A Supervised Learning Framework for Automatic Prostate Segmentation in Trans Rectal Ultrasound Images
- 1 January 2012
- book chapter
- Published by Springer Nature in Lecture Notes in Computer Science
- p. 190-200
Abstract
No abstract availableKeywords
This publication has 17 references indexed in Scilit:
- A probabilistic framework for automatic prostate segmentation with a statistical model of shape and appearance2011
- Statistical shape and texture model of quadrature phase information for prostate segmentationInternational Journal of Computer Assisted Radiology and Surgery, 2011
- Spatial Decision Forests for MS Lesion Segmentation in Multi-Channel MR Images2010
- Automatic initialization of an active shape model of the prostateMedical Image Analysis, 2008
- Parametric Image Alignment Using Enhanced Correlation Coefficient MaximizationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2008
- Semi-automated segmentation of the prostate gland boundary in ultrasound images using a machine learning approach2008
- Prostate Segmentation in 2D Ultrasound Images Using Image Warping and Ellipse Fitting2006
- Segmentation of abdominal ultrasound images of the prostate using a priori information and an adapted noise filterComputerized Medical Imaging and Graphics, 2005
- Active appearance models1998
- Use of active shape models for locating structures in medical imagesImage and Vision Computing, 1994