An Efficient Segmentation Method for Juxtapleural Parenchyma with Modified Fractal Geometry-Based Pulmonary Boundary Detection
S.Vaishali1, G.V.Subba Rao2, K.Kishan Rao3
1S.Vaishali*, Research Scholar, Department of ECE, KLEF, Vijayawada, Andhra Pradesh, India.
2Dr.G.V.Subba Rao Professor, Department of ECE, KLEF, Vijayawada, Andhra Pradesh, India
3Dr.K.Kishan Rao, Professor, Department of ECE, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India.
Manuscript received on November 16, 2019. | Revised Manuscript received on 26 November, 2019. | Manuscript published on December 10, 2019. | PP: 968-975 | Volume-9 Issue-2, December 2019. | Retrieval Number: I8218078919/2019©BEIESP | DOI: 10.35940/ijitee.I8218.129219
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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: Lung cancer seems to be the world has ever known the most leading cause of death from cancer. Early diagnosis of lung cancer will help patients significantly. The exactness of tumour form, location, and volume detection wants to play an important role to play in effective tumour treatment. Artefacts can severely deteriorate the performance or even clinically obsolete of computed tomographic (CT) images. An automatic approach to incorporate the adaptive threshold iteration technique with the segmentation of images of the lungs. The implementation involves an enhanced convex hull repair to undertake a precise lung parenchyma segmentation. The results of the analysis say with certainty that the current approach can precisely segment juxtapleural parenchymal images and that the algorithm has been put into practice and evaluated in Matlab on Windows. The investigation results indicate that the modified convex hull calculation uses only a very small number of points. The study has also shown that the algorithm proposed improves the complexity of time compared to several other algorithms.
Keywords: Lung Cancer; CT-image, Lung Parenchymal Segmentation, Juxtapleural Nodules
Scope of the Article: Image analysis and Processing