Optik 145 (2017) 225–239 Contents lists available at ScienceDirect Optik j ourna l ho me pa ge: www.elsevier.de/ijleo Original research article Quantitation of Malarial parasitemia in Giemsa stained thin blood smears using Six Sigma threshold as preprocessor Srinivasan Sankaran a,1 , Muthukumaran Malarvel b,1 , Gopalakrishnan Sethumadhavan b,∗ , Dinkar Sahal c a HCL Technologies, Chennai, Tamil Nadu, India b School of Computing, SASTRA University, Thanjavur, Tamil Nadu, India c International Centre for Genetic Engineering and Biotechnology, New Delhi, India a r t i c l e i n f o Article history: Received 6 February 2017 Received in revised form 27 May 2017 Accepted 16 July 2017 Keywords: Malaria detection Six-Sigma threshold Hough transform Otsu threshold Kapur threshold Kapur entropy a b s t r a c t Malaria is a precarious disease and a serious illness that can become life threatening very fast and can be incurable if not treated on time. According to the World Health Organi- zation, Malaria causes 150–250 million infections and an estimated more than a million deaths each year. Microscopic image capture followed by visual observation as per the manual is the gold standard method for diagnosis of malaria. However, due to subjectivity and the complexity of manual assessment, microscopic diagnosis of malaria is tiring, time consuming and subject to human error. In this study, we have developed a novel automatic high performance method with minimal human dependence for detection and quantitation of malaria infected Red Blood Cells (RBC). The parasites detection test to identify malaria was computed using digital image processing techniques. This automated quantitation pro- cedure was done in three parts: Segmentation, Identification and Detection. The methods used for the quantitation were Six Sigma threshold for segmenting Region of Interest fol- lowed by modified Hough transform to identify and count RBCs, and Kapur’s threshold method to detect malaria parasite infected RBCs. The developed package using Microsoft ® VB.NET 2008 framework is executed on various Giemsa stained thin blood smears digi- tally acquired using a charge coupled device attached to a microscope. Following analysis performed on seven sets of thin blood smear images, the values of Precision, Recall and F- measures obtained were 96%, 97% and 97% respectively. This is the first attempt to use a combination of Six Sigma threshold, Chess-Board distance, Hough transform and Kapur’s threshold to find the RBCs by deriving information from the image itself. Further, Kapur’s entropy measure was successfully applied to distinguish parasitized from un-infected cells. © 2017 Elsevier GmbH. All rights reserved. ∗ Corresponding author. E-mail addresses: srinivasans@hcl.com (S. Sankaran), mmkmtech@gmail.com (M. Malarvel), sgk@mca.sastra.edu, headca@sastra.edu (G. Sethumadha- van), dinkar@icgeb.res.in (D. Sahal). 1 Equal contributors. http://dx.doi.org/10.1016/j.ijleo.2017.07.047 0030-4026/© 2017 Elsevier GmbH. All rights reserved.