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      Computational microscopic imaging for malaria parasite detection: a systematic review.

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          Abstract

          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.

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          Author and article information

          Journal
          J Microsc
          Journal of microscopy
          Wiley-Blackwell
          1365-2818
          0022-2720
          Oct 2015
          : 260
          : 1
          Affiliations
          [1 ] School of Medical Science & Technology, IIT Kharagpur, India.
          [2 ] Department of Electrical Engineering, IIT Kharagpur, India.
          Article
          10.1111/jmi.12270
          26047029

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