Image analysis methods for diagnosis of diffuse lung disease in multi-detector computed tomography
type: CreativeWork
description: Image analysis techniques have been broadly used in computer aided diagnosis tasks in recent years. Computer-aided image analysis is a popular tool in medical imaging research and practice, especially due to the development of different imag- ing modalities and due to the increased volume of image data. Image segmenta- tion, a process that aims at identifying and separating regions of an image, is crucial in many medical applications, such as in identiﬁcation (delineation) of anatomical structures and pathological regions, providing objective quantitative assessment and monitoring of the onset and progression of the disease. Multidetector CT (MDCT) allows acquisition of volumetric datasets with almost isotropic voxels, enabling visualization, characterization and quantiﬁcation of the entire extent of lung anatomy, thus lending itself to characterization of Interstitial Lung Diseases (ILDs), often characterized by non uniform (diffuse) distribution in the lung volume. Interpretation of ILDs is characterized by high inter and intra- observer variability, due to lack of standardized criteria in assessing its complex and variable morphological appearance, further complicated by the increased vol- ume of image data being reviewed. Computer-Aided Diagnosis (CAD) schemes that automatically identify and char- acterize radiologic patterns of ILDs in CT images have been proposed to improve diagnosis and follow-up management decisions. These systems typically consist of two stages. The ﬁrst stage is the segmentation of left and right Lung Parenchyma (LP) region, resulting from lung ﬁeld segmentation and vessel tree removal, while the second stage performs classiﬁcation of LP into normal and abnormal tissue types. The segmentation of Lung Field (LF) and vessel tree structures are crucial preprocessing steps for the subsequent characterization and quantiﬁcation of ILD patterns. Systems proposed for identiﬁcation and quantiﬁcation of ILDpatterns havemainly exploited 2D texture ex…
abstract: Image analysis techniques have been broadly used in computer aided diagnosis tasks in recent years. Computer-aided image analysis is a popular tool in medical imaging research and practice, especially due to the development of different imag- ing modalities and due to the increased volume of image data. Image segmenta- tion, a process that aims at identifying and separating regions of an image, is crucial in many medical applications, such as in identiﬁcation (delineation) of anatomical structures and pathological regions, providing objective quantitative assessment and monitoring of the onset and progression of the disease. Multidetector CT (MDCT) allows acquisition of volumetric datasets with almost isotropic voxels, enabling visualization, characterization and quantiﬁcation of the entire extent of lung anatomy, thus lending itself to characterization of Interstitial Lung Diseases (ILDs), often characterized by non uniform (diffuse) distribution in the lung volume. Interpretation of ILDs is characterized by high inter and intra- observer variability, due to lack of standardized criteria in assessing its complex and variable morphological appearance, further complicated by the increased vol- ume of image data being reviewed. Computer-Aided Diagnosis (CAD) schemes that automatically identify and char- acterize radiologic patterns of ILDs in CT images have been proposed to improve diagnosis and follow-up management decisions. These systems typically consist of two stages. The ﬁrst stage is the segmentation of left and right Lung Parenchyma (LP) region, resulting from lung ﬁeld segmentation and vessel tree removal, while the second stage performs classiﬁcation of LP into normal and abnormal tissue types. The segmentation of Lung Field (LF) and vessel tree structures are crucial preprocessing steps for the subsequent characterization and quantiﬁcation of ILD patterns. Systems proposed for identiﬁcation and quantiﬁcation of ILDpatterns havemainly exploited 2D texture ex…
author: Κορφιάτης, Παναγιώτης
language: en
keywords: Computed tomography, Segmentation, Texture, Image processing, Interstitial lung diseases (ILD), 616.240 752 2, Υπολογιστική τομογραφία, Τμηματοποίηση, Υφή, Επεξεργασία εικόνας, Διάμεση νόσος (ΔΝ)
date published: 2011-10-21T13:12:31Z
date modified: 2026-07-27T20:29:07Z
provider: University of Patras
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Patras

PropertyValue
property ID: Handle
value: https://hdl.handle.net/10889/4778