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A Web-accessible Framework for Automated Storage with Compression and Textural Classification of Malaria Parasite Images

by Maitreya Maity, Ashok K. Maity, Pranab K. Dutta, Chandan Chakraborty
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 52 - Number 15
Year of Publication: 2012
Authors: Maitreya Maity, Ashok K. Maity, Pranab K. Dutta, Chandan Chakraborty
10.5120/8279-1906

Maitreya Maity, Ashok K. Maity, Pranab K. Dutta, Chandan Chakraborty . A Web-accessible Framework for Automated Storage with Compression and Textural Classification of Malaria Parasite Images. International Journal of Computer Applications. 52, 15 ( August 2012), 31-39. DOI=10.5120/8279-1906

@article{ 10.5120/8279-1906,
author = { Maitreya Maity, Ashok K. Maity, Pranab K. Dutta, Chandan Chakraborty },
title = { A Web-accessible Framework for Automated Storage with Compression and Textural Classification of Malaria Parasite Images },
journal = { International Journal of Computer Applications },
issue_date = { August 2012 },
volume = { 52 },
number = { 15 },
month = { August },
year = { 2012 },
issn = { 0975-8887 },
pages = { 31-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume52/number15/8279-1906/ },
doi = { 10.5120/8279-1906 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:52:19.760753+05:30
%A Maitreya Maity
%A Ashok K. Maity
%A Pranab K. Dutta
%A Chandan Chakraborty
%T A Web-accessible Framework for Automated Storage with Compression and Textural Classification of Malaria Parasite Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 52
%N 15
%P 31-39
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Malaria being one of the serious health burdens especially in Indian population is conventionally diagnosed by expert pathologists through microscopic observation of stained peripheral blood smears. In order to provide rapid and efficient healthcare support to the common people at rural areas where experts are not (often) available, there is indeed a requirement of developing web-enabled healthcare system. In view of this, in this study, a web-accessible framework for automated storage of compressed microscopic images and texture-based screening of malaria parasite has been developed to provide rapid and efficient diagnosis even at remote public health clinics. It consists of (a) automated storage of microscopic images followed by JPEG image compression for faster transmission; (b) watershed transform based erythrocyte segmentation followed by image preprocessing; (c) texture feature extraction and selection; and (d) supervised classification and validation. Here, total 76 textures are extracted from segmented erythrocytes. Twenty six significant features are selected by using SVM based recursive feature elimination (SVM-RFE) method. Thereafter, supervised classifiers viz. Naïve Baye's approach, C4. 5 and NBTree are considered for six-class classification problem and their performance are compared. From the result, it has been found that NBTRee classifier provides higher accuracy to classify P. vivax and P. falciparum (sensitivity: 99. 0%, specificity: 99. 8%) with different stages viz. ring, gametocytes and scizon under our developed web-accessible framework.

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Index Terms

Computer Science
Information Sciences

Keywords

Web application J2EE platform Compression JPEG Malaria Screening Texture Feature Extraction Classification

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