Kidney stone disease is a common problem amongst the western population. Most kidney stones are small and pass spontaneously. These patients often need no further treatment. However, some nephrolithiasis patients develop large stones, which can cause significant morbidity in the form of acute symptoms and chronic complications if they are not treated. Yet effective treatment and prevention may eradicate the disease completely to overcome this we proposed wavelet approach avoids both log and exponential transform, considering the fully developed speckle as additive signal-dependent noise with zero mean. The proposed method throughout the wavelet transform has the capacity to combine the information at different frequency bands and accurately measure the local regularity of image features and watershed algorithm enhance the image in the quality way and it classifies with the Neural network.
So, we need to study about image processing for it. After that u will be able to find the process to detect the kidney stone detection. For that purpose, we are taking our trendy methodologies for it. These are the first steps for our project. Now take a better step to taking of input images using CT images of the kidney from the known data set. Due to these steps involve for the detection of the disease with the stages of the disease. Basically, in kidney stone detection will be done by unwanted waste food like if you are taking tomato for daily it will be affected on kidney.? To avoid it by using detection in early stage using pre-process, segmentation, feature extraction of GLCM and neural network classification of algorithm.
1.The first literature survey is detecting the images with doppler. Here, they are not getting better result with better techniques. This was proposed in the year of 2014.
Title: –Improved Detection of Kidney Stones Using anOptimized Doppler Imaging Sequence.
Authors: -Bryan Cunitz1, Barbrina Dunmire1, Marla Paun1, Oleg Sapozhnikov1,2, John Kucewicz1, Ryan Hsi3, Franklin Lee3,Matthew Sorensen3,4, Jonathan Harper3, Michael Bailey2
Abstract?Kidney stones have been shown to exhibit a ?twinklingartifact? (TA) under Color Doppler ultrasound. Although thistechnique has better specificity than conventional B-modeimaging, it has lower sensitivity. To improve the overallperformance of TA as a diagnostic tool, Doppler outputparameters were optimized in vitro. The collected data supportsa previous hypothesis that TA is caused by random oscillations ofmultiple micron-sized bubbles trapped in the cracks and crevicesof kidney stones. A set of optimized parameters wereimplemented such that the acoustic output remained within theFDA approved limits. Several clinical kidney scans wereperformed withthe optimized settings showing improved SNRrelative to the default settings.