Estimation of Texture Variation in Malaria Diagnosis

  • A. Vijayalakshmi
  • B. Rajesh Kanna
  • Shanthi Banukumar
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 490)


Malaria parasite has been visually inspected from the Giemsa-stained blood smear image using light microscope. The trained technicians are needed to screen the malaria from the microscope; this manual inspection requires more time. To reduce the problems in manual inspection, nowadays pathologist moves to the digital image visual inspection. The computer-aided microscopic image examination will improve the consistency in detection, and even a semiskilled laboratory technician can be employed for diagnosis. Most of the computer-aided malaria parasite detection consists of four stages namely preprocessing of blood smear images, segmentation of infected erythrocyte, extracting the features, detection of parasite and classification of the parasites. Feature extraction is one of the vital stages to detect and classify the parasite. To carry out feature extraction, geometric, color, and texture-based features are extracted for identifying the infected erythrocyte. Among these clause of features, texture might be considered as a very fine feature, and it provides the characteristics of smoothness over the region of interest using the spatial distribution of intensity. The proposed work demonstrates the merit of the texture feature in digital pathology which is prone to vary with respect to change in image brightness. In microscope, brightness of the image could be altered by iris aperture diameter and illumination intensity control knob. However, the existing literature failed to mention the details about these illumination controlling parameters. So the obtained texture feature may not be considered as distinct feature. In this paper, we conducted an experiment to bring out the deviation of texture feature values by changing the brightness of the acquired image by varying the intensity control knob.


Malaria diagnosis Image brightness Gray-level co-occurrence matrix Digital pathology Digital microscopy 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • A. Vijayalakshmi
    • 1
  • B. Rajesh Kanna
    • 1
  • Shanthi Banukumar
    • 2
  1. 1.School of Computing Science and EngineeringVIT UniversityChennaiIndia
  2. 2.Department of MicrobiologyTagore Medical College & HospitalChennaiIndia

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