Performing accurate and fully automated lung segmentation of high-resolution computed tomography (HRCT) images affected by dense abnormalities is a challenging problem. This paper presents a novel algorithm for automated segmentation of lungs based on modified convex hull algorithm and mathematical morphology techniques. Sixty randomly selected lung HRCT scans with different abnormalities are used to test the proposed algorithm, and experimental results show that the proposed approach can accurately segment the lungs even in the presence of disease patterns, with some limitations in the apices and bases of lungs. The algorithm demonstrates a high segmentation accuracy (dice similarity coefficient = 98.62 and shape differentiation metrics d mean = 1.39 mm, and d rms = 2.76 mm). Therefore, the developed automated lung segmentation algorithm is a good candidate for the first stage of a computer-aided diagnosis system for diffuse lung diseases.

The pulmonary lung nodule is the most common indicator of lung cancer. An efficient automated pulmonary nodule detection system aids the radiologists to detect the lung abnormalities at an early stage. In this paper, an automated lung nodule detection system using a feature descriptor based on optimal manifold statistical thresholding to segment lung nodules in Computed Tomography (CT) scans is presented. The system comprises three processing stages. In the first stage, the lung region is extracted from thoracic CT scans using gray level thresholding and 3D connected component labeling. After that novel lung contour correction method is proposed using modified convex hull algorithm to correct the border of a diseased lung. In the second stage, optimal manifold statistical image thresholding is described to minimize the discrepancy between nodules and other tissues of the segmented lung region. Finally, a set of 2D and 3D features are extracted from the nodule candidates, and then the system is trained by employing support vector machines (SVM) to classify the nodules and non-nodules. The performance of the proposed system is assessed using Lung TIME database. The system is tested on 148 cases containing 36408 slices with total sensitivity of 94.3%, is achieved with only 2.6 false positives per scan.

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