Quantitative And Qualitative Analysis For Lung Nodule Segmentation

International Journal of Electronics and Communication Engineering

© 2019 by SSRG - IJECE Journal

Volume 6 Issue 5

Year of Publication : 2019

Authors : Rabiya Banu .A , Kannan.R

10.14445/23488549/IJECE-V6I5P104

How to Cite?

Rabiya Banu .A , Kannan.R, "Quantitative And Qualitative Analysis For Lung Nodule Segmentation," SSRG International Journal of Electronics and Communication Engineering, vol. 6,  no. 5, pp. 16-21, 2019. Crossref, https://doi.org/10.14445/23488549/IJECE-V6I5P104

Abstract:

Prevention of lung cancer is the toughest problem due to cancer cells’ structure, where most of the cells are combined. The image processing techniques are mostly used for the prediction of lung cancer and also for early detection and treatment to prevent lung cancer. Various features are extracted from the 2D images; therefore, pattern recognition based approaches are useful for predicting lung cancer. 2D Image processing techniques are used in most medical areas for image improvement and analysis in earlier detection and treatment stages. The time factor is very important to discover the abnormality points in target images, especially in various cancer nodules such as lung cancer, breast cancer. Image quality assessment and efficiency are the main factors of this research; image quality assessment, as well as improvement, are depending on the enhancement stage where low pre-processing techniques are used based on the filter. Following the Output segmentation principles, an enhanced region of the object of interest used as a basic foundation of feature extraction is obtained. Relying on general features, a normality comparison is made. A comprehensive and comparative review for lung cancer prediction by a previous researcher using image processing techniques is presented. The objection for the prediction of lung cancer by a previous researcher using image processing techniques is also presented

Keywords:

Image processing, Segmentation, lung cancer, feature extraction.

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