Pixel Position Regression - Application to medical image segmentation

B. van Ginneken and M. Loog

in: International Conference on Pattern Recognition, 2004, pages 718-721



Pixel position regression (PPR), an automatic supervised method for image segmentation, is presented. The method uses a set of corresponding points indicated in each train image. For each point in this set, the mean position in all train images is determined. By warping the set of corresponding points to their mean positions, one can associate with each position in each train image a reference position. PPR estimates the reference position from a rich set of local image features through k-nearest-neighbor regression. The deformation field thus obtained determines the segmentation. It is demonstrated that the deformation field estimate can be improved by (weighted) blurring and more sophisticated methods such as global modeling of the deformation field through principal component analysis and iterated regression. The method is evaluated on a set of chest radiographs in which the lung fields, heart and clavicles are segmented.

A pdf file of this publication is available for personal use. Enter your e-mail address in the box below and press the button. You will receive an e-mail message with a link to the pdf file.