An Efficient De noising Based Clustering Algorithm for Detecting Dead Centers and Removal of Noise in Digital Images
Lakshmana Phaneendra Maguluri1, Naga Srinivasu Parvathanni2, Ravi Kiran Karri3
1Lakshmana Phaneendra Maguluri, M.Tech, Department of Information Technology, GITAM Institute of Technology, GITAM University, (Andhra Pradesh), India.
2Nagasrinivasuparvathanni, M.Tech, Department of Computer Science Technology, GITAM Institute of Technology, GITAM University, (Andhra Pradesh), India.
3Ravi Kiran Karri, M.Tech, Department of Information Technology, GITAM Institute of Technology, GITAM University, Vishakhapatnam (Andhra Pradesh), India.
Manuscript received on 15 April 2013 | Revised Manuscript received on 22 April 2013 | Manuscript Published on 30 April 2013 | PP: 48-53 | Volume-2 Issue-5, April 2013 | Retrieval Number: E0642032413/13©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: As of now, several improvements have been carried out to increase the performance of previous conventional clustering algorithms for image segmentation. However, most of them tend to have met with unsatisfactory results. In order to overcome some of the drawback like dead centers and trapped centers, in this article presents a new clustering-based segmentation technique that may be able to overcome some of the drawbacks we are passing with conventional clustering algorithms. 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 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.
Keywords: Limitations of Conventional Clustering Algorithms, Dead Center Problem, Salt-And-Pepper Noise, Image Segmentation.
Scope of the Article: Clustering