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Proceedings Paper

Neural cell image segmentation method based on support vector machine
Author(s): Shiwei Niu; Kan Ren
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Paper Abstract

In the analysis of neural cell images gained by optical microscope, accurate and rapid segmentation is the foundation of nerve cell detection system. In this paper, a modified image segmentation method based on Support Vector Machine (SVM) is proposed to reduce the adverse impact caused by low contrast ratio between objects and background, adherent and clustered cells’ interference etc. Firstly, Morphological Filtering and OTSU Method are applied to preprocess images for extracting the neural cells roughly. Secondly, the Stellate Vector, Circularity and Histogram of Oriented Gradient (HOG) features are computed to train SVM model. Finally, the incremental learning SVM classifier is used to classify the preprocessed images, and the initial recognition areas identified by the SVM classifier are added to the library as the positive samples for training SVM model. Experiment results show that the proposed algorithm can achieve much better segmented results than the classic segmentation algorithms.

Paper Details

Date Published: 8 October 2015
PDF: 9 pages
Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 967537 (8 October 2015); doi: 10.1117/12.2205114
Show Author Affiliations
Shiwei Niu, Nanjing Univ. of Science and Technology (China)
Kan Ren, Nanjing Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 9675:
AOPC 2015: Image Processing and Analysis
Chunhua Shen; Weiping Yang; Honghai Liu, Editor(s)

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