Regularized discriminative direction for shape difference analysis

The “discriminative direction” has been proven useful to re-

veal the subtle difference between two anatomical shape classes. When a

shape moves along this direction, its deformation will best manifest the

class difference detected by a kernel classifier. However, we observe that

such a direction cannot maintain a shape’s “anatomical” correctness, in-

troducing spurious difference. To overcome this drawback, we develop

a regularized discriminative direction by requiring a shape to conform

to its population distribution when it deforms along the discriminative

direction. Instead of iterative optimization, an analytic solution is pro-

vided to directly work out this direction. Experimental study shows its

superior performance in detecting and localizing the difference of hip-

pocampal shapes for sex. The result is supported by other independent

research in the same domain.