Learn More
We have developed a double-matching method and an artificial visual neural network technique for lung nodule detection. This neural network technique is generally applicable to the recognition of medical image pattern in gray scale imaging. The structure of the artificial neural net is a simplified network structure of human vision. The fundamental(More)
The authors have developed a neural-digital computer-aided diagnosis system, based on a parameterized two-level convolution neural network (CNN) architecture and on a special multilabel output encoding procedure. The developed architecture was trained, tested, and evaluated specifically on the problem of diagnosis of lung cancer nodules found on digitized(More)
This paper reports on technology developments aimed at improving the state of the art for image-guided minimally invasive spine procedures. Back pain is a major health problem with serious economic consequences. Minimally invasive procedures to treat back pain are rapidly growing in popularity due to improvements in technique and the substantially reduced(More)
This paper addresses the quantification and seg-mentation in brain tissue analysis by using MR brain scan. It is shown that this problem can be solved by distribution learning and relaxation labeling, an efficient method that may be particularly useful in quantifying and segmenting abnormal brain cases where the distribution of each tissue type may heavily(More)
In recent years, many computer-aided diagnosis schemes have been proposed to assist radiologists in detecting lung nodules. The research efforts have been aimed at increasing the sensitivity while decreasing the false-positive detections on digital chest radiographs. Among the problems of reducing the number of false positives, the differentiation between(More)