Leukocyte image segmentation using simulated visual attention

Authors:
Chen Pan;Dong Sun Park;Sook Yoon;Ju Cheng Yang
Affiliations:
College of Information Engineering, China Jiliang University, Hangzhou, Zhejiang 310018, China and Division of Electronics and Information Engineering, Chonbuk National University, Jeonju, Jeonbuk ...;Division of Electronics and Information Engineering, Chonbuk National University, Jeonju, Jeonbuk 561-756, South Korea;Department of Multimedia Engineering, Mokpo National University, Jeonnam, South Korea;School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, Jiangxi 330013, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

Computer-aided automatic analysis of microscopic leukocyte is a powerful diagnostic tool in biomedical fields which could reduce the effects of human error, improve the diagnosis accuracy, save manpower and time. However, it is a challenging to segment entire leukocyte populations due to the changing features extracted in the leukocyte image, and this task remains an unsolved issue in blood cell image segmentation. This paper presents an efficient strategy to construct a segmentation model for any leukocyte image using simulated visual attention via learning by on-line sampling. In the sampling stage, two types of visual attention, ''bottom-up'' and ''top-down'' together with the movement of the human eye are simulated. We focus on a few regions of interesting and sample high gradient pixels to group training sets. While in the learning stage, the SVM (support vector machine) model is trained in real-time to simulate the visual neuronal system and then classifies pixels and extracts leukocytes from the image. Experimental results show that the proposed method has better performance compared to the marker controlled watershed algorithms with manual intervention and thresholding-based methods.