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
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;
Conference_Titel :
Computer and Information Technology, 2009. CIT '09. Ninth IEEE International Conference on
Conference_Location :
Xiamen
Print_ISBN :
978-0-7695-3836-5
DOI :
10.1109/CIT.2009.92