A non-linear gray-level appearance model improves active shape model segmentation

Bram Van Ginneken, Alejandro F. Frangi, Joes J. Staal, Bart M. Ter Haar Romeny, Max A. Viergever

Research output: Contribution to conferencePaperpeer-review

Abstract

Active Shape Models (ASMs), a knowledge-based segmentation algorithm developed by Cootes and Taylor, have become a standard and popular method for detecting structures in medical images. In ASMs - and various comparable approaches - the model of the object's shape and of its gray-level variations is based the assumption of linear distributions. In this work, we explore a new way to model the gray-level appearance of the objects, using a k-nearest-neighbors (kNN) classifier and a set of selected features for each location and resolution of the Active Shape Model. The construction of the kNN classifier and the selection of features from training images is fully automatic. We compare our approach with the standard ASMs on synthetic data and in four medical segmentation tasks. In all cases, the new method produces significantly better results (p < 0.001).

Original languageEnglish
Pages205-212
Number of pages8
Publication statusPublished - 2001
EventWorkshop on Mathematical Methods in Biomedical Image Analysis MMBIA 2001 - Kauai, HI, United States
Duration: 9 Dec 200110 Dec 2001

Conference

ConferenceWorkshop on Mathematical Methods in Biomedical Image Analysis MMBIA 2001
Country/TerritoryUnited States
CityKauai, HI
Period9/12/0110/12/01

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