Multiresolution Approach to Biomedical Image Segmentation with Statistical Models of Appearance

Structural variability present in biomedical images is known to aggravate the segmentation process. Statistical models of appearance proved successful in exploiting the structural variability information in the learning set to segment a previously unseen medical image more reliably. In this paper we show that biomedical image segmentation with statistical models of appearance can be improved in terms of accuracy and efficiency by a multiresolution approach. We outline two different multiresolution approaches. The first demonstrates a straightforward extension of the original statistical model and uses a pyramid of statistical models to segment the input image on various resolution levels. The second applies the idea of direct coefficient propagation through the Gaussian image pyramid and uses only one statistical model to perform the multiresolution segmentation in a much simpler manner. Experimental results illustrate the scale of improvement achieved by using the multiresolution approaches described. Possible further improvements are discussed at the end.