Computer Aided Diagnosis For the Detection of Lung Nodules

Shivakumar, G.

doi:10.9790/3021-0271134136

Cited by 1 publication

(2 citation statements)

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“…In the remainder of this section, two categories of papers are investigated. The first category discusses nodule detection,[ 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ] and the second group considers nodule segmentation. [ 17 18 19 20 21 22 23 ]…”

Section: Introductionmentioning

confidence: 99%

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Detection of pulmonary nodules in low-dose computed tomography using localized active contours and shape features

et al. 2017

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Background: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.Methods: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.Results: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.Conclusions: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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Section: Introductionmentioning

confidence: 99%

“…This method was applied to both a high-resolution CT and to a normal CT. Yogananda et al . [ 8 ] proposed an algorithm to detect the pulmonary nodules. After preprocessing step, morphological operations were used to detect the nodule candidates.…”

Section: Introductionmentioning

confidence: 99%