Hybrid Image Segmentation Model using KM, FCM, Wavelet KM and Wavelet FCM Techniques - Barbhuiya, A.H.M. Jaffar Iqbal; Hemachandran, K (International Journal of Computer Sciences and Engineering, 2018)
An attempt has been made to study the DWT (Discrete Wavelet Transform) based K-Means (KM) clustering and DWT based Fuzzy C-Means (FCM) clustering methods for the segmentation of digital images. The segmentation results of Wavelet KM clustering and Wavelet FCM clustering are compared with the conventional KM clustering and FCM clustering techniques used for the segmentation. The images are split-up into identical areas using KM, FCM, wavelet KM and wavelet FCM algorithms. The algorithms are tested on different image formats available in the literature. The proposed methods are analyzed using discrete wavelet transform (DWT) for enhancing the digital images and various image features like regions, colors and shapes are considered to validate the proposed work. The segmentation results exhibit that the objects in various image clusters of wavelet KM and wavelet FCM performs better as compared to traditional KM and FCM clustering algorithm with respect to CPU execution time, sensitivity analysis, segmentation accuracy and PSNR(Peak Signal to Noise Ratio).