Segmentation of anatomical structure by using a local classifier derived from neighborhood information
Satoko Takemoto, Hideo Yokota, Ryutaro Himeno, Taketoshi Mishima · 2008
Rapid advances in imaging modalities have increased the importance of image segmentation techniques. These techniques automatically extract data for the anatomical structure of interest and facilitate their quantitative analysis. Here we present a framework for a semi-automatic segmentation method that incorporates a local classifier derived from a neighboring image. Using the local classifier we were able to consider otherwise challenging cases of segmentation merely as two-class classification without any complicated parameters. Our method is simple to implement and easy to operate. We successfully tested our method on computed tomography images.