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Thermal Image Processing Approach to Detect Malaria using Fuzzy Logic

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Amey Walke, Goutam Ghosh, Shashikant Dewangan
10.5120/ijca2016908710

Amey Walke, Goutam Ghosh and Shashikant Dewangan. Article: Thermal Image Processing Approach to Detect Malaria using Fuzzy Logic. International Journal of Computer Applications 137(14):10-14, March 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Amey Walke and Goutam Ghosh and Shashikant Dewangan},
	title = {Article: Thermal Image Processing Approach to Detect Malaria using Fuzzy Logic},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {137},
	number = {14},
	pages = {10-14},
	month = {March},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

The thermal image processing technique for detecting malaria using General Fuzzy Min-Max neural network (GFMM). For detecting malaria, image should go through 4 standard steps, pre-processing, segmentation, feature extraction and selection and classification. Median filter is used in pre-processing step which reduces salt-and-pepper noise of the image. The filtered image is then segmented with the help of Otsu thresholding technique which automatically computes the optimum threshold partitioning the two classes such that spreading is minimal. The features of the segmented part are extracted by Gray Level Co-occurrence Matrix (GLCM), which extracts the infected part of the malaria blood cell. This matrix holds data of gray values of every pixel at its corresponding location. Finally, the GFMM is performed on the extracted data for classification. It performs classification along with clustering, which provides efficient way in recognizing and searching the infected part of the cell.

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Keywords

Image pre-processing, Median filtering, Segmentation, Otsu Thresholding, Feature extraction, GLCM, Classification, General Fuzzy Min-Max neural network.