摘要:
A method and medical system for the postdischarge surveillance of a patient (1) for detecting a case of pneumonia, secondary bleeding, a wound healing problem, a pulmonary complication, a urinary tract infection or a thrombosis of the patient (1), having a data bank (13), which is arranged at a location other than the location (2) at which the patient (1) is based during the surveillance and stores data relevant for the postdischarge surveillance of the patient (1) recorded during the postdischarge surveillance.
摘要:
The invention relates to a method for analyzing image processing procedures, in which unprocessed original image data is stored, stored original image data is retrieved, retrieved original image data is processed, the individual processing steps during the processing of the image data are in some instances stored together with the respective processing step parameter values, and the processed image data is stored such that it can be assigned to the processing steps stored during its processing and in some instances processing step parameter values. According to the invention the processed image data is analyzed statistically and the result of the statistical analysis is stored such that it can be assigned to the stored processing steps and in some instances processing step parameter values. The original image data is optionally included in the statistical analysis of the processed image data.
摘要:
A local adaptive method is proposed for automatic detection of microaneurysms in a digital ocular fundus image. Multiple subregions of the image are automatically analyzed and adapted to local intensity variation and properties. A priori region and location information about structural features such as vessels, optic disk and hard exudates are incorporated to further improve the detection accuracy. The method effectively improves the specificity of microaneurysms detection, without sacrificing sensitivity. The method may be used in automatic level-one grading of diabetic retinopathy screening.
摘要:
A method for optic disk detection in retinal images, includes: extracting a vessel tree from a retinal image; locating an optic disk in the retinal image using the vessel tree; enhancing a contrast of the optic disk; removing vessels from the contrast enhanced optic disk; detecting a boundary of the optic disk with vessels removed by generating an edge map; projecting the edge map at a plurality of angles by using a radon transform; and estimating a radius and center of the optic disk using the projections.