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Automatic Detection of Malaria Infected RBCs from a Focus Stack of Bright Field Microscope Slide Images

Publication Type : Conference Proceedings

Publisher : Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing

Source : Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing, ACM, New York, NY, USA (2016)

Url : http://doi.acm.org/10.1145/3009977.3010024

ISBN : 9781450347532

Keywords : CNN, Malaria diagnosis, Plasmodium falciparum

Campus : Amritapuri

School : Department of Computer Science and Engineering, School of Computing, School of Engineering

Center : Computer Vision and Robotics, Research & Projects

Department : Computer Science

Verified : No

Year : 2016

Abstract : Malaria is a deadly infectious disease affecting red blood cells in humans due to the protozoan of type Plasmodium. In 2015, there is an estimated death toll of 438, 000 patients out of the total 214 million malaria cases reported world-wide. Thus, building an accurate automatic system for detecting the malarial cases is beneficial and has huge medical value. This paper addresses the detection of Plasmodium Falciparum infected RBCs from Leishman's stained microscope slide images. Unlike the traditional way of examining a single focused image to detect the parasite, we make use of a focus stack of images collected using a bright field microscope. Rather than the conventional way of extracting the specific features we opt for using Convolutional Neural Network that can directly operate on images bypassing the need for hand-engineered features. We work with image patches at the suspected parasite location there by avoiding the need for cell segmentation. We experiment, report and compare the detection rate received when only a single focused image is used and when operated on the focus stack of images. Altogether the proposed novel approach results in highly accurate malaria detection.

Cite this Research Publication : Gopakumar G, Swetha, M., Siva, G. Sai, and Subrahmanyam, G. R. K. S., “Automatic Detection of Malaria Infected RBCs from a Focus Stack of Bright Field Microscope Slide Images”, Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing. ACM, New York, NY, USA, 2016

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