Narrow Results

The purpose of this study was to compare the stiffness of the transverse carpal ligament (TCL) between healthy volunteers and carpal tunnel syndrome (CTS) patients using sonoelastography. We studied 17 healthy volunteers (four men, 13 women; range 37–84 years) and 18 hands of 13 patients with CTS (three men, ten women; range 41–79 years). Thickness and elasticity of the TCL were evaluated by sonoelastography. Elasticity was estimated by strain ratio of an acoustic coupler, which has a standardized elasticity as a reference medium, to the TCL (AC/T strain ratio). The AC/T strain ratios of the healthy volunteers and the CTS patients were 6.0 and 8.1, respectively (p = 0.030). The AC/T strain ratio showed a positive correlation with the duration of symptoms in the CTS patients (p = 0.035, r = 0.50). We concluded that increased stiffness of the TCL could be one of the causes for CTS.

Breast cancer is the leading cause of death in women. Early detection and early treatment can significantly reduce the breast cancer mortality. Texture features are widely used in classification problems, i.e., mainly for diagnostic purposes where the region of interest is delineated manually. It has not yet been considered for sonoelastographic segmentation. This paper proposes a method of segmenting the sonoelastographic breast images with optimum number of features from 32 features extracted from three different extraction methods: Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Edge-Based Features. The image undergoes preprocessing by Sticks filter that improves the contrast and enhances the edges and emphasizes the tumor boundary. The features are extracted and then ranked according to the Sequential Forward Floating Selection (SFFS). The optimum number of ranked features is used for segmentation using k-means clustering. The segmented images are subjected to morphological processing that marks the tumor boundary. The overall accuracy is studied to investigate the effect of automated segmentation where the subset of first 10 ranked features provides an accuracy of 79%. The combined metric of overlap, over- and under-segmentation is 90%. The proposed work can also be considered for diagnostic purposes, along with the sonographic breast images.