Fuzzy C-means with a local membership kl distance for medical image segmentation
| Autor: | G. Gendy, R. R. Gharieb |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Kullback–Leibler divergence
business.industry Fuzzy set ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Image segmentation Fuzzy logic Information distance ComputingMethodologies_PATTERNRECOGNITION Computer Science::Computer Vision and Pattern Recognition Piecewise Artificial intelligence business Cluster analysis Membership function Mathematics |
| Zdroj: | 2014 Cairo International Biomedical Engineering Conference (CIBEC). |
| Popis: | This paper presents a new technique for incorporating local membership information into the standard fuzzy C-means (FCM) clustering algorithm. In this technique, the objective consists of minimizing the classical FCM function with a unity fuzzifier exponent plus the Kullback-Leibler (KL) information distance acting as a fuzzification and regularization term. The KL distance is proposed to measure the proximity between cluster membership function of a pixel and an average of the cluster membership functions of immediate neighborhood pixels. Therefore, minimizing this KL distance biases the cluster membership of the pixel toward this smoothed membership function of the local neighborhoods. This can provide immunity against noise and results in clustered images with piecewise homogeneous regions. Results of clustering and segmentation of synthetic and real-world medical images are presented to compare the performance of the proposed local membership KL information based FCM (LMKLFCM) and the standard FCM, a local data information based FCM (LDFCM) and a type of local membership information based FCM (LMFCM) algorithms. |
| Databáze: | OpenAIRE |
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