Analysis of polycystic kidney disease in medical ultrasound images

Abstract

The growth of kidney diseases has gradually increased in recent years. Ultrasound imaging provides the internal structure of the body to detect eventually diseases or abnormal tissues non-invasively. Segmentation of required region in ultrasound images is one of the challenging tasks. The proposed method focuses on classification of medical ultrasound images of kidney as cystic and polycystic types. Segmentation is performed using gradient vector force (GVF) snakes. Before segmentation, speckle noise is removed using Gaussian filter and contrast is enhanced. We have segmented normal, cystic and polycystic kidney ultrasound images effectively using GVF snakes. We have also carried out segmentation using morphological operations which requires a user intervention during the process of segmentation. Geometrical features are used with k-NN for classifying medical US images of kidney as normal, single cystic and polycystic types for segmented regions .The proposed method has applications in analysis of organ morphology and realising quantitative measurements.