Image analysis methods for diagnosis of diffuse lung disease in multi-detector computed tomography
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 extraction techniques, while only a few have investigated 3D texture features. Speciﬁcally, texture feature extraction methods that have been exploited towards lung parenchyma analysis are: ﬁrst order statistics, grey level co-occurrence matrices, gray level run length matrices, histogram signatures and fractals. The identiﬁcation and quantiﬁcation of lung parenchyma into normal and abnormal tissue type has been achieved by means of supervised classiﬁcation tech- niques (e.g. Artiﬁcial Neural Networks, ANN, Bayesian classiﬁer, linear discrimi- nant analysis (LDA) and k-Nearest Neighboor (k-NN). However, the previously proposed identiﬁcation and quantiﬁcation schemes in- corporate preprocessing segmentation algorithms, effective on normal patient data. In addition the effect of the preprocessing stages (i.e. segmentation of LF and ves- sel tree structures) on the performance of ILD characterization and quantiﬁcation schemes has not been investigated. Finally, the complex interaction of such automated schemes with the radiologists remains an open issue. The current thesis deals with identiﬁcation and quantiﬁcation of ILD in lung CT. The thesis aims at optimizing all major steps encountered in a computer aided ILD quantiﬁcation scheme, by exploiting 3D texture feature extraction techniques and supervised and unsupervised pattern classiﬁcation schemes to derive 3D disease segments. The speciﬁc objectives of the current thesis are focused on: • Development of LF segmentation algorithms adapted to pathology. • Development of vessel tree segmentation adapted to presence of pathology. • Development of ILD identiﬁcation and quantiﬁcation algorithms. • Investigation of the interaction of an ILD identiﬁcation and quantiﬁcation scheme with the radiologist, by an interactive image editing tool.
Computed tomography, Segmentation, Texture, Image processing, Interstitial lung diseases (ILD), 616.240 752 2, Υπολογιστική τομογραφία, Τμηματοποίηση, Υφή, Επεξεργασία εικόνας, Διάμεση νόσος (ΔΝ)
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