Learning to Segment Using Machine-Learned Penalized Logistic Models

Classical maximum-a-posteriori (MAP) segmentation uses generative models for images. However creating tractable generative models can be difficult for complex images. Moreover; generative models require auxiliary parameters to be included in the maximization, which makes the maximization more complicated. This paper proposes an alternative to the MAP approach: using a penalized logistic model to directly model the segmentation posterior This approach has two advantages: (1) It requires fewer auxiliary parameters, and (2) it provides a standard way of incorporating powerful machine-learning methods into segmentation so that complex image phenomenon can be learned easily from a training set. The technique is used to segment cardiac ultrasound images sequences which have substantial spatio-temporal contrast variation that is cumbersome to model. Experimental results show that the method gives accurate segmentations of the endocardium in spite of the contrast variation.