Classification-Driven Pathological Neuroimage Retrieval Using Statistical Asymmetry Measures

Authors:
Yanxi Liu;Frank Dellaert;William E. Rothfus;A. Moore;Jeff G. Schneider;Takeo Kanade
Affiliations:
-;-;-;-;-;-
Venue:
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Year:
2001

Citing 6
Cited 5

Efficient and effective querying by image content

Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
Photobook: content-based manipulation of image databases

International Journal of Computer Vision
Alignment by Maximization of Mutual Information

International Journal of Computer Vision
Medical Image Analysis: Progress over Two Decades and the Challenges Ahead

IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape-Based Indexing in a Medical Image Database

WBIA '98 Proceedings of the IEEE Workshop on Biomedical Image Analysis
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)

Using human perceptual categories for content-based retrieval from a medical image database

Computer Vision and Image Understanding
Facial asymmetry quantification for expression invariant human identification

Computer Vision and Image Understanding - Special issue on Face recognition
Automatic detection of PET lesions

VIP '02 Selected papers from the 2002 Pan-Sydney workshop on Visualisation - Volume 22
A Similarity Retrieval Method in Brain Image Sequence Database

ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Registration and retrieval of highly elastic bodies using contextual information

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper reports our methodology and initial results on volumetric pathological neuroimage retrieval. A set of novel image features are computed to quantify the statistical distributions of approximate bilateral asymmetry of normal and pathological human brains. We apply memory-based learning method to find the most-discriminative feature subset through image classification according to predefined semantic categories. Finally, this selected feature subset is used as indexing features to retrieve medically similar images under a semantic-based image retrieval framework. Quantitative evaluations are provided.