Application of Machine Intelligence in Digital Pathology: Identification of Falciparum Malaria in Thin Blood Smear Image

Sanjay Nag, Nabanita Basu, Samir Kumar Bandyopadhyay

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

Abstract

Advancement of microscopic imaging has contributed to the development of digitized blood smear images. Application of automated algorithms to analyse these images can help pathologist for a rapid and accurate diagnosis. This chapter proposes a new algorithm that uses a combination of both supervised and unsupervised learning methods as well as rule-based methods, for faster and effective segmentation of parasite-infected cells. Morphological and textural features are extracted for identification of parasite stage using publicly available MaMic dataset. The infected cells are segmented with very high accuracy for better classification results. Single classifiers and ensemble of classifiers were used for best classification outcome. The best overall image-level accuracy of 98.74% was achieved by ensemble classifier.
Original languageEnglish
Title of host publicationAdvancement of Machine Intelligence in Interactive Medical Image Analysis
EditorsOm Prakash Verma, Sudipta Roy, Subhash Chandra Pandey, Mamta Mittal
Place of PublicationSingapore
Chapter4
Pages65-97
Number of pages33
Edition1
ISBN (Electronic)978-981-15-1100-4
DOIs
Publication statusPublished - 12 Dec 2019

Publication series

NameAdvancement of Machine Intelligence in Interactive Medical Image Analysis
ISSN (Print)2524-7565
ISSN (Electronic)2524-7573

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