An efficient de noising based clustering algorithm for detecting dead centers and removal of noise in digital images

Clustering algorithms are used for segmenting Digital images however noise are introduced into images during image acquisition, due to switching, sensor temperature. They may also occur due to interference in the channel and due to atmospheric disturbances during image transmission and affecting the segmentation results Noise reduction is a pulmonary step prior to feature extraction attempts from digital images. In order to overcome this drawback, this paper presents a new clustering based segmentation technique that can be used in segmenting noise in Digital images. We named this approach as De noising based Optimized K-means clustering algorithm (DOKM).where De noising is fully data driven approach. The qualitative and quantitative analyses have been performed to investigate the robustness of the OKM algorithm. And this new approach is effective to avoid dead centre and trapped centre in segmented Digital Images.