Active contour based segmentation and classification for pleura diseases based on Otsu's thresholding and Support Vector Machine (SVM)

Abstract

Objective: Generally, lung cancer is the abnormal growth of cells that originates in one or both lungs. Finding the pulmonary nodule helps in the diagnosis of lung cancer in early stage and also increase the lifetime of the individual. Accurate segmentation of normal and abnormal portion in segmentation is challenging task in computer-aided diagnostics. Methods: The article proposes an innovative method to spot the cancer portion using Otsu's segmentation algorithm. It is followed by a Support Vector Machine (SVM) classifier to classify the abnormal portion of the lung image. Results: The suggested methods use the Otsu's thresholding and active contour based segmentation techniques to locate the affected lung nodule of CT images. The segmentation is followed by an SVM classifier in order to categorize the affected portion is normal or abnormal. The proposed method is suitable to provide good and accurate segmentation and classification results for complex images. Conclusion: The comparative analysis between the two segmentation methods along with SVM classifier was performed. A classification process based on active contour and SVM techniques provides better than Otsu's segmentation for complex lung images.