Our group developed the computer aided diagnosis (CAD) system for lung cancer in 1996, and has been used in clinical field since 1997. From this CAD system (conventional system), we discovered problem and we attempted to solve the problem by using our proposed algorithm. The proposed algorithm succeeded in the improvement of the following three problems of the conventional system. (1) Weak extraction algorithm of region of interest (ROI) with noise, (2) Poor knowledge of chest structure, and (3) diagnostic processing for nodule of limited size. In this paper, the algorithm that solves problem (2) and (3) is described. We evaluated the proposed algorithm, which was applied to the following four databases. (A) Lung cancer database, (B) detailed examination database, (C) a large-scale screening database by 10mm-thickness images reconstructed from single-slice CT scan, and (D) a large-scale screening database by 10mm-thickness images reconstructed from multi-slice CT scan. The proposed method obtained the following successful results: Lung cancer database 95.7% TP and detailed examination 94.8% TP. For the large-scale screening database, we evaluated each examination process from physicians’ reading to cancer decision. The extraction rate of proposed algorithm improved as the examinations proceed. Two false positive results were obtained. False positive 1 (6.8-9.2 shadows/case) needed for a detailed examination and the object of false positive 2 (2.6-4.0 shadows/case) was an abnormal shadow.
We have been developed a computer-aided diagnosis (CAD) system in the lung cancer detection from a low-dose single-slice CT scanner. The objective of this study is to solve three problems of the conventional CAD system; application of image obtained by other CT scanner, diagnostic procedure for the ground glass shadow less than 5 mm in diameters, and diagnostic procedure for nodule in contact with blood vessels. We analyzed characteristics between each CT images, and pattern of blood vessels. The structural analysis procedure using three-dimensional data is the newly added process. The diagnostic rules to detect nodule consist of the four classes, which are divided by size and CT value. We applied two lung cancer databases; 55 nodules of TCT-900S and 67 nodules of Asteion. The present result from the former database achieved a sensitivity of 94.5%, the latter database achieved a sensitivity of 90.0%. Most of false negative cases had two cases which are a nodule overlapped by blood vessels and a nodule on mediastinum.
We have already developed a prototype of computer-aided diagnosis (CAD) system that can automatically detect suspicious shadows from Chest CT images. But the CAD system cannot detect Ground-Grass-Attenuation perfectly. In many cases, this reason depends on the inaccurate extraction of the region of interests (ROI) that CAD system analyzes, so we need to improve it.
In this paper, we propose a method of an accurate extraction of the ROI, and compare proposed method to ordinary method that have used in CAD system. Proposed Method is performed by application of the three steps. Firstly we extract lung area using threshold. Secondly we remove the slowly varying bias field using flexible Opening Filter. This Opening Filter is calculated by the combination of the ordinary opening value and the distribution which CT value and contrast follow. Finally we extract Region of Interest using fuzzy clustering. When we applied proposal method to Chest CT images, we got a good result in which ordinary method cannot achieve. In this study we used the Helical CT images that are obtained under the following measurement: 10mm beam width; 20mm/sec table speed; 120kV tube voltage; 50mA tube current; 10mm reconstruction interval.
We have been developed a computer-aided diagnosis (CAD) system for the lung cancer detection of early stage from low dose single-slice computed tomography (CT) with 10 mm beam width on chest screening. The objective of this study is to solve three problems of the conventional CAD system; (1) lesion which overlaps blood vessel, (2) lesion in contact with blood vessel and (3) lesion near upper mediastinum. This paper presents a new method to solve problem-1 and problem-2. The blood vessels, which overlap lesions and others in contact with lesion, are eliminated by detecting region of interest (ROI) with accuracy. Detection method of ROIs consists of 3 processes; firstly, streak shadows elimination using linear feature detector filter, secondly, estimation of pulmonary background bias using the intensity histogram and the opening method, and finally, ROI's border detection using laplacian filter. We evaluated the new system by apply it to 155 shadows which need confirmation diagnosis. These cases were selected from clinical test from July 1997 to December 2000 in retrospective study. True positive cases of this algorithm achieved sensitivity 91.0 %. The average of false positive cases was 0.53 per slice.