Segmentation of prostate boundaries from ultrasound images using statistical shape model

Presents a statistical shape model for the automatic prostate segmentation in transrectal ultrasound images. A Gabor filter bank is first used to characterize the prostate boundaries in ultrasound images in both multiple scales and multiple orientations. The Gabor features are further reconstructed to be invariant to the rotation of the ultrasound probe and incorporated in the prostate model as image attributes for guiding the deformable segmentation. A hierarchical deformation strategy is then employed, in which the model adaptively focuses on the similarity of different Gabor features at different deformation stages using a multiresolution technique, i.e., coarse features first and fine features later. A number of successful experiments validate the algorithm.

I. Introduction

Prostate cancer is the second-leading cause of cancer deaths in American men. The American Cancer Society predicted that in 2002, 189 000 men would be diagnosed with prostate cancer and about 30 200 would die [1]. When prostate cancer is diagnosed in its early stages, it is usually curable; and the treatment is often effective even in its later stages. Therefore, the decision of when, how, and on whom to apply a diagnostic procedure is very important [2].