Semi-automatic discrimination of normal tissue and liver cancer lesions in contrast enhanced X-ray CT-scans

Sanat Upadhyay; Manos Papadakis; Saurabh Jain; Gregory Gladish M.d.; Ioannis A. Kakadiaris; Robert Azencott

doi:10.1007/978-3-642-33612-6_17

Semi-automatic discrimination of normal tissue and liver cancer lesions in contrast enhanced X-ray CT-scans

Sanat Upadhyay, Manos Papadakis, Saurabh Jain, Gregory Gladish M.d., Ioannis A. Kakadiaris, Robert Azencott

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Scopus citations

Abstract

In this paper we present a set of 3D-rigid motion invariant texture features. We experimentally establish that when they are combined with mean attenuation intensity differences the new augmented features are capable of discriminating normal from abnormal liver tissue in arterial phase contrast enhanced X-ray CT-scans with high sensitivity and specificity. To extract these features CT-scans are processed in their native dimensionality. We experimentally observe that the 3D-rotational invariance of the proposed features improves the clustering of the feature vectors extracted from normal liver tissue samples.

Original language	English (US)
Title of host publication	Abdominal Imaging
Subtitle of host publication	Computational and Clinical Applications - 4th International Workshop, Held in Conjunction with MICCAI 2012, Proceedings
Pages	158-167
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-33612-6_17
State	Published - 2012
Event	4th International Workshop on Computational and Clinical Applications in Abdominal Imaging, Held in Conjunction with the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012 - Nice, France Duration: Oct 1 2012 → Oct 1 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7601 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	4th International Workshop on Computational and Clinical Applications in Abdominal Imaging, Held in Conjunction with the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012
Country/Territory	France
City	Nice
Period	10/1/12 → 10/1/12

Keywords

3D-texture classification
Liver cancer
rotationally invariant features
soft tissue discrimination

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-33612-6_17

Cite this

Upadhyay, S., Papadakis, M., Jain, S., Gladish M.d., G., Kakadiaris, I. A., & Azencott, R. (2012). Semi-automatic discrimination of normal tissue and liver cancer lesions in contrast enhanced X-ray CT-scans. In Abdominal Imaging: Computational and Clinical Applications - 4th International Workshop, Held in Conjunction with MICCAI 2012, Proceedings (pp. 158-167). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7601 LNCS). https://doi.org/10.1007/978-3-642-33612-6_17

Semi-automatic discrimination of normal tissue and liver cancer lesions in contrast enhanced X-ray CT-scans. / Upadhyay, Sanat; Papadakis, Manos; Jain, Saurabh et al.
Abdominal Imaging: Computational and Clinical Applications - 4th International Workshop, Held in Conjunction with MICCAI 2012, Proceedings. 2012. p. 158-167 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7601 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Upadhyay, S, Papadakis, M, Jain, S, Gladish M.d., G, Kakadiaris, IA & Azencott, R 2012, Semi-automatic discrimination of normal tissue and liver cancer lesions in contrast enhanced X-ray CT-scans. in Abdominal Imaging: Computational and Clinical Applications - 4th International Workshop, Held in Conjunction with MICCAI 2012, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7601 LNCS, pp. 158-167, 4th International Workshop on Computational and Clinical Applications in Abdominal Imaging, Held in Conjunction with the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012, Nice, France, 10/1/12. https://doi.org/10.1007/978-3-642-33612-6_17

Upadhyay S, Papadakis M, Jain S, Gladish M.d. G, Kakadiaris IA, Azencott R. Semi-automatic discrimination of normal tissue and liver cancer lesions in contrast enhanced X-ray CT-scans. In Abdominal Imaging: Computational and Clinical Applications - 4th International Workshop, Held in Conjunction with MICCAI 2012, Proceedings. 2012. p. 158-167. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-33612-6_17

Upadhyay, Sanat ; Papadakis, Manos ; Jain, Saurabh et al. / Semi-automatic discrimination of normal tissue and liver cancer lesions in contrast enhanced X-ray CT-scans. Abdominal Imaging: Computational and Clinical Applications - 4th International Workshop, Held in Conjunction with MICCAI 2012, Proceedings. 2012. pp. 158-167 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In this paper we present a set of 3D-rigid motion invariant texture features. We experimentally establish that when they are combined with mean attenuation intensity differences the new augmented features are capable of discriminating normal from abnormal liver tissue in arterial phase contrast enhanced X-ray CT-scans with high sensitivity and specificity. To extract these features CT-scans are processed in their native dimensionality. We experimentally observe that the 3D-rotational invariance of the proposed features improves the clustering of the feature vectors extracted from normal liver tissue samples.",

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AU - Kakadiaris, Ioannis A.

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Semi-automatic discrimination of normal tissue and liver cancer lesions in contrast enhanced X-ray CT-scans

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