Lung cancer detection based on helical CT images using curved surface morphology analysis

Lung cancer is known as one of the most difficult cancers to cure. The detection of lung cancer in its early stage can be helpful for medical treatment to limit the danger. A conventional technique that assists the detection uses helical CT, which provides information of 3D cross sectional images of the lung. We expect that the proposed technique will increase diagnostic confidence. However, mass screening based on helical CT images leads to a considerable number of images for the diagnosis, this time-consuming fact makes it difficult to be used in the clinic. To increase the efficiency of the mass screening process, we had proposed a computer-aided diagnosis (CAD). In this paper, we describe lung cancer detection based on helical CT Images using curved surface morphology analysis. Firstly, we extract the lung area from the original image. Secondly, we compute shape index value of the lung area. Thirdly, we extract the ROI (Region Of Interest) from the computed shape index value. Finally, we apply the diagnosis rule using neural network and detect the suspicious regions. We show here the result of our algorithm which is applied to helical CT images of 390 patients.