Random Articles
Reseach Article
Data mining, Classification and Clustering with Morphological features of Microbes
| 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
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.
References
- Shillabeer, A. and Roddick J, Establishing a Lineage for medical Knowledge Discovery. ACM International Conference Proceeding Series. 70, 29-37, 2007.
- Riccardo Bellazzi, Fulvia Ferrazzi, Lucia Sacchi, Predictive data mining in clinical medicine: a focus on selected methods and applications,wiley Interdisciplinary reviews: Data mining and Knowledge discovery, vol1, 2011
- G. T. Raju, Chayadevi M. L, "Data Mining in Bioinformatics and its Applications–a Survey", ICDECS2011, ISBN 978-93-81583-17-3, dec 2011.
- Khalid Raza, Application of Data mining in Bioinformatics,Indian Journal of Computer Science and Engineering Vol 1, No 2, 114-118, 2010.
- Massana R,Gasol JM, Measurements of bacterial size via image analysis of spifluorescene preparationscientia Martina 61,397-407,1997.
- P. S. Hiremath and Parashuram B, Automatic Identification and classification of Bacilli Bacterial cell growth phases, IJCA RTIPPR, 2010.
- Riries Rulaningtya, Adriyan B. Suksmono, Tati , Automatic Classification of Tuberculosis Bacteria using Neural n/w, International Conference on Electrical Engineering and informatics ,2011.
- S. Prabhakar, K. Porkumaran, J. Samson Isaac, Development of Image Processing scheme for bacterial Classification based on Optimal discriminant feature, IEEE,978-1-4244-4713, 2010.
- Yasuhito Abe, Teiri Sagawa, Ken Sakai and Shigeru Kimura. Enzyme-linked immunosorbent assay (ELISA) for human epidermal growth factor (hEGF). Clinica Chimica Acta, 168: 87-95, 1987.
- Nakul Soni, Chirag Gandhi, Application of Data Mining to Health Care, International Journal of Computer Science and its Applications, 2010.
- Yuanyuan Shen, Zhe Liu, Jurg Ott, Support vector machines with L1 penalty for detecting gene-gene interactions, International journal of Data Mining and Bioinformatics, 2011.
- Woolf P. J. , Wang Y. (2000), "A fuzzy logic approach to analyzing gene expression data", Physiological Genomics, vol3(1), 9-15, 2000.
- Chayadevi M. L, G. T. Raju, Extraction of Bacterial Clusters from Digital Microscopic Images through Statistical and Neural Network Approaches", ICADC 2012, pp 1091-1099.
- Jiawei Han, Micheline kamber Kian Pei, Data Mining: Concepts and Technquies.
Index Terms
Keywords