Computer Aided Diagnosis System for Stone Detection and Early Detection of Kidney Stones

Tamilselvi Tamilselvi

doi:10.3844/jcssp.2011.250.254

Abstract

Problem statement: Most of the previous study in diagnosis of kidney stone identifies a mere presence or absence of the stones in the kidney. However proposal in our study even present an early detection of kidney stones which helps to change the diet conditions and prevent the formation of stones. Approach: The study presented a scheme for ultrasound kidney image diagnosis for stone and its early detection based on improved seeded region growing based segmentation and classification of kidney images with stone sizes. With segmented portions of the images the intensity threshold variation helps in identifying multiple classes to classify the images as normal, stone and early stone stages. The improved semiautomatic Seeded Region Growing (SRG) based image segmentation process homogeneous region depends on the image granularity features, where the interested structures with dimensions comparable to the speckle size are extracted. The shape and size of the growing regions depend on this look up table entries. The region merging after the region growing also suppresses the high frequency artifacts. The diagnosis process is done based on the intensity threshold variation obtained from the segmented portions of the image and size of the portions compared to that of the standard stone sizes (less than 2 mm absence of stone, 2-4 mm early stages and 5mm and above presence of kidney stones). Results: The parameters of texture values, intensity threshold variation and stones sizes are evaluated with experimentation of various Ultrasound kidney image samples taken from the clinical laboratory. The texture extracted from the segmented portion of the kidney images presented in our study precisely estimate the size of the stones and the position of the stones in the kidney which was not done in the earlier studies. Conclusion: The integrated improved SRG and classification mechanisms presented in this study diagnosis the kidney stones presence and absence along with the early stages of stone formation.

Highlights

The study has focused on the kidney image segmentation and diagnosis for stone detection and absence in the ultrasound images
The proposed integration improved Seeded Region Growing (SRG) and classification is fast enough to run at a frame rate of 20 Hz and integrated in an automated system to sweep the surface of kidney stones and remove them by remedy measures such as sound shocks to fragment the stones to negligible pieces
The study presented in the study diagnosis the kidney stone detection and early stages of formation with integrated image segmentation and classification

Summary

INTRODUCTION

The study has focused on the kidney image segmentation and diagnosis for stone detection and absence in the ultrasound images. The local variance and mean ratio of the granularity in the fully developed ultrasound speckle kidney image is used as the measured parameter for seed point selection According to this parameter, it is possible to decide whether the processed pixel is within homogeneous region or not. The proposed automated study of integrated segmentation and diagnosis provides precise stone detection in the kidney for any number of ultrasound kidney images. The detection rate for the red visually distinct texture pairs can be discriminated using spot is nearly 95%, the impact of specular reflections on above method These statistical features of second order the surface of the kidney images and eventual blood are computed in a two step process. Each (i, j)th entry of the matrices represents the probability of going from pixel with gray level (i) to another with a gray level (j) under a drops flowing through the kidney was examined with noise preprocessing stages (Rizon et al, 2005)

RESULTS

MATERIALS AND METHODS

DISCUSSION

CONCLUSION