Automated Colour Segmentation of Malaria Parasite with Fuzzy and Fractal Methods

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 33)

Abstract

Malaria is an endemic, global and life threatening disease. Technically skilled person or an expert is needed to analyze the microscopic blood smears for long hours. This paper presents an efficient approach to segment the parasites of malaria. Three different colour space namely LAB, HSI and gray has been used effectively to pre-process and segment the parasite in digital images with noise, debris and stain. L and B plane of LAB, S plane of HSI of input image is extracted with convolution and DCT. Fuzzy based segmentation has been proposed to segment the malaria parasite. Colour features, fractal features are extracted and feature vectors are prepared as a result of segmentation. Adaptive Resonance Theory Neural Network (ARTNN), Back Propagation Network (BPN) and SVM classifiers are used with Fuzzy segmentation and fractal feature extraction methods. Automated segmentation with ARTNN has recorded an accuracy of 95 % compared to other classifiers.

Keywords

Fuzzy segmentation Fractal features ARTNN BPN SVM 

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Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.JSSATEBangaloreIndia
  2. 2.RNSITBangaloreIndia

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