Lung Image Segmentation using Modified K-Means Algorithm
Naveen Raj M1, Aiswariya Lakshmi A2, Edlin Shejila E3, Kausalya K4, Vinitha R5
1Naveen Raj M, Assistant Professor, Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore
2Aiswariya Lakshmi A, Student, Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore (TamilNadu), India.
3Edlin Shejila E, Student, Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore (TamilNadu), India.
4Kausalya K, Student, Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore (TamilNadu), India.
5Vinitha R, Student, Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore (TamilNadu), India.
Manuscript received on 05 March 2019 | Revised Manuscript received on 17 March 2019 | Manuscript Published on 22 March 2019 | PP: 432-434 | Volume-8 Issue-5S April 2019 | Retrieval Number: ES3459018319/19©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Lung Cancer is also referred as Lung Carcinoma, characterized by unrestrained cell growth in tissues of lung. It has high mortality rate when compared to other cancers. The main reason of Lung Cancer is smoking and exposure to secondhand smoke. A fine Lung Cancer detection system must sense the Lung Cancer in its premature stages. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are the two tools used to capture the Lung image. The various stages in the Lung Cancer detection include Image Capturing, Image Enhancement, Image Segmentation and Feature Extraction. In this, various image processing techniques are utilized for lung cancer detection and performance of each technique is compared.
Keywords: CT, MRI, K-Means Clustering.
Scope of the Article: Communication