Alpha-gamma equalization-enhanced hand radiographic image segmentation scheme

Abstract

Bone age assessment of children is a procedure frequently performed in pediatric radiology. The feature extractions of metaphyseal and epiphyseal regions are crucial to automatic bone age assessment. The first step of feature extraction is applying a segmentation scheme to find exact regions of epiphysis and metaphysis. A segmentation method is normally based on both intensity information and the relative location of pixels. There is a fundamental problem; when the intensity contrast of soft tissue and bony tissue is poor, bony and soft tissue cannot easily be separated. We propose an alpha-gamma equalization method to increase the intensity contrast between bony and soft tissue. Sobel, two-means, Canny edge-detection, and watershed methods are applied to illustrate the effect of this method on initial segmentation. Adaptive two-means and gradient vector flow snake are adopted for the final segmentation. Experimental results reveal that alpha-gamma equalization-enhanced two-means initial segmentation with an adaptive two-means clustering scheme can greatly reduce the average error measurements of segmentations. These are evaluated according to the following measurements: misclassification error, edge mismatch, region nonuniformity, relative foreground area eror, and modified Hausdorff distance. Furthermore, the experimental results show that the proposed scheme provides increased stable performance for the segmentation of epiphyseal/metaphyseal regions. (C) 2009 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.3242852]