NDLI: Effective 3D object detection and regression using probabilistic segmentation features in CT images

Content Provider	IEEE Xplore Digital Library
Author	Le Lu Jinbo Bi Wolf, M. Salganicoff, M.
Copyright Year	2011
Description	Author affiliation: CAD & Knowledge Solutions, Siemens Medical Solutions, Inc., Malvern, PA 19355, USA (Le Lu; Jinbo Bi; Wolf, M.; Salganicoff, M.)
Abstract	3D object detection and importance regression/ranking are at the core for semantically interpreting 3D medical images of computer aided diagnosis (CAD). In this paper, we propose effective image segmentation features and a novel multiple instance regression method for solving the above challenges. We perform supervised learning based segmentation algorithm on numerous lesion candidates (as 3D VOIs: Volumes Of Interest in CT images) which can be true or false. By assessing the statistical properties in the joint space of segmentation output (e.g., a 3D class-specific probability map or cloud), and original image appearance, 57 descriptive features in six subgroups are derived. The new feature set shows excellent performance on effectively classifying ambiguous positive and negative VOIs, for our CAD system of detecting colonic polyps using CT images. The proposed regression model on our segmentation derived features behaves as a robust object (polyp) size/importance estimator and ranking module with high reliability, which is critical for automatic clinical reporting and cancer staging. Extensive evaluation is executed on a large clinical dataset of 770 CT scans from 12 medical sites for validation, with the best state-of-the-art results.
Starting Page	1049
Ending Page	1056
File Size	2367522
Page Count	8
File Format	PDF
ISBN	9781457703942
ISSN	10636919
e-ISBN	9781457703959
DOI	10.1109/CVPR.2011.5995359
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Three dimensional displays Probabilistic logic Computed tomography Feature extraction Cancer Biomedical imaging
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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