Abstract: A method includes receiving a time series of slice images of medical imaging. The images have a region of interest located at a lung lesion. The method also includes tracking over at least one subset of slice images in a time series of slice images variations over time of at least one image parameter at the set of points in the region of interest. Classifier processing is applied to set of signals indicative of tracked time variations of the at least one image parameter at respective points in the set of points. A classification signal is indicative of the tracked time variations of the at least one image parameter reaching or failing to reach at least one classification threshold.

Abstract: A method includes receiving a time series of slice images of medical imaging. The images have a region of interest located at a lung lesion. The method also includes tracking over at least one subset of slice images in a time series of slice images variations over time of at least one image parameter at the set of points in the region of interest. Classifier processing is applied to set of signals indicative of tracked time variations of the at least one image parameter at respective points in the set of points. A classification signal is indicative of the tracked time variations of the at least one image parameter reaching or failing to reach at least one classification threshold.

Abstract: A method can be used for analyzing digital images of skin lesions. The images include pixels distributed over a lesion area. Sets of values including a first discrimination value indicative of a weighted average of the image pixels with weighing at the border of the lesion, a second discrimination value indicative of skewness and kurtosis of the distribution of the image pixels, a third discrimination value indicative of the ratio of symmetry and gray-level power of the distribution of the image pixels and calculated. A total additive score of the values in the sets of values is provided and compared with a total score threshold. The first, second and third discrimination values are compared with respective first, second and third discrimination threshold values. An output classification for the image analyzed is provided as a function of the results of the comparing.

Abstract: A method can be used for analyzing digital images of skin lesions. The images include pixels distributed over a lesion area. Sets of values including a first discrimination value indicative of a weighted average of the image pixels with weighing at the border of the lesion, a second discrimination value indicative of skewness and kurtosis of the distribution of the image pixels, a third discrimination value indicative of the ratio of symmetry and gray-level power of the distribution of the image pixels and calculated. A total additive score of the values in the sets of values is provided and compared with a total score threshold. The first, second and third discrimination values are compared with respective first, second and third discrimination threshold values. An output classification for the image analyzed is provided as a function of the results of the comparing.

Abstract: A method can be used for analyzing digital images of skin lesions. The images include pixels distributed over a lesion area. Sets of values including a first discrimination value indicative of a weighted average of the image pixels with weighing at the border of the lesion, a second discrimination value indicative of skewness and kurtosis of the distribution of the image pixels, a third discrimination value indicative of the ratio of symmetry and gray-level power of the distribution of the image pixels and calculated. A total additive score of the values in the sets of values is provided and compared with a total score threshold. The first, second and third discrimination values are compared with respective first, second and third discrimination threshold values. An output classification for the image analyzed is provided as a function of the results of the comparing.

Abstract: A method can be used for analyzing digital images of skin lesions. The images include pixels distributed over a lesion area. Sets of values including a first discrimination value indicative of a weighted average of the image pixels with weighing at the border of the lesion, a second discrimination value indicative of skewness and kurtosis of the distribution of the image pixels, a third discrimination value indicative of the ratio of symmetry and gray-level power of the distribution of the image pixels and calculated. A total additive score of the values in the sets of values is provided and compared with a total score threshold. The first, second and third discrimination values are compared with respective first, second and third discrimination threshold values. An output classification for the image analyzed is provided as a function of the results of the comparing.