Detection of Pulmonary Nodules in Low-Dose Computed Tomography using Localized Active Contours and Shape Features

zahra nadealian, Behzad Nazari, Saeid Sadri, Mohammad Momeni

DOI: 10.4103/jmss.JMSS_71_16

Abstract


Pulmonary nodules are symptoms of lung cancer. The shape and size of these nodules are used to
diagnose lung cancer in computed tomography (CT) images. In the early stages, nodules are very
small, and radiologist has to refer to many CT images to diagnose the disease, causing operator
mistakes. Image processing algorithms are used as an aid to detect and localize nodules. In this paper,
a novel lung nodules detection scheme is proposed. First, in the preprocessing stage, our algorithm
segments two lung lobes to increase processing speed and accuracy. Second, template-matching is
applied to detect the suspicious nodule candidates, including both nodules and some blood vessels.
Third, the suspicious nodule candidates are segmented by localized active contours. Finally, the
false-positive errors produced by vessels are reduced using some two-/three-dimensional geometrical
features in three steps. In these steps, the size, long and short diameters and sphericity are used to
decrease the false-positive rate. In the first step, some vessels that are parallel to CT cross-plane are
identified. In the second step, oblique vessels are detected using shift of center of gravity in two
successive slices. In step three, vessels vertical to CT cross-plane are identified. Using these steps,
vessels are separated from nodules. Early Lung Cancer Action Project is used as a popular dataset in
this work. Our algorithm achieved a sensitivity of 90.1% and a specificity of 92.8%, quite acceptable
in comparison to other related works.

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