A dataset for automatic contrast enhancement of microscopic malaria infected blood RGB images
J. Somasekar,
G. Ramesh,
Gandikota Ramu,
P. Dileep Kumar Reddy,
B. Eswara Reddy,
Ching-Hao Lai
Affiliations
J. Somasekar
Department of Computer Science and Engineering, Gopalan College of Engineering and Management, Whitefield, Bangalore, Karnataka, 560 048, India; Corresponding author.
G. Ramesh
Department of CSE, GRIET, Bachupally, Hyderabad, Telangana, 500 090, India
Gandikota Ramu
Department of CSE, Institute of Aeronautical Engineering, Hyderabad, Telangana, 500043, India
P. Dileep Kumar Reddy
Department of CSE, Sri Venkateswara College of Engineering, Karakambadi Road, Tirupati, 517 507, India
B. Eswara Reddy
Department of CSE, JNTUA College of Engineering Ananthapuramu, Anantapuramu, Andhra Pradesh, 515 002, India
Ching-Hao Lai
Computational Intelligence Technology Center, Industrial Technology Research Institute, Chung Hsing Rd., Chutung, Hsinchu, 310 40, Taiwan
In this article we introduce a malaria infected microscopic images dataset for contrast enhancement which assist for malaria diagnosis more accurately. The dataset contains around two hundred malaria infected, normal, species and various stages of microscopic blood images. We propose and experimentally demonstrate a contrast enhancement technique for this dataset. This simple technique increases the contrast of an image and hence, reveals significant information about malaria infected cells. Experiments on the dataset show the superior performance of our proposed method for contrast enhancement of malaria microscopic imaging. Keywords: Malaria diagnosis, Low contrast images, Histogram equalization, Microscopic RGB blood images, Contrast enhancement
