Solitary pulmonary nodules may indicate an early stage of lung cancer. Hence, the early detection of nodules is the most efficient way for saving the lives of patients. The aim of this paper is to present a comprehensive Computer Aided Diagnosis (CADx) framework for detection of the lung nodules in computed tomography images. The four major components of the developed framework are lung segmentation, identification of candidate nodules, classification and visualization. The process starts with segmentation of lung regions from the thorax. Then, inside the segmented lung regions, candidate nodules are identified using an approach based on multiple thresholds followed by morphological opening and 3D region growing algorithm. Finally, a combination of a rule-based procedure and support vector machine classifier (SVM) is utilized to classify the candidate nodules. The proposed CADx method was validated on CT images of 60 patients, containing the total of 211 nodules, selected from the publicly available Lung Image Database Consortium (LIDC) image dataset. Comparing to the other state of the art methods, the proposed framework demonstrated acceptable detection performance (Sensitivity: 0.80; Fp/Scan: 3.9). Furthermore, we visualize a range of anatomical structures including the 3D lung structure and the segmented nodules along with the Maximum Intensity Projection (MIP) volume rendering method that will enable the radiologists to accurately and easily estimate the distance between the lung structures and the nodules which are frequently difficult at best to recognize from CT images.
Reducing False-Positives in Lung Nodules Detection Using Balanced Datasets
