In this paper, we first introduce three different geometric features including shape index,
curvedness and sphericity ratio, for colonic polyp detection. A new color coding scheme is
designed to highlight the detected polyps, and help radiologists to distinguish them from
other tissues more easily. The key idea is to place the detected polyp candidates at the
same locations in a newly created polygonal dataset with exactly the same topological and
geometrical properties as the triangulated mesh surface of real colon dataset, and assign
different colors to the two separated datasets to highlight the polyps. Finally, we validate
the proposed polyp detection framework and color coding scheme by computer simulated
and real colon datasets. For sixteen synthetic polyps with different shapes and different
sizes, the sensitivity is 100%, and false positive is 0.
A cost-effective autofluorescence detecting system has been developed by our research group to diagnose and localize the early gastrointestinal cancer, which is occult to the traditional means of detection, for example, biopsy. At the early stage, we utilize autofluorescence spectrum detected by OMA (Optical Multichannel Analyzer) to discriminate cancerous tissue. Although this method can effectively distinguish tumors from normal tissues, it is not suitable to be applied in clinic use due to the high cost of the- most-often used OMA-autofluorescence detector. Then we designed a novel Double PMTs (Photomultiply Tube) system, which consists of two parallel-working A/Ds with lower frequency of acquisition, to replace the OMA system, and the results of clinic experiments prove that it can effectively determine gastrointestinal cancers.
In this paper, a model for correction of distortion in endoscope image is proposed. The definition of optical distortion is described briefly and the theory of correction of distortion using grid consisting of solid circles is presented. Then, we address the three steps of correction, including pre-processing of grid image, correction of spatial distortion and reconstruction of gray level. Finally, the corrected results are given to demonstrate the performance and validity of correction algorithm with standard calibration grid.
The heart is the most important organ to our life. The coronary artery is the only path through which blood is provided to the heart. In the vessels of the heart especially the coronary artery, fluency of blood is mainly determined by vascular diameter and blood pressure. If blood is obstructed thus flows not freely due to vascular straitness, coronary heart disease would have a high occurrence among the aged people. Therefore, measurement of vessel diameter of coronary artery and the vascular parameters are of great importance of reflecting the function of the heart and disease diagnosis. In this paper, one novel and simple method to measure the coronary arterial diameter is presented and the relative difference of vessel (Dpi) is defined as one clinic diagnosis criterion for the degree of vascular straitness.
Extraction of edge feature and accurate measurement of vascular diameter in cardiovascular image are the bases for labeling the coronary hierarchy, 3D refined reconstruction of the coronary arterial tree and accurate fusion between the calculated 3D vascular trees and other views. In order to extract vessels from the image, the grayscale minimization of the circle template and differential edge detection are put forward. Edge pixels of the coronary artery are set according to maximization of the differential value. The edge lines are determined after the edge pixels are smoothed by B-Spline function. The assessment of feature extraction is demonstrated by the excellent performance in computer simulation and actual application.
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