Seeded Segmentation Based on Object Homogeneity
2012 (English)In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR), 2012, 21-24 p.Conference paper, Poster (Refereed)
Seeded segmentation methods attempt to solve the segmentation problem in the presence of prior knowledge in the form of a partial segmentation, where a small subset of the image elements (seed-points) have been assigned correct segmentation labels. Common for most of the leading methods in this area is that they seek to find a segmentation where the boundaries of the segmented regions coincide with sharp edges in the image. Here, we instead propose a method for seeded segmentation that seeks to divide the image into areas of homogeneous pixel values. The method is based on the computation of minimal cost paths in a discrete representation of the image, using a novel path-cost function. The utility of the proposed method is demonstrated in a case study on segmentation of white matter hyperintensitities in MR images of the human brain.
Place, publisher, year, edition, pages
2012. 21-24 p.
Discrete Mathematics Medical Image Processing
Research subject Computerized Image Analysis; Computerized Image Processing
IdentifiersURN: urn:nbn:se:uu:diva-188438ISBN: 978-1-4673-2216-4OAI: oai:DiVA.org:uu-188438DiVA: diva2:577856
ICPR 2012, 21 st International Conference on Pattern Recognition, November 11-15 2012, Tsukuba International Congress Center, Tsukuba Science City, Japan