Detection of Turner syndrome using hand X-ray using anchor based links segmentation method

Abstract

Turner Syndrome (TS) is a chromosomal disorder, wherein the female’s growth is impacted. Immature ovaries, low stature, and heart abnormalities are a range of developmental and medical issues due to TS. The condition of TS might be detected prior to birth, throughout infancy or in the early years of life. The diagnosis of TS in girls with modest symptoms and indications is sometimes deferred until they reach adolescence or become young adults. This study presents an algorithm to segment the hand digital X-ray image in children with TS. In medical image and computer vision examination, image segmentation is demanding, and very crucial. Prevailing segmentation algorithms even now suffer from common segmentation issues including under-segmentation, over-segmentation, and spurious or non-closed edges, regardless of the several years of studies. In this paper, Anchor Based Link (ABL) segmentation approach is proposed to detect TS based on fourth Metacarpal bone from left hand X-ray images. The detection of TS is demonstrated based upon the comparison of proposed approach with existing watershed segmentation and Gaussian-Mixture-Model-based Hidden-Markov-Random-Field (GMM-HMRF) method. The proposed approach attains better segmentation based on the ratio of height and width of left fourth finger that is analyzed for normal children and children having TS with the help of edge pixel present in the metacarpal bone that has been segmented. The suggested method is verified on fifty (50) sample X-ray hand images of carpal bones, providing 0.60 ± 0.02 as an average Dice coefficient.