in this paper, we report the image processing technique for computer-aided diagnosis of lung cancer screening system by CT (LSCT). LSCT is the newly developed mobile-type CT scanner for the mass screening of lung cancer by our project team. in this new LSCT system, one essential problem is the increase of image information to be diagnosed by a doctor to about 30 slices per patient from I X-ray film. To solve this difficult problem, we are trying to reduce the image information drastically to be displayed for the doctor by image processing techniques. We propose a new method named Variable New-Quoit filter for the automatic recognition of the pathological shadow candidates. Our computer aided diagnosis (CAD) system can satisfactoiily reduce the number of CT cross sections by this method, containing the abnormal shadow candidates.

SUMMARYThe authors have developed the quoit filter, which is a kind of mathematical morphological filter, for automatic extraction of candidate pathological areas of lung cancer. The method has problems, however, in processing speed or extraction accuracy. To overcome these problems, this paper proposes variable quoit filtering, in which the filter size is adjusted flexibly according to the pathological shadow, and distance transformation with gray-level weight is applied as preprocessing before the main filtering procedure. First, the performance of the method is analyzed using a model, and the effectiveness of the proposed method is shown. Then, trial applications to images of 82 actual cases (including 21 cancer areas) show that all of the cancer areas were correctly extracted. Compared to the conventional algorithm, the processing time is reduced to less than 1/20.

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