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Reseach Article

Data mining, Classification and Clustering with Morphological features of Microbes

by Chayadevi M.l, Raju G.t
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 52 - Number 4
Year of Publication: 2012
Authors: Chayadevi M.l, Raju G.t
10.5120/8187-1547

Chayadevi M.l, Raju G.t . Data mining, Classification and Clustering with Morphological features of Microbes. International Journal of Computer Applications. 52, 4 ( August 2012), 1-5. DOI=10.5120/8187-1547

@article{ 10.5120/8187-1547,
author = { Chayadevi M.l, Raju G.t },
title = { Data mining, Classification and Clustering with Morphological features of Microbes },
journal = { International Journal of Computer Applications },
issue_date = { August 2012 },
volume = { 52 },
number = { 4 },
month = { August },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume52/number4/8187-1547/ },
doi = { 10.5120/8187-1547 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:51:23.372224+05:30
%A Chayadevi M.l
%A Raju G.t
%T Data mining, Classification and Clustering with Morphological features of Microbes
%J International Journal of Computer Applications
%@ 0975-8887
%V 52
%N 4
%P 1-5
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The idiosyncrasies of the medical profession makes medical image mining a challenge. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. It is very difficult to determine the exact number of microorganisms under the microscope in the presence of a human expert in conventional methods. An automated tool for fast recognition of microbes is needed to examine the medical data before it expires. Digital image processing is an integral part of microscopy. Automated color image segmentation for bacterial image is proposed to classify the bacteria into two broad categories of gram images. Edge detection algorithm with 8 neighbor-connectivity contour is used. Bacterial morphological, geometric features extracted from microscopy images are used for classification and clustering. The potential and distinguished features are extracted from each bacterial cell. Experimental results with self organizing map shows that the bacterial cluster patterns obtained are better than the statistical approach.

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Index Terms

Computer Science
Information Sciences

Keywords

Data mining Bacterial classification Cluster performance SOM k-means