This research aims at developing a fully automatic Computer-Assisted Diagnosis (CAD) system for lung cancer screening using chest spiral CT scans. One thousand subjects are enrolled in a chest cancer screening program in Louisville, KY, USA, which aims at quantification of the effectiveness of low dose spiral CT scans for early diagnosis of lung cancer, and evaluating its possible impact on improving the mortality rate of cancer patients. This paper presents an image analysis system for 3-D reconstruction of the lungs and trachea, detection of the lung abnormalities, identification/classification of these abnormalities with respect to specific diagnosis, and distributed visualization of the results over computer networks. We present two novel approaches for segmentation of the lung tissues from the surrounding structures in the chest cavity, and detection of the abnormalities in the lungs. The segmentation algorithm is hierarchical; it starts with isolating the background from the chest cavity, then isolating the lungs from the surrounding structures (e.g., ribs, liver, and other organs that may appear in chest CT scans). Abnormalities in the lungs are detected by analyzing the segmented lung tissues and extracting the isolated lumps that appear in various connected regions. 3-D reconstructions are also generated for these abnormalities, in order to be used for subsequent identification/classification steps. Results of these algorithms are shown on 50 subjects, and have been evaluated vs. the radiologists. The image analysis approach presented in this paper has provided comparable results with respect to the experts. The approach is quite fast, and lends itself to distributed visualization over computer networks.
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