In physicians' interpretation, morphologic characteristics of pulmonary nodules are not only important signs for the discrimination, but also important features for the diagnosis with a reasonable degree of confidence. This paper describes about the computerized interpretation system which is developed to analyze the relation between the measuring values and the morphologic characteristics, and to make clear the logic of physicians' diagnosis. We think that the four basic morphologic characteristics of the discriminative diagnosis between benign and malignant nodules exist which are: (1) the density; (2) the homogeneity; (3) the definition; and (4) the convergence. To obtain each grade of the parameters, we developed an interpretation system. On the other hand, to obtain digital feature values, we used our computer aided diagnosis system. Interpretation experiments were performed by using 15 benign and 19 malignant cases of chest x-ray CT images. As the result of a statistical analysis, some digital features have the significant differences between benign and malignant nodules, and the morphological characteristics have also differences. Therefore the computerized system is feasible to help physicians' interpretation to distinct between malignant and benign nodules by showing digital feature values as some references.
This paper describes a computerized system of tumor detection for lung cancer diagnosis. Through the ten years study, we developed some key algorithms for computer-aided diagnosis. The most important algorithm is a filter to detect suspicious shadows of tumor on a plain chest x-ray image. The filter is named directional contrast filter for nodule (DCF-N). The DCF-N is highly sensitive to ambiguous shadows such as malignant tumors. And we developed a rule- based system to eliminate false-positive shadows. In our study, the system was effective to eliminate shadows of blood vessels and ribs which were primary groups of false-positives. Our current research is focusing on the development of the automatic tumor detection system for lung cancer examination by using CT images. In this paper, we discuss whether the system for plain chest x-ray images can apply to spiral CT images within a malignant tumor, which are reconstructed at 2 mm or 5 mm slice thickness. About a trial case, although the DCF-N can detect the malignant tumor, some false-positives are also detected. As to the analysis of the shadows, which are detected by the DCF-N, the major false-positives are blood vessels shadows. Therefore, the rule to eliminate blood vessels shadows in the rule-base for plain x- ray images is also effective for the CT images.
This paper describes a modified system for automatic detection of lung nodules by means of chest x ray image processing techniques. The objective of the system is to help radiologists to improve their accuracy in cancer detection. It is known from retrospective studies of chest x- ray images that radiologists fail to detect about 30 percent of lung cancer cases. A computerized method for detecting lung nodules would be very useful for decreasing the proportion of such oversights. Our proposed system consists of five sub-systems, for image input, lung region determination, nodule detection, rule-based false-positive elimination, and statistical false-positive elimination. In an experiment with the modified system, using 30 lung cancer cases and 78 normal control cases, we obtained figures of 73.3 percent and 89.7 percent for the sensitivity and specificity of the system, respectively. The system has been developed to run on the IBM* PS/55* and IBM RISC System/6000* (RS/6000), and we give the processing time for each platform.
KEYWORDS: Blood vessels, Image segmentation, 3D image processing, 3D modeling, Image processing, Veins, Medical imaging, Liver, Magnetic resonance imaging, 3D image reconstruction
This paper describes a method for segmentation and recognition of hepatic blood vessels from axial MR image sequences. We propose a method of accurate segmentation of blood vessel components, and recognition of the blood vessel structure by utilizing two dimensional (2-D) and three dimensional (3-D) anatomical information. The method consists of two parts: (1) extraction of blood vessel components and other anatomical structures, and (2) recognition of 3-D blood vessel structure using anatomical models. The system first extracts candidates of hepatic blood vessel segments from each 2-D image automatically using the directional contrast filter and other image processing techniques. The contour of the liver is extracted semi-automatically. By using the knowledge about segmental anatomy and characteristics of the way blood vessels extend, the system searches for points connecting the segments in different slices and recognizes hepatic vascular system (portal veins and hepatic veins). The knowledge is implemented in a 2-D shape model and a tree model.
This paper describes a system for automatic detection of lung nodules by means of digital image-processing techniques. The objective of the system is to help chest physicians to improve their accuracy of detection. For detecting lung nodules in chest x-ray images, the authors developed the directional contrast filter for nodules (DCF-N), which consists of three concentric circles. The DCF-N is effective for detecting patterns with obscure peripheries, such as lung cancer. The filter was evaluated using 192 lung cancer cases, and a detection ratio of 88.5% with false-positive foci was obtained. The authors also developed a rule-based system for eliminating these false-positive foci. The rule-base contains six rules that were heuristically developed according to a common method of diagnosis used by chest physicians. By using the rule-base, the authors succeeded in eliminating 63.3% of false-positive foci without increasing the number of false-negatives significantly (5.0%). In addition to the rule- base, a logic was developed for discriminating between lung nodules and false-positive foci by using the nine measured values on each shadow. The discrimination was tested by using 192 lung cancer cases and 74 normal control cases. As a result, figures of 92.2% and 71.6% were obtained for the sensitivity and specificity of the system, respectively. To evaluate the logic by using external data, 30 cases of lung cancer and 78 control cases were collected. As a result of the evaluation, the authors obtained figures of 71.3%, 76.7%, and 69.2% for the accuracy, sensitivity, and specificity of the system, respectively.
This paper presents a knowledge-based method for automatic reconstruction and recognition of pulmonary blood vessels from chest x-ray CT images with 10-mm thickness. The system has four main stages: (1) automatic extraction and segmentation of blood vessel components from each 2-D image, (2) analysis of these components, (3) a search for points connecting blood vessel segments in different CT slices, using a knowledge base for 3-D reconstruction, and (4) object manipulation and display. The authors also describe a method of representing 3-D anatomical knowledge of the pulmonary blood vessel structure. The edges of blood vessels in chest x-ray images are unclear, in contrast to those in angiograms. Each CT slice has thickness, and blood vessels are slender, so a simple graphical display, which can be used for bone tissues from CT images, is not sufficient for pulmonary blood vessels. It is therefore necessary to use anatomical knowledge to track the blood vessel lines in 3-D spaces. Experimental results using actual images of a normal adult male has shown that utilizing anatomical information enables one to improve processing efficiency and precision, such as blood vessel extraction and searching for connecting points.
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