Segmentation of Shinbone Interosseous Space using GVF Techniques

In this research, a new shinbone interosseous space segmentation method was proposed. The X-ray images were obtained from Dual Energy X-ray Absorptiometry (DXA) and consisted of 3 components (muscle, fat, and bone). The DXA scanner produces two different images, gray image and a color image each containing different information. Muscle and fat are two components that can be used to calculate muscle mass. The bone area is used to compute an estimate of the bone mineral density (BMD) measurement as an osteoporosis indicator. Muscle mass is used for the body mass index calculation. X-ray images are the main source of information to measure muscle, fat, and bone area in the human body. The main problem is the ambiguous outline of each component, the position of the legs being placed into the X-ray machine, and the variety of leg shapes. Traditional segmentation methods such as watershed transformation, Noise cancellation, and Image Enhancement were used in preprocessing the images. Analysis of light concentration fluctuations to determine the location of the Gradient Vector Flow (GVF) was the technique used to find the region of interest (ROI), shinbone interosseous space. The Jaccard Index percentage reach 93.92%. Overlapped Area percentage reaches 93.51%.