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Computational microscopic imaging for malaria parasite detection: a systematic review
Author(s) -
DAS D.K.,
MUKHERJEE R.,
CHAKRABORTY C.
Publication year - 2015
Publication title -
journal of microscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.569
H-Index - 111
eISSN - 1365-2818
pISSN - 0022-2720
DOI - 10.1111/jmi.12270
Subject(s) - malaria , diagnosis of malaria , computer science , segmentation , artificial intelligence , blood smear , gold standard (test) , pattern recognition (psychology) , feature (linguistics) , image segmentation , pathology , computer vision , plasmodium falciparum , medicine , radiology , linguistics , philosophy
Summary Malaria, being an epidemic disease, demands its rapid and accurate diagnosis for proper intervention. Microscopic image‐based characterization of erythrocytes plays an integral role in screening of malaria parasites. In practice, microscopic evaluation of blood smear image is the gold standard for malaria diagnosis; where the pathologist visually examines the stained slide under the light microscope. This visual inspection is subjective, error‐prone and time consuming. In order to address such issues, computational microscopic imaging methods have been given importance in recent times in the field of digital pathology. Recently, such quantitative microscopic techniques have rapidly evolved for abnormal erythrocyte detection, segmentation and semi/fully automated classification by minimizing such diagnostic errors for computerized malaria detection. The aim of this paper is to present a review on enhancement, segmentation, microscopic feature extraction and computer‐aided classification for malaria parasite detection.