Multi-Attribute Non-initializing Texture Reconstruction Based Active Shape Model (MANTRA)
Robert Toth,Jonathan Chappelow,Mark A. Rosen,Sona A. Pungavkar,Arjun Kalyanpur,Anant Madabhushi +5 more
- 06 Sep 2008
- Vol. 11, pp 653-661
TL;DR: MANTRA incorporates a number of features that improve on the the popular Active Shape Model (ASM) algorithm, and does not rely on the mean pixel intensity values to find the border; instead, it reconstructs potential image patches and the image patch with the best reconstruction based on CMI is considered the object border.
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Abstract: In this paper we present MANTRA (Multi-Attribute, Non-Initializing, Texture Reconstruction Based Active Shape Model) which incorporates a number of features that improve on the the popular Active Shape Model (ASM) algorithm. MANTRA has the following advantages over the traditional ASM model. (1) It does not rely on image intensity information alone, as it incorporates multiple statistical texture features for boundary detection. (2) Unlike traditional ASMs, MANTRA finds the border by maximizing a higher dimensional version of mutual information (MI) called combined MI (CMI), which is estimated from kNN entropic graphs. The use of CMI helps to overcome limitations of the Mahalanobis distance, and allows multiple texture features to be intelligently combined. (3) MANTRA does not rely on the mean pixel intensity values to find the border; instead, it reconstructs potential image patches, and the image patch with the best reconstruction based on CMI is considered the object border. Our algorithm was quantitatively evaluated against expert ground truth on almost 230 clinical images (128 1.5 Tesla (T) T2 weighted in vivoprostate magnetic resonance (MR) images, 78 dynamic contrast enhanced breast MR images, and 21 3T in vivoT1-weighted prostate MR images) via 6 different quantitative metrics. Results from the more difficult prostate segmentation task (in which a second expert only had a 0.850 mean overlap with the first expert) show that the traditional ASM method had a mean overlap of 0.668, while the MANTRA model had a mean overlap of 0.840.
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A magnetic resonance spectroscopy driven initialization scheme for active shape model based prostate segmentation
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Integrating structural and functional imaging for computer assisted detection of prostate cancer on multi-protocol in vivo 3 Tesla MRI
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Systems and Method for Automatic Prostate Localization in MR Images Using Random Walker Segmentation Initialized Via Boosted Classifiers
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- 08 Nov 2011
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Facilitating 3D spectroscopic imaging through automatic prostate localization in MR images using random walker segmentation initialized via boosted classifiers
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