A method of identifying anomalous data in a test data set , wherein the test data set is a test image comprising a plurality of test pixels or voxels as test data points, the method comprising: storing one or more standard data sets of a plurality of standard data cases, each standard data case containing a plurality of standard data points representing normal, non-anomalous, data, wherein each standard data case is a standard image comprising a plurality of standard pixels or voxels as standard data points; establishing a correspondence between test data points of the test data set and respective corresponding standard data points in the one or more sets of a plurality of standard data cases, wherein establishing a correspondence comprises aligning the test image in co-registration with the at least one standard image; calculating (B.7.4) a first average similarity distance measure between a test data point in the test data set and the nearest neighbouring corresponding standard data points in at least two of the plurality of standard data cases; wherein the nearest neighbouring corresponding standard data points are those standard pixels of the at least two of the plurality of standard data cases, closest in value to the test pixel; determining (B.7.6) a second average similarity distance measure between the nearest neighbouring corresponding standard data points in the at least two of the plurality of standard data cases; calculating (B.7.6) a normalised similarity distance measure relating to the test data point using a mathematical function, wherein the first and second average similarity distance measures are inputs to the mathematical function; wherein the mathematical function used to calculate the normalised similarity distance measure is the difference between the first and second average similarity distance measures; and identifying (B.4.8) the test data point as anomalous in dependence on the value of the calculated normalised similarity distance measure for the test data point. storing one or more standard data sets of a plurality of standard data cases, each standard data case containing a plurality of standard data points representing normal, non-anomalous, data, wherein each standard data case is a standard image comprising a plurality of standard pixels or voxels as standard data points; establishing a correspondence between test data points of the test data set and respective corresponding standard data points in the one or more sets of a plurality of standard data cases, wherein establishing a correspondence comprises aligning the test image in co-registration with the at least one standard image; calculating (B.7.4) a first average similarity distance measure between a test data point in the test data set and the nearest neighbouring corresponding standard data points in at least two of the plurality of standard data cases; wherein the nearest neighbouring corresponding standard data points are those standard pixels of the at least two of the plurality of standard data cases, closest in value to the test pixel; determining (B.7.6) a second average similarity distance measure between the nearest neighbouring corresponding standard data points in the at least two of the plurality of standard data cases; calculating (B.7.6) a normalised similarity distance measure relating to the test data point using a mathematical function, wherein the first and second average similarity distance measures are inputs to the mathematical function; wherein the mathematical function used to calculate the normalised similarity distance measure is the difference between the first and second average similarity distance measures; and identifying (B.4.8) the test data point as anomalous in dependence on the value of the calculated normalised similarity distance measure for the test data point. <EOS>