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JACIII Vol.23 No.6 pp. 1073-1079
doi: 10.20965/jaciii.2019.p1073
(2019)

Paper:

FCMLSM Segmentation of Micro-Vessels in Slight Defocused Microscopic Images

Zhongming Luo*,†, Yu Zhang*, Zixuan Zhou*, Xuan Bi*, Haibin Wu*, and Zhentao Xin**

*The Higher Educational Key Laboratory for Measuring and Control Technology and Instrumentations of Heilongjiang Province,
National Experimental Teaching Demonstration Center of Measuring and Control Technology, Harbin University of Science and Technology
No.52, Xuefu Road, Nangang District, Harbin, Heilongjiang 150080, China

**Department of Industry and Information of Heilongjiang Province
No.68, Heping Road, Nangang District, Harbin, Heilongjiang 150001, China

Corresponding author

Received:
June 4, 2019
Accepted:
July 26, 2019
Published:
November 20, 2019
Keywords:
micro-vessel, image segmentation, FCM clustering algorithm, level-set algorithm
Abstract

To address problems relating to microscopic micro-vessel images of living bodies, including poor vessel continuity, blurry boundaries between vessel edges and tissue and uneven field illuminance, and this paper put forward a fuzzy-clustering level-set segmentation algorithm. By this method, pre-treated micro-vessel images were segmented by the fuzzy c-means (FCM) clustering algorithm to obtain original contours of interesting areas in images. By the evolution equations of the improved level set function, accurate segmentation of microscopic micro-vessel images was realized. This method can effectively solve the problem of manual initialization of contours, avoid the sensitivity to initialization and improve the accuracy of level-set segmentation. The experiment results indicate that compared with traditional micro-vessel image segmentation algorithms, this algorithm is of high efficiency, good noise immunity and accurate image segmentation.

Cite this article as:
Z. Luo, Y. Zhang, Z. Zhou, X. Bi, H. Wu, and Z. Xin, “FCMLSM Segmentation of Micro-Vessels in Slight Defocused Microscopic Images,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.6, pp. 1073-1079, 2019.
Data files:
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Last updated on Jul. 01, 2020