DocumentCode
2354804
Title
Automatic Image Segmentation Algorithm Based on PCNN and Fuzzy Mutual Information
Author
Xiao, Zhiheng ; Shi, Jun ; Chang, Qiang
Author_Institution
Sch. of Commun. & Inf. Eng., Shanghai Univ., Shanghai, China
Volume
1
fYear
2009
fDate
11-14 Oct. 2009
Firstpage
241
Lastpage
245
Abstract
The pulse coupled neural network (PCNN) algorithm has been effectively used in image segmentation. In this paper, we proposed a new image auto-segmentation algorithm based on PCNN and fuzzy mutual information (FMI). The image was firstly segmented by PCNN, and then FMI was used as the optimization criterion to automatically stop the segmentation with the optimal result. Different images were segmented by max-FMI PCNN, Otsu segmentation algorithm and max-entropy PCNN to evaluate the segmentation accuracy. The experimental results demonstrated that the CT and ultrasound images could be well segmented by the proposed algorithm with strong robustness against noise. The results suggest that the proposed algorithm can be used for medical image segmentation.
Keywords
image segmentation; neural nets; optimisation; Otsu segmentation algorithm; automatic image segmentation algorithm; fuzzy mutual information; image auto-segmentation algorithm; max-FMI PCNN; max-entropy PCNN; medical image segmentation; optimization criterion; pulse coupled neural network algorithm; Active contours; Biomedical imaging; Deformable models; Image segmentation; Joining processes; Mutual information; Neural networks; Neurons; Pulse generation; Pulse modulation; PCNN; fuzzy mutual information; image segmentation;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer and Information Technology, 2009. CIT '09. Ninth IEEE International Conference on
Conference_Location
Xiamen
Print_ISBN
978-0-7695-3836-5
Type
conf
DOI
10.1109/CIT.2009.92
Filename
5329529
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