Features of calcific leaflet distribution from CT valve images may allow prediction of hemodynamic disease severity in calcific degenerative aortic valve stenosis (DAS). The proposed study describes a signal processing scheme for selecting valve areas from maximum intensity projection valve images in a cohort of 52+43 patient images, diagnosed as having severe and moderate DAS. First the valve center and perimeter are approximated by a manually determined circle of radius r and center (x,y). The circle is then used to define eight masks, based on concentric circles of radius r 2 , r √ 2 2 , r √ 3 2 and r, each with center (x,y). The masks are used to define pixel regions within the valvular area, and statistical and textural descriptors are applied to each. Sensitivity/ specificity testing is performed with these descriptors, applied to the pixels within each mask, which show that disease severity is best predicted by using the smallest, most central mask and statistical features of skewness and kurtosis, providing area under the curve of 0.844 and 0.840 respectively. Our methodology was simple to implement and use, and provided good discriminatory power for disease severity. It also overcomes some difficulties in an earlier method, since our solution is scalable to variation in aortic valve size and tests a range of statistical and textural descriptors.