Abstract: A computerized method of processing a representative ultrasound image of a solid ovarian, renal, pancreatic, bladder or cyst lesion e.g. ovarian, renal, hepatic, pancreatic, bladder, gallbladder subject to a subsequent medical diagnosis, including the phases of: segmenting the image using a first algorithm to: identify at least one outline and at least one corresponding object delimited by the outline and representative of a biological tissue of a cyst or a solid lesion; provide at least a first quantitative morphological parameter representative of the regularity of the outline of at least one object; and a second parameter representative of the content and/or consistency of an area of the ovarian, renal, pancreatic, bladder, gallbladder, hepatic solid lesion or cyst within the contour; processing the image using a second algorithm to estimate a benign or malignant classification of the cyst or solid lesion elaborating through a machine learning algorithm.

Abstract: A denoising method for removing artefacts acquiring a plurality of frames composing an ultrasound video; identifying pixels containing doppler activation. The method further includes generating for each frame, a first set of image data to associate to each pixel position of the corresponding frame at least a first or a second category to categorize the pixels of the corresponding frame and identifying for each frame the pixels containing doppler activation, generating at least a persistence sequence associated to a frame portion containing identification data ordered according to the frame sequence such that adjacent identification data in the sequence refers to consecutive frames, and in the persistence sequence, calculating the length of each persistence sub-sequence comprising consecutive identification data having the first value.

Abstract: A computer-implemented denoising method for removing artefacts from a doppler-type ultrasound video includes: acquiring a plurality (n) of frames composing an ultrasound video; identifying pixels containing doppler activation; generating for each frame, a first set of image data to associate to each pixel position of the corresponding frame at least a first or a second category to categorize the pixels of the corresponding frame and identifying for each frame the pixels containing doppler activation; generating at least a persistence sequence associated to a frame portion containing identification data ordered according to the frame sequence such that adjacent identification data in the sequence refers to consecutive frames; in the persistence sequence, calculating the length of each persistence sub-sequence comprising consecutive identification data having the first value; automatically calculating a reference threshold; eliminating the doppler pixels if the latter belong to a persistence sequence below the