A cascade learning method for liver lesion detection in CT images

Authors:
Dijia Wu;David Liu;Michael Suehling;Kevin S. Zhou;Christian Tietjen
Affiliations:
Siemens Corporate Research, Princeton, NJ;Siemens Corporate Research, Princeton, NJ;Siemens Corporate Research, Princeton, NJ;Siemens Corporate Research, Princeton, NJ;Siemens Healthcare, Forchheim, Germany
Venue:
MCV'12 Proceedings of the Second international conference on Medical Computer Vision: recognition techniques and applications in medical imaging
Year:
2012

Citing 9
Cited 0

Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Face Detection

International Journal of Computer Vision
Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Computer aided detection via asymmetric cascade of sparse hyperplane classifiers

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic 3D Polyp Detection in CT Images: The Role of Sample Alignment

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Robust Object Detection with Interleaved Categorization and Segmentation

International Journal of Computer Vision
General framework for automatic detection of matching lesions in follow-up CT

ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Automatic detection and segmentation of axillary lymph nodes

MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Automatic detection and segmentation of ground glass opacity nodules

MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

The automatic detection and segmentation of liver lesion is useful in many clinical application, whereas it remains a challenging task due to the largely varied shape, size and texture of the diseased masses. In this paper, we present a cascade learning approach comprising multiple classifiers for the detection of two different types of solid liver lesions, hypodense and hyperdense lesions. In particular, we propose an efficient gradient based locally adaptive segmentation method for the solid lesions, where the segmentation results are used to extract shape features to boost up the detection performance. The proposed method is validated on a total of 660 volumes with 1,302 hypodense lesions, and 234 volumes with 328 hyperdense lesions. The experimental results show a resulting 90% detection rate at 1.01 false positives per volume for hypodense lesion and 1.58 false positives per volume for hyperdense lesion, respectively, using three fold cross validation.