Evaluation of fast 2d and 3d medical image retrieval approaches based on image miniatures

Authors:
René Donner;Sebastian Haas;Andreas Burner;Markus Holzer;Horst Bischof;Georg Langs
Affiliations:
Computational Image Analysis and Radiology Lab, Department of Radiology, Medical University of Vienna, Austria;Computational Image Analysis and Radiology Lab, Department of Radiology, Medical University of Vienna, Austria;Computational Image Analysis and Radiology Lab, Department of Radiology, Medical University of Vienna, Austria;Computational Image Analysis and Radiology Lab, Department of Radiology, Medical University of Vienna, Austria;Institute for Computer Graphics and Vision, Graz University of Technology, Austria;Computational Image Analysis and Radiology Lab, Department of Radiology, Medical University of Vienna, Austria
Venue:
MCBR-CDS'11 Proceedings of the Second MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
Year:
2011

Citing 7
Cited 1

Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Endomicroscopic image retrieval and classification using invariant visual features

ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Generalized sparse MRF appearance models

Image and Vision Computing
Overview of the CLEF 2009 medical image annotation track

CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
ImageCLEF 2009 medical image annotation task: PCTs for hierarchical multi-label classification

CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Superpixel-Based interest points for effective bags of visual words medical image retrieval

MCBR-CDS'11 Proceedings of the Second MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support

Overview of the second workshop on medical content---based retrieval for clinical decision support

MCBR-CDS'11 Proceedings of the Second MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support

Quantified Score

Hi-index	0.00

Visualization

Abstract

The present work evaluates four medical image retrieval approaches based on features derived from image miniatures. We argue that due to the restricted domain of medical image data, the standardized acquisition protocols and the absence of a potentially cluttered background a holistic image description is sufficient to capture high-level image similarities. We compare four different miniature 2D and 3D descriptors and corresponding metrics, in terms of their retrieval performance: (A) plain miniatures together with euclidean distances in a k Nearest Neighbor based retrieval backed by kD-trees; (B) correlations of rigidly aligned miniatures, initialized using the kD-tree; (C) distribution fields together with the l1 -norm; (D) SIFT-like histogram of gradients using the χ2-distance. We evaluate the approaches on two data sets: the ImageClef 2009 benchmark of 2D radiographs with the aim to categorize the images and a large set of 3D-CTs representing a realistic sample in a hospital PACS with the objective to estimate the location of the query volume.