1. A method for detecting a level set tree feature for measuring changes between images with a non-transitory computer-readable medium, comprising: providing at least two pixilated images; electronically generating a model of said at least two pixilated images; electronically determining maximal meaningful nodes from said model of said at least two pixilated images; wherein said determining maximal meaningful nodes of said at least two pixilated images, further comprises: selecting a node from a set of nodes {I 1. .. I n }; calculating the number of white pixels in a region corresponding to said selected node; calculating a number of false alarms for said selected node; storing said number of false alarms for said selected node in an electronic storage device; calculating relative entropy, H q ({G i } i=1 q ), of said selected node, wherein {G i } i=1 q are children of said selected node; wherein labeling said selected node as meaningful when said calculated number of false alarms for said selected node is less than a predetermined value ε; or wherein labeling said selected node as not meaningful when said calculated number of false alarms for said selected node is not less than said predetermined value ε; and iterating through said selecting a node from a set of nodes {I 1. .. I n } step, said calculating the number of white pixels in a region corresponding to said node step, said calculating a number of false alarms for said selected node step, said storing said number of false alarms for said selected node step, said calculating relative entropy, H q ({G i } i=1 q ), of said selected node wherein {G i } i=1 q are children of said selected node step, said labeling said selected node as meaningful when said calculated number of false alarms for said selected node is less than a predetermined value ε step, and said labeling said selected node as not meaningful when said calculated number of false alarms for said selected node is not less than said predetermined value ε step, until each node in the set of nodes {I 1. .. I n } has been labeled either meaningful or not meaningful; and outputting in a tangible medium said maximal meaningful nodes.