Shape Particle Filtering for Image Segmentation

TY - GEN

T1 - Shape Particle Filtering for Image Segmentation

AU - de Bruijne, Marleen

AU - Nielsen, Mads

N1 - Conference code: 7

PY - 2004

Y1 - 2004

N2 - Deformable template models are valuable tools in medical image segmentation. Current methods elegantly incorporate global shape and appearance, but can not cope with localized appearance variations and rely on an assumption of Gaussian gray value distribution. Furthermore, initialization near the optimal solution is required. We propose a maximum likelihood shape inference that is based on pixel classification, so that local and non-linear intensity variations are dealt with naturally, while a global shape model ensures a consistent segmentation. Optimization by stochastic sampling removes the need for accurate initialization. The method is demonstrated on three different medical image segmentation problems: vertebra segmentation in spine radiographs, lung field segmentation in thorax X rays, and delineation of the myocardium of the left ventricle in MRI slices. Accurate results were obtained in all tasks.

AB - Deformable template models are valuable tools in medical image segmentation. Current methods elegantly incorporate global shape and appearance, but can not cope with localized appearance variations and rely on an assumption of Gaussian gray value distribution. Furthermore, initialization near the optimal solution is required. We propose a maximum likelihood shape inference that is based on pixel classification, so that local and non-linear intensity variations are dealt with naturally, while a global shape model ensures a consistent segmentation. Optimization by stochastic sampling removes the need for accurate initialization. The method is demonstrated on three different medical image segmentation problems: vertebra segmentation in spine radiographs, lung field segmentation in thorax X rays, and delineation of the myocardium of the left ventricle in MRI slices. Accurate results were obtained in all tasks.

U2 - 10.1007/b100265

DO - 10.1007/b100265

M3 - Article in proceedings

C2 - 20209048

SN - 978-3-540-22976-6

T3 - Lecture notes in computer science

SP - 168

EP - 175

BT - Medical Image Computing and Computer-Assisted Intervention – MICCAI

PB - <Forlag uden navn>

T2 - International Conference in Medical Image Computing and Computer-Assisted Intervention (MICCAI)

Y2 - 29 November 2010

ER -