Description

ABSTRACT

Kidney stone disease (nephrolithiasis) 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

DEMO VIDEO

INTRODUCTION

Kidney stone is a solid piece of material formed due to minerals in urine. These stones are formed by combination of genetic and environmental factors. It is also caused due to overweight, certain foods, some medication and not drinking enough of water. Kidney stone affects racial, cultural and geographical group. Many methods are used for diagnosing this kidney stone such as blood test, urine test, scanning. Scanning also differs in CT scan, Ultrasound scan and Doppler scan. Now days a field of automation came into existence which also being used in medical field. Rather many common problems rose due to automatic diagnosis such as use of accurate and correct result and also use of proper algorithms. Medical diagnosis process is complex and fuzzy by nature. Among all methods soft computing method called as neural network proves advantages as it will diagnosis the disease by first learning and then detecting on partial basis  In this paper two neural network algorithms i.e Feature extraction and watershed are used for detecting a kidney stone. Firstly two algorithms are used for training the data. The data in the form of blood reports of various persons having kidney stone is obtained for various hospitals, laboratories.

EXISTING SYSTEM

In Existing system we used Gabor filter and SVM classifier by that we wont get appropriate accuracy and high complexity by that we cant find the detection of stones in kidneys

PROPOSED SYSTEM

Here  in proposed methodology we are using the median filter to improve the quality of image by that we can see clearly without any noise we use GLCM for feature extraction to extract the image and classifies with the neural network whether to be known as effected or not.

BLOCK DIAGRAM

METHODOLOGY

• Discrete wavelet transform
• Watershed algorithm
• SFCM
• K-Means clustering
• Neural networks

ADVANTAGES

• Detect  in intial stage
• High accuracy
• Low complexity

APPLICATIONS

• Biomedical
• Medical Image Testability

SOFTWARE REQUIRMENTS

• MATLAB 7.14

REFERENCES

[1] Koushal Kumar, Abhishek, “Artificial Neural Networks for Diagnosis of Kidney Stones Disease”, International Journal Information Technology and Computer Science, 2012, 7, 20-25

[2] Tijjani Adam and U. HAshim And U.S.Sani , “Designing of Artificial neural network model for the prediction of kidney problem symptoms through patients mental behavior for pre clinical medical diagnosis”,ICBE Feb. 2012

[3] Rouhani M. et al, “The comparison of several ANN Architecture on thyroid disease”, Islami Azad University, Gonabad branch Gonabad ,2010

[4] Shukla A. et al , “Diagnosis of Thyroid Disorders using Artificial Neural Networks”, Department of Information Communication and Technology, ABV-Indian Institute of  Technology

[5] Duryeal A.P. et al, “Optimization of Histotripsy for Kidney Stone EROSION”, Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 2Department of Urology, University of Michigan, Ann Arbor, MI 2010

[6] Koizumi N et al , “Robust Kidney Stone Tracking for a Non-invasive Ultrasound’’, Shanghai International Conference Center May 9-13, 2011, Shanghai, China

[7] Sandhya A et al , “Kidney Stone Disease Etiology and Evaluation Institute of Genetics and Hospital for Genetic Diseases’’, India International Journal of Applied Biology and Pharmaceutical Technology, may june 2010