Lung Segmentation and Iterative Weighted Averaging Smoothing Technique on Chest Ct Images
C. Sridevi1, M.Kannan2
1C. Sridevi, Department of Electronics Engineering, Anna University, Chennai (Tamil Nadu), India.
2Dr. M. Kannan, Department of Electronics Engineering, Anna University, Chennai (Tamil Nadu), India.
Manuscript received on 18 December 2019 | Revised Manuscript received on 24 December 2019 | Manuscript Published on 31 December 2019 | PP: 383-387 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10841291S319/19©BEIESP | DOI: 10.35940/ijeat.A1084.1291S319
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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 (

Abstract: Computed Tomography (CT) is one of the most commonly used imaging modalities for tumour detection and diagnosis, due to its high spatial resolution, fast imaging speed and wide availability. Nodules of the lungs and pathological residues with varied diameter can be comfortably viewed by computed tomography and can be categorized as benign or malignant. The key intention of this segmentation and smoothing is to develop an efficient methodology for an automated lung tumour diagnosis. Segmentation is the quantitative preprocessing step used in the chest CT analysis. The iterative weighted averaging technique is used in addressing the issues related to the segmentation and smoothing method employed in this paper. The methodology incorporates the accurate Lung Segmentation, enclosure of Juxtapleural nodules, the proper insertion of the bronchial vessels for enhancing the smoothness of the segmented Lung area. The steps involved in this paper include Image preprocessing by nonlinear anisotropic diffusion filtering, Thorax Extraction, Lung segmentation and iterative weighted averaging technique for smoothing the contours. The main feature in choosing the nonlinear anisotropic diffusion filtering is for properly preserving the regions unaffected with cancer and also to differentiate the artefacts involved due to the subject movements. In the fuzzy c- means clustering algorithm, the lung parenchyma is identified from the thorax region based on the fuzzy membership value and the affected regions are attached. The standard execution time for segmentation process of one dataset is 1.91s, it is the more rapid process than the manual segmentation.
Keywords: Lung Segmentation, Lung Nodules, Fuzzy c-Means Clustering, Iterative Weighted Averaging.
Scope of the Article: Signal and Image Processing