Abstract: A method of producing a denoised medical image at a specified resolution, comprising: a) denoising an original medical image acquired at a resolution higher than the specified resolution; and b) reducing the resolution of the image, after denoising, to the specified resolution, using a nonlinear resolution reduction procedure that preserves more fine detail in the image than a linear resolution reduction procedure would.

Abstract: A method of reducing noise in an image, comprising: a) for each pixel being examined in the image, selecting a set of search pixels; b) calculating a value of one or more features of a neighborhood of each search pixel, and a value of corresponding features of a neighborhood of the pixel being examined; and c) calculating a reduced noise grey value for each pixel being examined, based on raw or transformed grey values of its search pixels, with greater sensitivity to those with one or more feature values similar to those of the pixel being examined; wherein calculating the value of at least one feature comprises calculating a characteristic of a distribution of raw or transformed grey values of pixels in the neighborhood, other than a mean grey value of all pixels in the neighborhood.

Abstract: A method for generating a three-dimensional (3D) low-resolution image from a 3D high-resolution medical image. The 3D high-resolution medical image is a plurality of 2D images having an axial resolution in an axial direction in which the plurality of 2D images were acquired, the method including decomposing the plurality of 2D images generating a plurality of transformed data sets, wherein the transformed data sets corresponds to one of the plurality of 2D images; weighting each pixel in the transformed data sets lo generate a plurality of weighted transformed data sets; combining the plurality of weighted transformed data into a single new transformed data set; and generating a 3D low-resolution image, having a first resolution in the axial direction from each of the single new transformed data sets, wherein the first resolution in the axial direction of the 3D low-resolution image is lower than or equal to the axial resolution.

Abstract: A system for analyzing a source medical image of a body organ. The system comprises an input unit for obtaining the source medical image having three dimensions or more, a feature extraction unit that is designed for obtaining a number of features of the body organ from the source medical image, and a classification unit that is designed for estimating a priority level according to the features.

Abstract: A method of producing a denoised medical image at a specified resolution, comprising: a) denoising an original medical image acquired at a resolution higher than the specified resolution; and b) reducing the resolution of the image, after denoising, to the specified resolution, using a nonlinear resolution reduction procedure that preserves more fine detail in the image than a linear resolution reduction procedure would.

Abstract: A method of reducing noise in an image, comprising: a) for each pixel being examined in the image, selecting a set of search pixels; b) calculating a value of one or more features of a neighborhood of each search pixel, and a value of corresponding features of a neighborhood of the pixel being examined; and c) calculating a reduced noise grey value for each pixel being examined, based on raw or transformed grey values of its search pixels, with greater sensitivity to those with one or more feature values similar to those of the pixel being examined; wherein calculating the value of at least one feature comprises calculating a characteristic of a distribution of raw or transformed grey values of pixels in the neighborhood, other than a mean grey value of all pixels in the neighborhood.

Abstract: A system for analyzing a source medical image of a body organ. The system comprises an input unit for obtaining the source medical image having three dimensions or more, a feature extraction unit that is designed for obtaining a number of features of the body organ from the source medical image, and a classification unit that is designed for estimating a priority level according to the features.