Abstract: A method for characterizing regions on a map or image generated from diffusion image data of a region of a patient's body, including: loading into a data processing device image data achieved by calculation from MR diffusion imaging; providing a first threshold level to distinguish pixels with image data values that are above or below the values found that are not representative of pathological portions of the region; providing a second threshold level higher than the first threshold; deriving a measure related to the gradient pattern of those pixels that lie within a margin zone defined by those pixels that satisfy a criterion of the second threshold but do not satisfy a criterion of the first threshold and; outputting the measure related to the gradient as a parameter indicative of the likelihood that the pixels that are within the margin zone and/or a region of the body part are cancerous.

Abstract: A method for generating a map of a region of a patient's body containing one or more lesions that provides information about MR diffusion properties and/or level of suspicion of malignancy. Performing at least one first scan of the region with an MRI apparatus set to a first b value to obtain a first matrix of pixel or voxel values, int(B1); performing at least one second scan of the region with the apparatus set to a second b value to obtain a second matrix of pixel or voxel intensity values, int(B2); deriving a first computed value that is a monotonic function of ln(int(B1)/int(B2); multiplying each computed value by a value proportional to int (B1) to obtain a second computed value; and producing a representation of all the second computed values that is indicative of the likelihood that one or more of the lesions are malignant.

Abstract: A method for generating a map of a region of a patient's body containing one or more lesions that provides information about MR diffusion properties and/or level of suspicion of malignancy. Performing at least one first scan of the region with an MRI apparatus set to a first b value to obtain a first matrix of pixel or voxel values, int(B1); performing at least one second scan of the region with the apparatus set to a second b value to obtain a second matrix of pixel or voxel intensity values, int(B2); deriving a first computed value that is a monotonic function of ln(int(B1)/int(B2); multiplying each computed value by a value proportional to int (B1) to obtain a second computed value; and producing a representation of all the second computed values that is indicative of the likelihood that one or more of the lesions are malignant.

Abstract: A method of aiding in the evaluation of lesions in a body using a plurality of sets of image data of sections of the body region in mutually parallel planes. Processing of the data requires several steps, including pixel intensity pattern recognition, ˜ contouring of the regions of probable pathology, thresholding and 3-dimensional clustering, providing each pixel with a color representative of a selected opacity level and displaying an image of the region of interest where areas of probable pathological conditions are highlighted.

Abstract: A method of identifying the location of a lesion in an image and evaluating whether the identified lesion is more likely to be cancerous, benign or uncertain is provided where the image includes a plurality of pixels, each pixel having a particular intensity I in the range of 0<I<2<sup.

Abstract: A method of identifying the location of a lesion in an image and evaluating whether the identified lesion is more likely to be cancerous, benign or uncertain is provided where the image includes a plurality of pixels, each pixel having a particular intensity I in the range of 0<I<2<sup.

Abstract: A method of aiding in the evaluation of lesions in a body using a plurality of sets of image data of sections of the body region in mutually parallel planes. Processing of the data requires several steps, including pixel intensity pattern recognition, contouring of the regions of probable pathology, thresholding and 3-dimensional clustering, providing each pixel with a color representative of a selected opacity level and displaying an image of the region of interest where areas of probable pathological conditions are highlighted.