Authors : S. Shaik Parveen and C. Kavitha
Abstract: Lung cancer is considered to be the main cause of cancer death worldwide and it is difficult to detect in its early stages because symptoms appear only in the advanced stages causing the mortality rate to be the highest among all other types of cancer. So, the early detection of cancer is vital to cure the disease completely. Many Computer Aided Detection Systems arise to increase the accuracy and performance rate. But still the performance rate is not high. This study proposes a complete automatic Computer Aided Diagnosis System (CAD) for early detection of lung cancer nodules using Chest Computer Tomography (CT) scan images. The proposed method consists of four phases. They are lung extraction, segmentation of lung region, feature extraction and finally classification of normal, benign and malignancy in the lung. Threat pixel identification with region growing method is used for segmentation of focal areas in the lung. For feature extraction Gray Level Co-occurrence Matrix (GLCM) and Gabor features are used. Extracted features are classified using Support Vector Machine (SVM). The experimentation is performed with the help of real time computer tomography images. The proposed algorithm is fully automatic and has shown 100% sensitivity.
S. Shaik Parveen and C. Kavitha, 2014. Automatic Computer Aided Diagnosis System for Detection of Lung Cancer Nodules Using Region Growing Method and Support Vector Machines (SVM). Research Journal of Applied Sciences, 9: 299-307.