Computer aided diagnosis system for lung cancer based on helical CT images

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Abstract
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 diagnosis, this time-consuming fact makes it difficult to be used in the clinic. To increase the efficiency of the mass screening process, we developed an algorithm for automatic detection of lung cancer candidates based on the helical CT images. Our algorithm consists of analysis and diagnosis procedures. In the analysis procedure, we extract the lung regions and the pulmonary blood vessel regions and we analyze the features of these regions using image processing techniques In the diagnosis procedure, we define diagnosis rules based on these features, and we detect tumor candidates using these rules. The diagnostic algorithm is applied to the helical CT images of 450 cases which have been diagnosed by three radiologists. Our system detected all tumors which were suspected to be lung cancer by the experts. Currently, we are planning to carry out a field test using our algorithm to evaluate the efficiency for visual diagnosis.

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