Ultrasound Image Analysis of Kidney Stone using Wavelet Transform

A wavelet-based method is introduced in this paper for efficient speckle suppression and detection of calculi in sonographic images of the kidney.Wavelets are developed in applied mathematics for the analysis of multiscale image structures. The aim of this project is to analyse and to provide most significant content descriptive parameters to identify and classify the kidney stones with ultrasound scan. Speckle filtering is a critical preprocessing step. Daubechies DWT provides an appropriate basis for separating the speckle noise from an image. Fuzzy c means clustering is used for unsupervised image segmentation.The statistical features are extracted by decomposing the kidney stone images into different frequency sub-bands using wavelet transform. The ability of these features in classifying kidney stone is done using Backpropagation Neural Network (BPNN) and it saves the radiologist time, increases accuracy and yield of diagnosis of kidney stone.