Automated Approach to Detect and Monitor the Development of Turner’s Syndrome

Abstract

Turner Syndrome (TS) is an illness that primarily affects females and is caused by a defective or partly misplaced X chromosome (sex chromosome). In this paper we discussed about monitoring the prognosis of TS in subjects of age 9-14 years to study how Turner’s affect their growth. This research work presents an algorithm to segment the hand digital X-ray images of children with TS. Identification of TS is proven in this study utilizing the 4th Metacarpal bone from left hand X-ray images centered on Anchor Based Link (ABL) segmentation technique. Then various features such as mean, variance, skewness, and kurtosis are extracted from normal and turner subjects of different age groups from 9-14years. This paper analyzed proposed ABL segmentation through ANOVA analysis which proves that as age of the turner subject increases growth occurs but it is lesser than the healthier subject. Based on the F value analysis which is below 0.5 it accurately differentiates normal and turner subject.