Measuring brain lesion progression with a supervised tissue classification system.

Evangelia I. Zacharaki, Stathis Kanterakis, R. Nick Bryan, Christos Davatzikos

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Brain lesions, especially White Matter Lesions (WMLs), are associated with cardiac and vascular disease, but also with normal aging. Quantitative analysis of WML in large clinical trials is becoming more and more important. In this paper, we present a computer-assisted WML segmentation method, based on local features extracted from conventional multi-parametric Magnetic Resonance Imaging (MRI) sequences. A framework for preprocessing the temporal data by jointly equalizing histograms reduces the spatial and temporal variance of data, thereby improving the longitudinal stability of such measurements and hence the estimate of lesion progression. A Support Vector Machine (SVM) classifier trained on expert-defined WML's is applied for lesion segmentation on each scan using the AdaBoost algorithm. Validation on a population of 23 patients from 3 different imaging sites with follow-up studies and WMLs of varying sizes, shapes and locations tests the robustness and accuracy of the proposed segmentation method, compared to the manual segmentation results from an experienced neuroradiologist. The results show that our CAD-system achieves consistent lesion segmentation in the 4D data facilitating the disease monitoring.

Original languageEnglish (US)
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages620-627
Number of pages8
Volume11
EditionPt 1
StatePublished - 2008
Externally publishedYes

Fingerprint

Brain
Vascular Diseases
Heart Diseases
Magnetic Resonance Imaging
Clinical Trials
White Matter
Population
Support Vector Machine

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Zacharaki, E. I., Kanterakis, S., Bryan, R. N., & Davatzikos, C. (2008). Measuring brain lesion progression with a supervised tissue classification system. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 1 ed., Vol. 11, pp. 620-627)

Measuring brain lesion progression with a supervised tissue classification system. / Zacharaki, Evangelia I.; Kanterakis, Stathis; Bryan, R. Nick; Davatzikos, Christos.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 11 Pt 1. ed. 2008. p. 620-627.

Research output: Chapter in Book/Report/Conference proceedingChapter

Zacharaki, EI, Kanterakis, S, Bryan, RN & Davatzikos, C 2008, Measuring brain lesion progression with a supervised tissue classification system. in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 1 edn, vol. 11, pp. 620-627.
Zacharaki EI, Kanterakis S, Bryan RN, Davatzikos C. Measuring brain lesion progression with a supervised tissue classification system. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 1 ed. Vol. 11. 2008. p. 620-627
Zacharaki, Evangelia I. ; Kanterakis, Stathis ; Bryan, R. Nick ; Davatzikos, Christos. / Measuring brain lesion progression with a supervised tissue classification system. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 11 Pt 1. ed. 2008. pp. 620-627
@inbook{d9ed4687e9e84bf6885b7726a985b49c,
title = "Measuring brain lesion progression with a supervised tissue classification system.",
abstract = "Brain lesions, especially White Matter Lesions (WMLs), are associated with cardiac and vascular disease, but also with normal aging. Quantitative analysis of WML in large clinical trials is becoming more and more important. In this paper, we present a computer-assisted WML segmentation method, based on local features extracted from conventional multi-parametric Magnetic Resonance Imaging (MRI) sequences. A framework for preprocessing the temporal data by jointly equalizing histograms reduces the spatial and temporal variance of data, thereby improving the longitudinal stability of such measurements and hence the estimate of lesion progression. A Support Vector Machine (SVM) classifier trained on expert-defined WML's is applied for lesion segmentation on each scan using the AdaBoost algorithm. Validation on a population of 23 patients from 3 different imaging sites with follow-up studies and WMLs of varying sizes, shapes and locations tests the robustness and accuracy of the proposed segmentation method, compared to the manual segmentation results from an experienced neuroradiologist. The results show that our CAD-system achieves consistent lesion segmentation in the 4D data facilitating the disease monitoring.",
author = "Zacharaki, {Evangelia I.} and Stathis Kanterakis and Bryan, {R. Nick} and Christos Davatzikos",
year = "2008",
language = "English (US)",
volume = "11",
pages = "620--627",
booktitle = "Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention",
edition = "Pt 1",

}

TY - CHAP

T1 - Measuring brain lesion progression with a supervised tissue classification system.

AU - Zacharaki, Evangelia I.

AU - Kanterakis, Stathis

AU - Bryan, R. Nick

AU - Davatzikos, Christos

PY - 2008

Y1 - 2008

N2 - Brain lesions, especially White Matter Lesions (WMLs), are associated with cardiac and vascular disease, but also with normal aging. Quantitative analysis of WML in large clinical trials is becoming more and more important. In this paper, we present a computer-assisted WML segmentation method, based on local features extracted from conventional multi-parametric Magnetic Resonance Imaging (MRI) sequences. A framework for preprocessing the temporal data by jointly equalizing histograms reduces the spatial and temporal variance of data, thereby improving the longitudinal stability of such measurements and hence the estimate of lesion progression. A Support Vector Machine (SVM) classifier trained on expert-defined WML's is applied for lesion segmentation on each scan using the AdaBoost algorithm. Validation on a population of 23 patients from 3 different imaging sites with follow-up studies and WMLs of varying sizes, shapes and locations tests the robustness and accuracy of the proposed segmentation method, compared to the manual segmentation results from an experienced neuroradiologist. The results show that our CAD-system achieves consistent lesion segmentation in the 4D data facilitating the disease monitoring.

AB - Brain lesions, especially White Matter Lesions (WMLs), are associated with cardiac and vascular disease, but also with normal aging. Quantitative analysis of WML in large clinical trials is becoming more and more important. In this paper, we present a computer-assisted WML segmentation method, based on local features extracted from conventional multi-parametric Magnetic Resonance Imaging (MRI) sequences. A framework for preprocessing the temporal data by jointly equalizing histograms reduces the spatial and temporal variance of data, thereby improving the longitudinal stability of such measurements and hence the estimate of lesion progression. A Support Vector Machine (SVM) classifier trained on expert-defined WML's is applied for lesion segmentation on each scan using the AdaBoost algorithm. Validation on a population of 23 patients from 3 different imaging sites with follow-up studies and WMLs of varying sizes, shapes and locations tests the robustness and accuracy of the proposed segmentation method, compared to the manual segmentation results from an experienced neuroradiologist. The results show that our CAD-system achieves consistent lesion segmentation in the 4D data facilitating the disease monitoring.

UR - http://www.scopus.com/inward/record.url?scp=79551686762&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79551686762&partnerID=8YFLogxK

M3 - Chapter

VL - 11

SP - 620

EP - 627

BT - Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

ER -