| Περίληψη: | 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.
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