Digital images assume a vital part both in day by day life applications, for example, satellite TV, computed tomography, magnetic resonance imaging and additionally in ranges of research and innovation, for example, cosmology and geographical information systems. An expansive segment of computerized image preparing incorporates image restoration. Image restoration is a technique for removal or decrease of corruption that are caused amid the image catching. Corruption originates from obscuring as well as commotion due to the electronic and photometric sources. Obscuring is the type of data transfer capacity decrease of images caused by flawed image development process, for example, relative movement amongst camera and unique scene or by an optical framework that is out of core interest. Image Denoising is an important pre-processing task before further processing of the image like segmentation, feature extraction, texture analysis, etc. which removes the noise while retaining the edges and other detailed features as much as possible. This noise gets introduced during acquisition, transmission & reception and storage & retrieval processes. This paper presents a novel pre-processing algorithm which is named as Profuse Clustering Technique (PCT) based on the super pixel clustering. K-Means clustering, Simple Linear Iterative Clustering, Fusing Optimization algorithms are involved in this proposed Profuse Clustering Technique and is further used for denoising the Lung Cancer images to get the more accurate result in the decision making process.
Thamilselvan, P., & Sathiaseelan, J. G. R. (2018). A Novel Profuse Clustering Technique for Image Denoising. In Procedia Computer Science (Vol. 125, pp. 132–142). Elsevier B.V. https://doi.org/10.1016/j.procs.2017.12.019