3. The method of claim 1 , wherein extracting a plurality of 1D profiles along each of the plurality of overlapping fixed size branch segments comprises: extracting, for each of the plurality of overlapping fixed length branch segments, 1D profiles of vessel scale, image intensity, centerline curvature, tubularity measure, image intensity and gradient statistics along and inside a cross-sectional circular boundary, and distance to a most proximal point in the branch of the detected centerline tree.
3. The method of claim 1 , wherein extracting a plurality of 1D profiles along each of the plurality of overlapping fixed size branch segments comprises: extracting, for each of the plurality of overlapping fixed length branch segments, 1D profiles of vessel scale, image intensity, centerline curvature, tubularity measure, image intensity and gradient statistics along and inside a cross-sectional circular boundary, and distance to a most proximal point in the branch of the detected centerline tree. 4. The method of claim 3 , wherein extracting, for each of the plurality of overlapping fixed length branch segments, 1D profiles of vessel scale, image intensity, centerline curvature, tubularity measure, image intensity and gradient statistics along and inside a cross-sectional circular boundary, and distance to a most proximal point in the branch of the detected centerline tree comprises: for each of the plurality of overlapping fixed length branch segments, extracting, from the medical image, a respective value at each centerline point of the overlapping fixed length branch segment for each of the vessel scale, the image intensity, the centerline curvature, the tubularity measure, an intensity mean along a cross-section circular boundary, an intensity standard deviation along the cross-section circular boundary, an intensity mean inside the cross-section circular boundary, an intensity standard deviation inside the cross-section circular boundary, a gradient mean along the cross-section circular boundary, a gradient standard deviation along the cross-section circular boundary, a gradient mean inside the cross-section circular boundary, a gradient standard deviation inside the cross-sectional circular boundary, and the distance to the most proximal point in the branch of the detected centerline tree.