Methods for automation of vascular lesions detection in computed tomography images
type: Dataset
description: This thesis presents a framework for the detection and diagnosis of vascular lesions with a special emphasis on coronary heart disease. Coronary heart disease remains to be the first cause of mortality worldwide. Typically, the problem of vascular lesion identification has been solved by trying to model the abnormalities (lesions). The main drawback of this approach is that lesions are highly heterogeneous, which makes the detection of previously unseen abnormalities difficult. We have selected not to model lesions directly, but to treat them as anomalies which are seen as low probability density points. We propose the use of two classification frameworks based on support vector machines (SVM) for the density level detection problem. The main advantage of these two methods is that the learning stage does not require labeled data representing lesions, which is always difficult to obtain. The first method is completely unsupervised, whereas the second one only requires a limited number of labels for normality. The use of these anomaly detection algorithms requires the use of features such that anomalies are represented as points with low probability density. For this purpose, we developed an intensity based metric, denoted concentric rings, designed to capture the nearly symmetric intensity profiles of healthy vessels, as well as discrepancies with respect to the normal behavior. Moreover, we have selected a large set of alternative candidate features to use as input for the classifiers. Experiments on synthetic data and cardiac CT data demonstrated that our metric has a good performance in the detection of anomalies, when used with the selected classifiers. Combination of other features with the concentric rings metric has potential to improve the classification performance. We defined an unsupervised feature selection scheme that allows the definition of an optimal subset of features. We compared it with existent supervised feature selection methods. These experimen…
landing page: https://theses.hal.science/tel-00860825
identifier: NNT: 2011LYO10010
keyword: appareil-detude
keyword: atherosclerose
keyword: atherosclerosis
keyword: computed-tomography
keyword: computer-assisted-diagnosis
keyword: diagnostic-assiste-par-ordinateur
keyword: feature-selection
keyword: imagerie-medicale
keyword: infoeu-reposemanticsdoctoralthesis
keyword: machine-learning
keyword: maladie-vasculaire
keyword: medical-imaging
keyword: sdvmheplife-sciences-q-biohuman-health-and-pathology
keyword: selection-de-descripteurs
keyword: theses
keyword: tomographie
keyword: vascular-disease
issued: 2026-05-09T18:59:01.088963
modified: 2026-05-09T18:59:01.088967
language: en
contact point: Organization
  fn: CCSD

test_moissonnage_selune
type: Agent

Methods for automation of vascular lesions detection in computed tomography images
type: Distribution
format: HTML
access URL: https://theses.hal.science/tel-00860825
issued: 2026-05-09T18:59:01.093025
modified: 2026-05-09T18:59:01.072301