마커 제어 워터셰드와 타원 적합기법을 결합한 다중 교모세포종 분할
Multi-cell Segmentation of Glioblastoma Combining Marker-based Watershed and Elliptic Fitting Method in Fluorescence Microscope Image
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초록
In order to analyze cell images, accurate segmentation of each cell is indispensable. However, the reality is that accurate cell image segmentation is not easy due to various noises, dense cells, and inconsistent shape of cells. Therefore, in this paper, we propose an algorithm that combines marker-based watershed segmentation and ellipse fitting method for glioblastoma cell segmentation. In the proposed algorithm, in order to solve the over-seg- mentation problem of the existing watershed method, the marker-based watershed technique is primarily performed through “seeding using local minima”. In addition, as a second process, the concave point search using ellipse fitting for final segmentation based on the connection line between the concave points has been performed. To evaluate the performance of the proposed algorithm, we compared three algorithms with other algorithms along with the cal- culation of segmentation accuracy, and we applied the algorithm to other cell image data to check the generalization and propose a solution.
Abstract
In order to analyze cell images, accurate segmentation of each cell is indispensable. However, the reality is that accurate cell image segmentation is not easy due to various noises, dense cells, and inconsistent shape of cells. Therefore, in this paper, we propose an algorithm that combines marker-based watershed segmentation and ellipse fitting method for glioblastoma cell segmentation. In the proposed algorithm, in order to solve the over-seg- mentation problem of the existing watershed method, the marker-based watershed technique is primarily performed through “seeding using local minima”. In addition, as a second process, the concave point search using ellipse fitting for final segmentation based on the connection line between the concave points has been performed. To evaluate the performance of the proposed algorithm, we compared three algorithms with other algorithms along with the cal- culation of segmentation accuracy, and we applied the algorithm to other cell image data to check the generalization and propose a solution.
참고문헌 (13)
- 김성곤, "구조적 특징 정보를 이용한 복잡한 세포영상 분할", 한국컴퓨터정보학회논문지, 17(10), pp.35~41 (2012), 한국컴퓨터정보학회
- Meyer F, "Morphological segmentation", Journal of Visual Communication and Image Representation, 1(1), pp.21~46 (1990)
- Luc Vincent, "Watersheds in digital spaces: an efficient algorithm based on immersion simulations", IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(6), pp.583~598 (1991)
- Kass, "Snakes: Active contour models", International journal of computer vision, 1(4), pp.321~331 (1988)
- Allen, SM, "A microscopic theory for antiphase boundary motion and its application to antiphase domain coarsening", Acta metallurgica, 27(6), pp.1085~1095 (1979)
- Yuqian Z, "Watershed Algorithm for Dowels Image Segmentation", World Congress on Intelligent Control and Automation, pp.7644~7648 (2008), IEEE
- Vincent L, "Watersheds in digital spaces : an efficient algorithm based on immersion simulations", IEEE transactions on pattern analysis and machine intelligence, 13(6), pp.583~598 (1991)
- Eddins S, "The watershed transform: strategies for image segmentation", MatLab News & Notes, ~ (2002)
- Aymen Mouelhi, "Hybrid Segmentation of Breast Cancer Cell Images Using a New Fuzzy Active Contour Model and an Enhanced Watershed Method", International Conference on Control, Decision and Information Technologies, 13, pp.382~387 (2013)
- Eddins S, "Ellipse visualization and regionprops", MatLab News & Notes, ~ (2015)
- 은성종, "원형객체의 기하학적 정보를 이용한 영상분할 알고리즘", 인터넷정보학회논문지, 10(6), pp.99~111 (2009), 한국인터넷정보학회
- Beneš, "Geometrical image segmentation by the Allen–Cahn equation", Applied Numerical Mathematics, 51(2), pp.187~205 (2004)
- Bai, "Splitting touching cells based on concave points and ellipse fitting", Pattern recognition, 42(11), pp.2434~2446 (2009)