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Reseach Article

A Study on Identifying Renal Calculi using different Techniques

Published on May 2015 by Ranjitha. M, G.m Nasira
An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds
Foundation of Computer Science USA
ICCTAC2015 - Number 2
May 2015
Authors: Ranjitha. M, G.m Nasira
3bc767a4-eda1-459d-95a1-0005f793e0ba

Ranjitha. M, G.m Nasira . A Study on Identifying Renal Calculi using different Techniques. An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds. ICCTAC2015, 2 (May 2015), 15-18.

@article{
author = { Ranjitha. M, G.m Nasira },
title = { A Study on Identifying Renal Calculi using different Techniques },
journal = { An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds },
issue_date = { May 2015 },
volume = { ICCTAC2015 },
number = { 2 },
month = { May },
year = { 2015 },
issn = 0975-8887,
pages = { 15-18 },
numpages = 4,
url = { /proceedings/icctac2015/number2/20927-2014/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds
%A Ranjitha. M
%A G.m Nasira
%T A Study on Identifying Renal Calculi using different Techniques
%J An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds
%@ 0975-8887
%V ICCTAC2015
%N 2
%P 15-18
%D 2015
%I International Journal of Computer Applications
Abstract

Identifying Renal Calculi is a major challenge in medical field. Many researchers have worked on different methods to identify the renal calculi from scanned images like Ultra sound, CT, MRI etc. The objective of this paper is to analyze different approaches suggested to detect renal calculi using various techniques. Existing literatures that have discussed the various approaches of detecting renal calculi from scanned images, categorizing them according to the methodology were reviewed. Algorithms for identifying renal calculi from Shadow, Seeded Growing Methods, Watershed Methods, Spatial gray level dependence Method and a Combinational Approach (CANR) with their advantages and limitations is discussed. CANR is compared with other methods and its performance is analyzed.

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Index Terms

Computer Science
Information Sciences

Keywords

Intensity Threshold Seeded Region Growing Preprocessing Classification Co-occurrence Matrices Watershed Method Noise Removing Smoothing