Fully Automated Coronal and Sagittal Chest Segmentation using Colour Features and Fuzzy C-Means Clustering in CT Images
Z. Faizal KhanCollege of Computing and Information Technology, Shaqra University, Kingdom of Saudi Arabia.
Corresponding Author E-mail: faizalkhan@su.edu.sa
Abstract: In this article, a Combination of Fuzzy logic and color features based segmentation approach for parenchyma of lung from the Coronal and Sagittal Chest CT images is proposed. This approach employs a modified method of segmenting lung parenchyma which is considered as the Region of Interest (ROI) from the Coronal and Sagittal Chest CT images. The first step is the pre-processing of CT lung image in for the process of removing the noise and artefacts present in it. The border detection process is carried out as a Second step where all the regions including the tissue and lung parenchyma is separated by a border detection algorithm. Third step is the color formation process in which the image along with its border is formed in magenta color. Then, the color features extracted from the image border and are given as input to the Improved Fuzzy C Means clustering (IFCM) method to produce the lung Parenchyma. Experimental results reveal that the proposed methodology provides better segmentation results of 97.8 % accuracy in segmenting the parenchyma from Coronal and Sagittal Chest CT images.
Keywords: Coronal; Improved Fuzzy-C-Means Clustering; Sagittal Chest Images; Segmentation; Texture Features; Back to TOC