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Manivannan, M.
- Lung Cancer Image Segmentation Using Rough Set Theory
Authors
1 School of Computing Science, Vels University, Chennai, IN
Source
Indian Journal of Medicine and Healthcare, Vol 4, No 6 (2015), Pagination: 1-8Abstract
Background/Objectives: Lung cancer seems to be the common cause of death among people through the world. Early detection of lung cancer can increase the chance of survival among people. An attempt is made to segment the CT image of lung cancer using Rough K-Means clustering, which is one of the most important unsupervised learning methods in machine learning.
Methods/Statistical analysis: The necessary CT images are collected from Mitra Scan centre, Salem for this study. The proposed method is compared with the bench mark K-means algorithm in order to achieve the efficiency. Findings: the performance of proposed Rough Set technique is compared with existing Clustering (k means) work which shows its efficiency level of segmented image portion and the prediction rate is better than its counterpart.
Improvements/Applications: The proposed technique predicts the early symptoms of the disease with segmented region of image matched to the similar patterns of diseased portions of trained patient images.
Keywords
Computed Tomography (CT) Image, Segmentation, Rough K-Means, Clustering.- Electrochemical Decolourisation of Reactive Dye Effluent Solution
Authors
Source
International Journal of Innovative Research and Development, Vol 2, No 4 (2013), Pagination: 992-1001Abstract
To evaluate the decolourisation efficiency of metals, platinised titanium, mild steel, aluminium, stainless steel and copper were used as anodes for decolourization of dye effluent solution. Declorization efficiency was calculated before decolorization and after decolorization. The result of this investigation study reveal that the % of decolorization efficiency of various anodes are in the following decreasing order.platinised titanium>copper>aluminium