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8 October 2015 Neural cell image segmentation method based on support vector machine
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Proceedings Volume 9675, AOPC 2015: Image Processing and Analysis; 967537 (2015)
Event: Applied Optics and Photonics China (AOPC2015), 2015, Beijing, China
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.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shiwei Niu and Kan Ren "Neural cell image segmentation method based on support vector machine", Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 967537 (8 October 2015);

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