Automated pulmonary nodule detection system in computed tomography images based on Active-contour and SVM classification algorithm

Abstract

Lung cancer is a common type of cancer that requires early diagnosis. Computer systems by particular different image processing techniques can use for increase the speed and accuracy of lung nodule detection. CT images used in this work in order to process medical images. In this paper proposed an automatic lung nodule detection algorithm using active contour method and SVM classification method. In proposed method, at first in order to achieve better results, lung CT image pre-processing is performed. Then the lung area is segmented by thresholding method followed by some reconstruction techniques to transfer non-isolated nodules into isolated ones. In the next step the nodule candidates are determined using active contour method. Then, nodules are detected by the support vector machine (SVM) classifier using efficient 2D stochastic and 3D anatomical features. In the result, nodules are detected with an overall detection rate of 87%; the number of false positive is 7.5/scan and the location of all detected nodules are recognized correctly.