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15 May 2003 A visual data-mining approach using 3D thoracic CT images for classification between benign and malignant pulmonary nodules
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Abstract
This paper presents a visual data-mining approach to assist physicians for classification between benign and malignant pulmonary nodules. This approach retrieves and displays nodules which exhibit morphological and internal profiles consistent to the nodule in question. It uses a three-dimensional (3-D) CT image database of pulmonary nodules for which diagnosis is known. The central module in this approach makes possible analysis of the query nodule image and extraction of the features of interest: shape, surrounding structure, and internal structure of the nodules. The nodule shape is characterized by principal axes, while the surrounding and internal structure is represented by the distribution pattern of CT density and 3-D curvature indexes. The nodule representation is then applied to a similarity measure such as a correlation coefficient. For each query case, we sort all the nodules of the database from most to less similar ones. By applying the retrieval method to our database, we present its feasibility to search the similar 3-D nodule images.
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Yoshiki Kawata, Noboru Niki, Hironobu Ohamatsu, Masahiko Kusumoto, Ryutaro Kakinuma, Kiyoshi Mori, K. Yamada, Hiroyuki Nishiyama, Kenji Eguchi, Masahiro Kaneko, and Noriyuki Moriyama "A visual data-mining approach using 3D thoracic CT images for classification between benign and malignant pulmonary nodules", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); https://doi.org/10.1117/12.480648
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