Automatic Detection of Malaria Infected Erythrocytes Based on the Concavity Point Identification and Pseudo-Valley Based Thresholding

Abstract

Automatic malaria diagnosis requires the segmentation of the erythrocytes from the microscopic blood smears images. In this paper, the system for the analysis of the erythrocyte to detect the malaria infection has been proposed. The challenging issues are: preprocessing, foreground extraction, clump erythrocyte segmentation and infected erythrocyte detection. The non-uniform illumination and other distortions of the acquired microscopic images are corrected by using median filtering technique. The foreground regions, i.e. erythrocyte regions are segmented from the other blood components in the smears by using Otsu’s thresholding along with morphological filtering. The clump erythrocytes are segmented based on the inherent geometrical property of the concavity point detection of the clump which is independent of any geometrical deformation or image acquisition errors. Further, the segmented erythrocytes are analysed to detect the infected erythrocytes using thresholding technique based on the “pseudo-valley” concept. The experimental analyses of the proposed method are carried out on three different sets of database. An accuracy of 97.95% was observed for the erythrocyte segmentation, with an improvement in accuracy of 8.76% and 0.87% as compared to classical watershed and marker controlled watershed segmentation technique respectively. Moreover, the infected erythrocyte was detected with an accuracy of 88.57%.

KEYWORDS:

• Clump splitting
• Concavity point
• Convex hull
• Erythrocyte
• Malaria detection
• Pseudo-valley
• Segmentation

Acknowledgement

The authors acknowledge the Speech and Image Processing Lab under Department of ECE at National Institute of Technology, Silchar, India, for providing all necessary facilities to carry out the research work.

Additional information

Notes on contributors

Manish Sharma

Manish Sharma received his BTech degree in 2013 from NERIST, Arunachal Pradesh. He completed MTech in the Department of Electronics and Communication Engineering at National Institute of Technology Silchar. He is currently working as senior research fellow in National Institute of Technology Silchar. His research interests include biomedical image processing, pattern recognition, machine learning. E-mail: [email protected]

Salam Shuleenda Devi

Salam Shuleenda Devi has completed her PhD from National Institute of Technology, Silchar, India, and MTech from KIIT University, Odisha. She received her BE degree in 2011 from Anna University Coimbatore. Her research interests include biomedical image processing, machine learning and pattern recognition. E-mail: [email protected]

Rabul Hussain Laskar

Rabul Hussain Laskar completed his PhD, from National Institute of Technology, Silchar, India, and his MTech from Indian Institute of Technology, Guwahati. He is currently working as associate professor in the Department of Electronics and Communication Engineering at NIT Silchar. His major research interests are in speech processing, image processing, digital signal processing.