A computer-implemented method of building an abnormality quantifier configured to identify and quantify the extent of abnormality in one or more measurements of subjects or members of a cohort, the abnormality being bone abnormality or obesity, the method comprising: generating at least one first selected dataset comprising measurements of a normal population or sample and at least one second selected dataset comprising measurements of an abnormal population or sample; generating a map (120; 140) by imagizing the datasets; identifying a normality zone (122; 132) within the map (120; 140) using the first dataset; identifying an abnormality zone within the map (120; 140) using the second dataset; determining a definition of abnormality based on a comparison of the normality zone (122; 132) and the abnormality zone; receiving or accessing at least one third dataset comprising measurements of a both known normal and abnormal population or sample; testing the performance of the definition of abnormality against one or more preset performance criteria, using the at least one third dataset; improving the performance by modifying characteristics used to identify one or more of normality and abnormality; and outputting an abnormality quantifier when a preset performance threshold has been reached; wherein the measurements of the normal population or sample comprise co-measurements, being measurements that relate to respective characteristics of the normal population or sample, and the measurements of the abnormal population or sample comprise co-measurements, being measurements that relate to respective characteristics of the abnormal population or sample; the abnormality quantifier is configured to identify and quantify the extent of abnormality in the sample on the basis of the improved definition of abnormality; the characteristics used to identify abnormality comprise one or more of: a shape, direction, orientation or other geometric definition of the abnormality zone, spatial characteristics of the abnormality zone, and a shape or orientation of the abnormality zone in relation to that of the normality zone; and the characteristics used to identify normality comprise any one or more of: spatial characteristics of the normality zone, a most central point in the normality zone, a most normal point, external edges of the normality zone, and a point with coordinates being average values of the co-measurements of the first dataset. generating at least one first selected dataset comprising measurements of a normal population or sample and at least one second selected dataset comprising measurements of an abnormal population or sample; generating a map (120; 140) by imagizing the datasets; identifying a normality zone (122; 132) within the map (120; 140) using the first dataset; identifying an abnormality zone within the map (120; 140) using the second dataset; determining a definition of abnormality based on a comparison of the normality zone (122; 132) and the abnormality zone; receiving or accessing at least one third dataset comprising measurements of a both known normal and abnormal population or sample; testing the performance of the definition of abnormality against one or more preset performance criteria, using the at least one third dataset; improving the performance by modifying characteristics used to identify one or more of normality and abnormality; and outputting an abnormality quantifier when a preset performance threshold has been reached; wherein the measurements of the normal population or sample comprise co-measurements, being measurements that relate to respective characteristics of the normal population or sample, and the measurements of the abnormal population or sample comprise co-measurements, being measurements that relate to respective characteristics of the abnormal population or sample; the abnormality quantifier is configured to identify and quantify the extent of abnormality in the sample on the basis of the improved definition of abnormality; the characteristics used to identify abnormality comprise one or more of: a shape, direction, orientation or other geometric definition of the abnormality zone, spatial characteristics of the abnormality zone, and a shape or orientation of the abnormality zone in relation to that of the normality zone; and the characteristics used to identify normality comprise any one or more of: spatial characteristics of the normality zone, a most central point in the normality zone, a most normal point, external edges of the normality zone, and a point with coordinates being average values of the co-measurements of the first dataset.