Leukocyte image segmentation using simulated visual attention

2012
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. (C) 2012 Elsevier Ltd. All rights
EXPERT SYSTEMS WITH APPLICATIONS
页码:7479-7494|卷号:39|期号:8
ISSN:0957-4174
收录类型
SSCI
发表日期
2012
学科领域
循证管理学
国家
中国
语种
英语
DOI
10.1016/j.eswa.2012.01.114
其他关键词
ALGORITHM; BLOOD
EISSN
1873-6793
资助机构
Ministry of Education, Science Technology (MEST)Ministry of Education, Science and Technology, Republic of Korea; National Research Foundation of Korea (NRF)National Research Foundation of Korea; Natural Science Foundation of Zhejiang Province of ChinaNatural Science Foundation of Zhejiang Province [Y1091039]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61063035]
资助信息
This research was financially supported by the Ministry of Education, Science Technology (MEST) and National Research Foundation of Korea (NRF) through the Human Resource Training Project for Regional Innovation and second stage of Brain Korea 21, partly supported by the Natural Science Foundation of Zhejiang Province of China (No. Y1091039) and the National Natural Science Foundation of China (No. 61063035).
被引频次(WOS)
34
被引更新日期
2022-01
来源机构
China Jiliang University Jeonbuk National University Mokpo National University Jiangxi University of Finance & Economics
关键词
Image segmentation Visual attention Machine learning Leukocyte image SVM