Toward unsupervised classification of calcified arterial lesions

Gerd Brunner; Uday Kurkure; Deepak R. Chittajallu; Raja P. Yalamanchili; Ioannis A. Kakadiaris

doi:10.1007/978-3-540-85988-8_18

Toward unsupervised classification of calcified arterial lesions

Gerd Brunner
, Uday Kurkure
, Deepak R. Chittajallu
, Raja P. Yalamanchili
, Ioannis A. Kakadiaris

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

13 Scopus citations

Abstract

There is growing evidence that calcified arterial deposits play a crucial role in the pathogenesis of cardiovascular disease. This paper investigates the challenging problem of unsupervised calcified lesion classification. We propose an algorithm, US-CALC (UnSupervised Calcified Arterial Lesion Classification), that discriminates arterial lesions from non-arterial lesions. The proposed method first mines the characteristics of calcified lesions using a novel optimization criterion and then identifies a subset of lesion features which is optimal for classification. Second, a two stage clustering is deployed to discriminate between arterial and non-arterial lesions. A histogram intersection distance measure is incorporated to determine cluster proximity. The clustering hierarchies are carefully validated and the final clusters are determined by a new intra-cluster compactness measure. Experimental results indicate an average accuracy of approximately 80% on a database of electron beam CT heart scans.

Original language	English (US)
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings
Pages	144-152
Number of pages	9
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-540-85988-8_18
State	Published - 2008
Event	11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008 - New York, NY, United States Duration: Sep 6 2008 → Sep 10 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	5241 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008
Country/Territory	United States
City	New York, NY
Period	9/6/08 → 9/10/08

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-540-85988-8_18

Cite this

Brunner, G., Kurkure, U., Chittajallu, D. R., Yalamanchili, R. P., & Kakadiaris, I. A. (2008). Toward unsupervised classification of calcified arterial lesions. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings (PART 1 ed., pp. 144-152). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5241 LNCS, No. PART 1). https://doi.org/10.1007/978-3-540-85988-8_18

Brunner, Gerd ; Kurkure, Uday ; Chittajallu, Deepak R. et al. / Toward unsupervised classification of calcified arterial lesions. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings. PART 1. ed. 2008. pp. 144-152 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

@inproceedings{0b97b2e5a985498c840529daa2198e43,

title = "Toward unsupervised classification of calcified arterial lesions",

abstract = "There is growing evidence that calcified arterial deposits play a crucial role in the pathogenesis of cardiovascular disease. This paper investigates the challenging problem of unsupervised calcified lesion classification. We propose an algorithm, US-CALC (UnSupervised Calcified Arterial Lesion Classification), that discriminates arterial lesions from non-arterial lesions. The proposed method first mines the characteristics of calcified lesions using a novel optimization criterion and then identifies a subset of lesion features which is optimal for classification. Second, a two stage clustering is deployed to discriminate between arterial and non-arterial lesions. A histogram intersection distance measure is incorporated to determine cluster proximity. The clustering hierarchies are carefully validated and the final clusters are determined by a new intra-cluster compactness measure. Experimental results indicate an average accuracy of approximately 80\% on a database of electron beam CT heart scans.",

author = "Gerd Brunner and Uday Kurkure and Chittajallu, \{Deepak R.\} and Yalamanchili, \{Raja P.\} and Kakadiaris, \{Ioannis A.\}",

year = "2008",

doi = "10.1007/978-3-540-85988-8\_18",

language = "English (US)",

isbn = "354085987X",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 1",

pages = "144--152",

booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings",

edition = "PART 1",

note = "11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008 ; Conference date: 06-09-2008 Through 10-09-2008",

}

Brunner, G, Kurkure, U, Chittajallu, DR, Yalamanchili, RP & Kakadiaris, IA 2008, Toward unsupervised classification of calcified arterial lesions. in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 5241 LNCS, pp. 144-152, 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008, New York, NY, United States, 9/6/08. https://doi.org/10.1007/978-3-540-85988-8_18

Toward unsupervised classification of calcified arterial lesions. / Brunner, Gerd; Kurkure, Uday; Chittajallu, Deepak R. et al.
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings. PART 1. ed. 2008. p. 144-152 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5241 LNCS, No. PART 1).

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

TY - GEN

T1 - Toward unsupervised classification of calcified arterial lesions

AU - Brunner, Gerd

AU - Kurkure, Uday

AU - Chittajallu, Deepak R.

AU - Yalamanchili, Raja P.

AU - Kakadiaris, Ioannis A.

PY - 2008

Y1 - 2008

N2 - There is growing evidence that calcified arterial deposits play a crucial role in the pathogenesis of cardiovascular disease. This paper investigates the challenging problem of unsupervised calcified lesion classification. We propose an algorithm, US-CALC (UnSupervised Calcified Arterial Lesion Classification), that discriminates arterial lesions from non-arterial lesions. The proposed method first mines the characteristics of calcified lesions using a novel optimization criterion and then identifies a subset of lesion features which is optimal for classification. Second, a two stage clustering is deployed to discriminate between arterial and non-arterial lesions. A histogram intersection distance measure is incorporated to determine cluster proximity. The clustering hierarchies are carefully validated and the final clusters are determined by a new intra-cluster compactness measure. Experimental results indicate an average accuracy of approximately 80% on a database of electron beam CT heart scans.

AB - There is growing evidence that calcified arterial deposits play a crucial role in the pathogenesis of cardiovascular disease. This paper investigates the challenging problem of unsupervised calcified lesion classification. We propose an algorithm, US-CALC (UnSupervised Calcified Arterial Lesion Classification), that discriminates arterial lesions from non-arterial lesions. The proposed method first mines the characteristics of calcified lesions using a novel optimization criterion and then identifies a subset of lesion features which is optimal for classification. Second, a two stage clustering is deployed to discriminate between arterial and non-arterial lesions. A histogram intersection distance measure is incorporated to determine cluster proximity. The clustering hierarchies are carefully validated and the final clusters are determined by a new intra-cluster compactness measure. Experimental results indicate an average accuracy of approximately 80% on a database of electron beam CT heart scans.

UR - https://www.scopus.com/pages/publications/58849127933

UR - https://www.scopus.com/pages/publications/58849127933#tab=citedBy

U2 - 10.1007/978-3-540-85988-8_18

DO - 10.1007/978-3-540-85988-8_18

M3 - Conference contribution

C2 - 18979742

AN - SCOPUS:58849127933

SN - 354085987X

SN - 9783540859871

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 144

EP - 152

BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings

T2 - 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008

Y2 - 6 September 2008 through 10 September 2008

ER -

Brunner G, Kurkure U, Chittajallu DR, Yalamanchili RP, Kakadiaris IA. Toward unsupervised classification of calcified arterial lesions. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings. PART 1 ed. 2008. p. 144-152. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-540-85988-8_18

Toward unsupervised classification of calcified arterial lesions

Abstract

Publication series

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this