Assessment
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.
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Introduction
The chronological situations of humans are described by certain indices such as height, dental age, and bone maturity. Of these, bone age measurement plays a significant role because of its reliability and practicability in diagnosing hereditary diseases and growth disorders. Bone age assessment using a hand radiograph is an important clinical tool in the area of pediatrics, especially in relation to endocrinological problems and growth disorders. A single reading of skeletal age informs the clinician of the relative maturity of a patient at a particular time in his or her life and integrated with other clinical finding, separates the normal from the relatively advanced or retarded [1]. The bone age of children is apparently influenced by gender, race, nutrition status, living environments and social resources, etc. Based on a radiological examination of skeletal development of the left-hand wrist, bone age is assessed and compared with the chronological age. A discrepancy between these two values indicates abnormalities in skeletal 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 patient can be treated with growth hormones. BAA is universally used due to its simplicity, minimal radiation exposure, and the availability of multiple ossification centers for evaluation of maturity.
Conclusion
The work presents an efficient system for skeletal age assessment. The system takes digital left hand wrist radiographs as input and outputs the skeletal bone age as the output. There are two phases: the training phase and the testing phase. The former is used to build and fine tune the ID3 decision tree classifier and the latter is used to estimate the bone age. The input images were first preprocessed by smoothening with Gaussian filter. Then edges of the bones were detected using Sobel operator and segmentation was done by a new PSO algorithm using Tetrolets. From the segmented ROI, 9 carpal features and 42 phalangeal features were extracted. The extracted features were analyzed using PCA and among them 7 dominant features were selected. Feature modeling was done to convert the selected features into a form understandable by the classifier. The ID3 classifier mapped the features onto the bone age class for the image. This bone age class disguised the bone age. 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 86% for girls and 84% for boys. Future work will be focused on extending the system to work on the age group above 10 years, broadening the system to include the further TW2 bones such as radius, ulna, etc. and integrating the system with PACS