Intensity, Shape and Size Based Detection of Lung Nodules from CT Images
Lung cancer has become one of the most widely spreading diseases in the world. Detection of lung nodules is the initial step in lung cancer detection. We propose an idea to locate the lung nodules based on its intensity, shape and size. Lung CT images are used for detecting the lung nodules. Initially, Variant Ant Colony Optimization algorithm is used to detect the edges. Variant ACO algorithm greatly helps to reduce the False Positives. Nodules centers are detected in the edge detected image based on the proposed black circular neighborhood algorithm. The intensity of the lung nodules are classified based on the input image using the positions of the lung nodule center. We use lung intensity identification algorithm. Finally the malignant lung nodules are identified from the input CT image based on three features – intensity, shape and size.
KeywordsEdge detection Ant Colony Optimization (ACO) Intensity based clustering Image processing lung CT images
Unable to display preview. Download preview PDF.
- 1.Leung, A., Smithuis, R.: Solitary pulmonary nodule: benign versus malignant Differentiation with CT and PET-CT. Radiology (May 20, 2007), http://www.radiologyassistant.nl/en/p460f9fcd50637
- 2.Veerakumar, K., Ravichandran, C.G.: Applying Ant Colony Optimization algorithms and variants for lung nodule detection. Pattern Analysis and Applications (2013) (Communicated)Google Scholar
- 5.Sánchez, C.I., Niemeijer, M., Išgum, I., Dumitrescu, A., Suttorp-Schulten, M.S.A., Abràmoff, M.D., van Ginneken, B.: Contextual computer-aided detection: Improving bright lesion detection in retinal images and coronary calcification identification in CT scans. Medical Image Analysis 16(1), 50–62 (2012)CrossRefGoogle Scholar
- 8.Atsushi, T., Fujita, H.: Fast lung nodule detection in chest CT images using cylindrical nodule-enhancement filter. International Journal of Computer Assisted Radiology and Surgery, 1–13 (2013)Google Scholar
- 9.van Ginneken, B., Armato, S.G., de Hoop, B., van de Vorst, S., Duindam, T., Niemeijer, M., Murphy, K., et al.: Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: the ANODE09 study. Medical image analysis 14(6), 707–722 (2010)CrossRefGoogle Scholar