Active shape model segmentation with optimal features
- PMID: 12472265
- DOI: 10.1109/TMI.2002.803121
Active shape model segmentation with optimal features
Abstract
An active shape model segmentation scheme is presented that is steered by optimal local features, contrary to normalized first order derivative profiles, as in the original formulation [Cootes and Taylor, 1995, 1999, and 2001]. A nonlinear kNN-classifier is used, instead of the linear Mahalanobis distance, to find optimal displacements for landmarks. For each of the landmarks that describe the shape, at each resolution level taken into account during the segmentation optimization procedure, a distinct set of optimal features is determined. The selection of features is automatic, using the training images and sequential feature forward and backward selection. The new approach is tested on synthetic data and in four medical segmentation tasks: segmenting the right and left lung fields in a database of 230 chest radiographs, and segmenting the cerebellum and corpus callosum in a database of 90 slices from MRI brain images. In all cases, the new method produces significantly better results in terms of an overlap error measure (p < 0.001 using a paired T-test) than the original active shape model scheme.
Similar articles
-
A novel approach for curve evolution in segmentation of medical images.Comput Med Imaging Graph. 2010 Jul;34(5):354-61. doi: 10.1016/j.compmedimag.2009.12.006. Epub 2010 Jan 18. Comput Med Imaging Graph. 2010. PMID: 20083384
-
Statistical shape models for 3D medical image segmentation: a review.Med Image Anal. 2009 Aug;13(4):543-63. doi: 10.1016/j.media.2009.05.004. Epub 2009 May 27. Med Image Anal. 2009. PMID: 19525140 Review.
-
Segmenting lung fields in serial chest radiographs using both population-based and patient-specific shape statistics.IEEE Trans Med Imaging. 2008 Apr;27(4):481-94. doi: 10.1109/TMI.2007.908130. IEEE Trans Med Imaging. 2008. PMID: 18390345
-
Active shape models with invariant optimal features: application to facial analysis.IEEE Trans Pattern Anal Mach Intell. 2007 Jul;29(7):1105-17. doi: 10.1109/TPAMI.2007.1041. IEEE Trans Pattern Anal Mach Intell. 2007. PMID: 17496371
-
Adaptive elastic segmentation of brain MRI via shape-model-guided evolutionary programming.IEEE Trans Med Imaging. 2002 Aug;21(8):910-23. doi: 10.1109/TMI.2002.803124. IEEE Trans Med Imaging. 2002. PMID: 12472264
Cited by
-
MADR-Net: multi-level attention dilated residual neural network for segmentation of medical images.Sci Rep. 2024 Jun 3;14(1):12699. doi: 10.1038/s41598-024-63538-2. Sci Rep. 2024. PMID: 38830932 Free PMC article.
-
Structure boundary-preserving U-Net for prostate ultrasound image segmentation.Front Oncol. 2022 Jul 28;12:900340. doi: 10.3389/fonc.2022.900340. eCollection 2022. Front Oncol. 2022. PMID: 35965563 Free PMC article.
-
A computer-aid multi-task light-weight network for macroscopic feces diagnosis.Multimed Tools Appl. 2022;81(11):15671-15686. doi: 10.1007/s11042-022-12565-0. Epub 2022 Feb 28. Multimed Tools Appl. 2022. PMID: 35250359 Free PMC article.
-
Lung Segmentation using Active Shape Model to Detect the Disease from Chest Radiography.J Biomed Phys Eng. 2021 Dec 1;11(6):747-756. doi: 10.31661/jbpe.v0i0.2105-1346. eCollection 2021 Dec. J Biomed Phys Eng. 2021. PMID: 34904071 Free PMC article.
-
2D Statistical Lung Shape Analysis Using Chest Radiographs: Modelling and Segmentation.J Digit Imaging. 2021 Jun;34(3):523-540. doi: 10.1007/s10278-021-00440-7. Epub 2021 Mar 22. J Digit Imaging. 2021. PMID: 33754214 Free PMC article.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources