Lung Cancer Segmentation in CT Images Using Fuzzy-C Means Clustering and Artificial Bee Colony Algorithm
J.Maruthi Nagendra Prasad1, M.Vamsi Krishna2

1J Maruthi Nagendra Prasad, Research Scholar, Department of Computer Science & Engineering , Centurion University Of Technology and Management, Paralakhemundi, Orissa
2Dr.M,Vamsi Krishna, Professor, Department of Computer Science and Engineering, Chaitanya Engineering College, Kakinada

Manuscript received on 02 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 1737-1739 | Volume-8 Issue-10, August 2019 | Retrieval Number: J90880881019/2019©BEIESP | DOI: 10.35940/ijitee.J9088.0881019
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Abstract: One of the challenging issues to most of the researches is to segment pulmonary nodules from the CT Lung images. This Research focus on rapid segmentation of pulmonary nodules from the CT Lung images based on Fuzzy-C Means Clustering and Artificial Bee Colony Algorithm. Classic 2D otsu algorithm is used for segmentation and Artificial Bee colony algorithm is used for finding optimum threshold values. Finally, FCM (Fuzzy-C Means) clustering is used over the CT segmented images to cluster the images.
Keywords: CT Lung Images, Segmentation, ABC Algorithm, FCM Clustering.
Scope of the Article: Fuzzy Logics