A method for obtaining a segmented image of a region of interest, in a programmable system, comprising: A. a training phase, comprising the steps of: A1. receiving data sets each comprised of volume data each obtained by imaging a region of interest by a different imaging modality, A2. determining a reference segmented image corresponding to each data set; A3. extracting, from each data set, a vector of feature values termed feature vector, comprising values for each of said volume data, relating to a pre-determined set of image features; A4. applying one or more segmentation algorithms to each of said data sets, each segmentation algorithm being defined by a set of algorithm parameters, each algorithm being applied a plurality of times by varying the values of said algorithm parameters; A5. comparing the results of the previous step to the corresponding reference segmented image and obtaining a quality metric quantifying the quality of the segmentation for each segmentation algorithm and algorithm parameters; A6. constructing a set of interrelated tables which put into relationship feature vectors, segmentation algorithms, algorithms parameters and quality metric; and B. an execution phase, comprising the steps of: B1. receiving a data set comprised of volume data each obtained by imaging regions of interests by a different imaging modality; B2. extracting, from said data set, a vector of feature values termed feature vector, comprising values for each of said volume data, relating to said pre-determined set of image features; B3. selecting a best segmentation algorithm and best algorithm parameters for the best segmentation algorithm based on the comparison between said feature vector of the previous step with feature vectors in said set of interrelated tables; B4. obtaining a segmented image of said region of interest by segmenting the data set by using the best segmentation algorithm and relevant best algorithm parameters. A. a training phase, comprising the steps of: A1. receiving data sets each comprised of volume data each obtained by imaging a region of interest by a different imaging modality, A2. determining a reference segmented image corresponding to each data set; A3. extracting, from each data set, a vector of feature values termed feature vector, comprising values for each of said volume data, relating to a pre-determined set of image features; A4. applying one or more segmentation algorithms to each of said data sets, each segmentation algorithm being defined by a set of algorithm parameters, each algorithm being applied a plurality of times by varying the values of said algorithm parameters; A5. comparing the results of the previous step to the corresponding reference segmented image and obtaining a quality metric quantifying the quality of the segmentation for each segmentation algorithm and algorithm parameters; A6. constructing a set of interrelated tables which put into relationship feature vectors, segmentation algorithms, algorithms parameters and quality metric; and A1. receiving data sets each comprised of volume data each obtained by imaging a region of interest by a different imaging modality, A2. determining a reference segmented image corresponding to each data set; A3. extracting, from each data set, a vector of feature values termed feature vector, comprising values for each of said volume data, relating to a pre-determined set of image features; A4. applying one or more segmentation algorithms to each of said data sets, each segmentation algorithm being defined by a set of algorithm parameters, each algorithm being applied a plurality of times by varying the values of said algorithm parameters; A5. comparing the results of the previous step to the corresponding reference segmented image and obtaining a quality metric quantifying the quality of the segmentation for each segmentation algorithm and algorithm parameters; A6. constructing a set of interrelated tables which put into relationship feature vectors, segmentation algorithms, algorithms parameters and quality metric; and B. an execution phase, comprising the steps of: B1. receiving a data set comprised of volume data each obtained by imaging regions of interests by a different imaging modality; B2. extracting, from said data set, a vector of feature values termed feature vector, comprising values for each of said volume data, relating to said pre-determined set of image features; B3. selecting a best segmentation algorithm and best algorithm parameters for the best segmentation algorithm based on the comparison between said feature vector of the previous step with feature vectors in said set of interrelated tables; B4. obtaining a segmented image of said region of interest by segmenting the data set by using the best segmentation algorithm and relevant best algorithm parameters. B1. receiving a data set comprised of volume data each obtained by imaging regions of interests by a different imaging modality; B2. extracting, from said data set, a vector of feature values termed feature vector, comprising values for each of said volume data, relating to said pre-determined set of image features; B3. selecting a best segmentation algorithm and best algorithm parameters for the best segmentation algorithm based on the comparison between said feature vector of the previous step with feature vectors in said set of interrelated tables; B4. obtaining a segmented image of said region of interest by segmenting the data set by using the best segmentation algorithm and relevant best algorithm parameters.