Effective 3D Object Detection and Regression Using Probabilistic Segmentation Features in CT Images

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 rank...

• Type: Journal
• Year: 2011
• Where: CVPR
• Authors: Le Lu, Jinbo Bi, Matthias Wolf, Marcos Salganicoff
• Keywords: Computer Vision, CT Images, CVPR 2011, Effective Image Segmentation, Images