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Medical Image Segmentation using an Extended Active Shape Model
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 69 - Number 19 |
| Year of Publication: 2013 |
| Authors: Eustace Ebhotemhen |
10.5120/12079-8173
|
Eustace Ebhotemhen . Medical Image Segmentation using an Extended Active Shape Model. International Journal of Computer Applications. 69, 19 ( May 2013), 24-29. DOI=10.5120/12079-8173
Abstract
In this paper, an approach for improving active shape segmentation of medical images using machine learning techniques which can achieve high segmentation accuracy is described. A statistical shape model is created from a training dataset which is used to search for an object of interest in an image. Active shape model has shown over time to be a reliable image segmentation methodology, but its segmentation accuracy is hindered especially by poor initialization which can't be guaranteed to always be perfect. In our methodology, we extract features for each landmark using haarfilters[9]. We train a classifier with these features and use the classifier to classify points around the final points of an Active shape model search. The aim of this approach is to better place points that might have been wrongly placed from the ASM search. We have used the simple, yet effective K-Nearest Neighbour machine learning algorithm, and have demonstrated the ability of this method to improve segmentation accuracy by segmenting 2d images of the lateral ventricles of the brain.
References
- D. Cristinacce and T. F. Cootes, "Boosted Regression Active Shape Models", Proc. British Machine Vision Conference, Vol. 2, 2007, pp. 880-889.
- Etyngier P, Ségonne F, Keriven R, "Active-contour-based image segmentation using machine learning techniques". Med Image Comput Assist Interv. 2007;10(Pt 1):891-9.
- Jain V, Seung HS, Turaga SC, "Machines that learn to segment images: a crucial technology for connectomics". CurrOpinNeurobiol. 2010 Oct;20(5):653-66.
- L. Padma Suresh, K. L. Shunmuganathan and S. H. Krishna Veni, "Dermoscopic Image Segmentation using Machine Learning Algorithm", American Journal of Applied Sciences 8 (11): 1159-1168, 2011.
- Neeraj Sharma and Laht M. Aggarwal, "Automatic medical image segmentation techniques". Journal of medical physics. Jmedphys 2010, Jan-Mar, 35(1) 3-14.
- Nils Plath, Marc Toussaint, Shinichi Nakajima, "Multi-class image segmentation using conditional random fields and global classification". Proceedings of the 26th Annual International Conference on Machine Learning, 2009,pp. 817-824.
- P. Etyngier , F. Segonne and R. Keriven N. Ayache , S. Ourselin and A. Maeder "Active-Contour-Based Image Segmentation Using Machine Learning Techniques", Proc. Medical Image Computing and Computer-Assisted Intervention, pp. 891 -899 2007.
- P. Viola and M. Jones. "Robust real-time object detection". International Journal of Computer Vision, 57(2):137_154, 2004.
- Salem Almari, N. V. Kalyankar and Khamitkar S. D, "Image segmentation by using Thresholding techniques". Journal of Computing, vol 2, issue 5, May 2010, ISSN 2151-9617.
- Shuyu Li, Litao Zhu, Tianzi Jiang, "Active Shape Model Segmentation Using Local Edge Structures and AdaBoost". Medical Imaging and Augmented Reality Lecture Notes in Computer Science Volume 3150, 2004, pp 121-128.
- Srinivas C. Turaga, Kevin L. Briggman, Moritz Helmstaedter, Winfried Denk, H. Sebastian Seung, "Maximin affinity learning of image segmentation", In Advances in Neural Information Processing Systems 22 (2009), pp. 1865-1873.
- T. F. Cootes, D. Cooper, C. J. Taylor and J. Graham, "Active Shape Models - Their Training and Application. " Computer Vision and Image Understanding. Vol. 61, No. 1, Jan. 1995, pp. 38-59.
- T. F. Cootes, G. J. Edwards and C. J. Taylor. "Active Appearance Models", IEEE PAMI, Vol. 23, No. 6, pp. 681-685, 2001.
- Xudong Cao, Yinchen Wei, Feng Wen, Jian Sun, "Face alignment by Explicit Shape Regression". Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2887 – 2894.
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