Context enhanced graphical model for object localization in medical images

Authors:
Yang Song;Weidong Cai;Heng Huang;Yue Wang;David Dagan Feng
Affiliations:
BMIT Research Group, School of IT, University of Sydney, Australia;BMIT Research Group, School of IT, University of Sydney, Australia;Computer Science and Engineering, University of Texas at Arlington;Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University;BMIT Research Group, School of IT, University of Sydney, Australia
Venue:
MCV'12 Proceedings of the Second international conference on Medical Computer Vision: recognition techniques and applications in medical imaging
Year:
2012

Citing 15
Cited 0

Least Squares Support Vector Machine Classifiers

Neural Processing Letters
What Energy Functions Can Be Minimizedvia Graph Cuts?

IEEE Transactions on Pattern Analysis and Machine Intelligence
What, where and how many? combining object detectors and CRFs

ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Graph cuts with invariant object-interaction priors: application to intervertebral disc segmentation

IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Multiple structure tracing in 3d electron micrographs

MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Discriminative segmentation of microscopic cellular images

MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Sparse classification for computer aided diagnosis using learned dictionaries

MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Discriminative pathological context detection in thoracic images based on multi-level inference

MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization

MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
TextonBoost: joint appearance, shape and context modeling for multi-class object recognition and segmentation

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Learning a discriminative dictionary for sparse coding via label consistent K-SVD

CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Single-image shadow detection and removal using paired regions

CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation

IEEE Transactions on Signal Processing
Greed is good: algorithmic results for sparse approximation

IEEE Transactions on Information Theory
Cooperation of color pixel classification schemes and color watershed: a study for microscopic images

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Object localization is an important step common to many different medical applications. In this Chapter, we will review the challenges and recent approaches tackling this problem, and focus on the work by Song et.al. [20]. In [20], a new graphical model with additional contrast and interest-region potentials is designed, encoding the higher-order contextual information between regions, on the global and structural levels. A discriminative sparse-coding based interest-region detector is also integrated as one of the context prior in the graphical model. This object localization method is generally applicable to different medical imaging applications, in which the objects can be distinguished from the background mainly based on feature differences. Successful applications on two different medical imaging applications --- lesion dissimilarity on thoracic PET-CT images and cell segmentation on microscopic images --- are demonstrated in the experimental results.