An Effective Automatic Detection of Lung Tumor Based on Novel Optimized Chan-Vese Algorithm
Lim J Seelan1, L. Padma Suresh2
1Lim J Seelan, Research Scholar, Department of EEE, Noorul Islam University, Thuckalay (Tamil Nadu), India.
2Dr. L. Padma Suresh, Principal, Baselios Mathews II College of Engineering, Sasthamkotta (Kerala), India.
Manuscript received on 25 March 2019 | Revised Manuscript received on 04 April 2019 | Manuscript Published on 27 April 2019 | PP: 400-405 | Volume-7 Issue-6S2 April 2019 | Retrieval Number: F9080047619/2019©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: One of the severe health hazards is lung cancer. United States alone bears approximately 25% lung cancer burden. This type of cancer is cured if it is detected in an earlier stage and reduces mortality rate. With the rapid rising of lung cancer patients, the CAD (Computer Aided Detection) method plays a significant role in the field of automatic recognition for medical images. This method focused on automatic identification of lung nodule using optimized chan-vese algorithms. The computer automatic system consist of following steps: – image acquisition, image preprocessing, and image segmentation. This method is mainly helpful for automated finding of lung nodules that are appended to the chest wall. The final output shows the application of the proposed method in the medical field will bring great progress for medical development.
Keywords: Histogram Equalization, Curvelet Transform, Adaptive Concave Hull, Optimized Chan-Vese.
Scope of the Article: Algorithm Engineering