Open Access

ARTICLE

A Morphological Image Segmentation Algorithm for Circular Overlapping Cells

Fuchu Zhang¹, Yanpeng Wu^2,*, Miaoqing Xu², Sanjun Liu³, Changling Peng², Zhichen Gao⁴

1 Department of Information Engineering, Shaoyang University, Shaoyang, 422000, China
2 Department of Information Science and Engineering, Hunan First Normal University, Changsha, 410205, China
3 School of Resource Environment and Safety Engineering, South China University, Hengyang, 421001, China
4 Department of Applied Mathematics and Statistics, College of Engineering and Applied Sciences, Stony Brook University, NY, 11794, USA

* Corresponding Author: Yanpeng Wu. Email:

Intelligent Automation & Soft Computing 2022, 32(1), 301-321. https://doi.org/10.32604/iasc.2022.021929

Received 20 July 2021; Accepted 21 August 2021; Issue published 26 October 2021

Abstract

Cell segmentation is an important topic in medicine. A cell image segmentation algorithm based on morphology is proposed. First, some morphological operations, including top-hat transformation, bot-hat transformation, erosion operation, dilation operation, opening operation, closing operation, majority operation, skeleton operation, etc., are applied to remove noise or enhance cell images. Then the small blocks in the cell image are deleted as noise, the medium blocks are removed and saved as normal cells, and the large blocks are segmented as overlapping cells. Each point on the edge of the overlapping cell area to be divided is careful checked. If the shape of the surrounding area is a corner and its angle is smaller than the specified value, the overlapping cell will be divided along the midline of the corner. The length of each division is about a quarter of the diameter of a normal cell. Then small blocks are deleted, and medium blocks are removed and saved, after the edges of all blocks are smoothed. This step is repeated until no dividing point is found. The last remaining image, plus the saved blocks, is the final segmentation result of the cell image. The experimental results show that this algorithm has high segmentation accuracy for lightly or moderately overlapping cells.

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Keywords

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