Self-Organizing Map based Extended Fuzzy C-Means (SEEFC) algorithm for image segmentation

摘要

A Novel hybrid algorithm based on Self-Organizing-Map (SOM) and Extended Fuzzy C-Means (EFCM) named self-organizing-map based extended fuzzy c-means (SEEFC) has been designed and implemented for image segmentation. The proposed algorithm works in three stages. At first, the images are decomposed by discrete wavelet transform (DWT) into various frequencies and gradient, pixel value and statistical parameters are obtained to form a feature prototype. The feature prototypes are input to the Self Organizing Map (SOM). Finally, the codebook vectors of the trained SOM have been clustered automatically using Extended Fuzzy C-Means (EFCM). The clustered codebook vector centers are used for image segmentation based on minimum distance criterion. The proposed method has been tested with images from Berkeley's database and results obtained are promising. The segmentation results are evaluated against the ground truth. Comparisons with another state of the art approaches indicate advantages of the SOM based EFCM algorithm. (C) 2017 Elsevier B.V. All rights reserved.