Kluwer - Handbook of Biomedical Image Analysis Vol.2
Preface
In Chapter 1 we present in detail a framework for fully automated brain tissue
classification. The framework consists of a sequence of fully automated state
of the art image registration (both rigid and nonrigid) and image segmentation
algorithms. Models of the spatial distribution of brain tissues are combined with
models of expected tissue intensities, including correction of MR bias fields and
estimation of partial voluming. We also demonstrate how this framework can
be applied in the presence of lesions.
Chapter 2 presents the intravascular ultrasound (IVUS), which is a tomographic
imaging technique that has provided unique tool for observation and supervision
of vessel structures and exact vascular dimensions. In this way, it has
contributed to the better understanding of the coronary content and processes:
vascular remodelling, plaque morphology, and evolution, etc. Most investigators
are convinced that the best way to detect plaque ruptures is by IVUS sequences.
At the same time, cardiologists confirm that due to the “speckle nature” of IVUS
images, conventional IVUS imaging is difficult to clearly diagnose potentially
vulnerable plaques due to the image resolution, lack of contours, speckle motion,
etc. Advanced automatic classification techniques can significantly help
the physicians take decisions about different classes of tissue morphology. The
characterization of tissue and plaque involves different problems. Image feature
space determines the reliable descriptions that should be sufficiently expressive
to capture differences between different classes but at the same time should not
increase unnecessarily the complexity of the classification problem. We consider
and compare a wide set of different feature spaces (Gabor filters, DOG filters,
cooccurrence matrices, binary local patterns, etc). In particular, we show that
the binary local patterns represent an optimal description of ultrasound regions
that at the same time allow real-time processing of images. After reviewing the
IVUS classification works available in the bibliography, we present a comparison
between classical and advanced classification techniques (principal component
analysis, linear discriminant analysis, nonparametric discriminant analysis,
Kernel principal component analysis, Kernel fisher analysis, etc.). The classification
“goodness” of IVUS regions can be significantly improved by applying
multiple classifiers (boosting, adaboost, etc.). The result of the classification
techniques represents a map of classified pixels that still need to be organized
in regions. The technique of snakes (deformable models) is a convenient way to
organize regions of pixels with similar characteristics. Incorporating the classification
map or the likelihood map into the snake framework, allows to organize
pixels into compact image regions representing different plaque zones of IVUS
images.
Chapter 3 is dedicated to functional imaging techniques. The last few decades
of the twentieth century have witnessed significant advances in multidimensional
medical imaging, which enabled us to view noninvasively, the anatomic
structure of internal organs with unprecedented precision and to recognize any
gross pathology of organs and diseases without the need to “open” the body. This
marked a new era of medical diagnostics with many invasive and potentially
morbid procedures being substituted by noninvasive cross-sectional imaging.
Continuing advances in instrumentation and computer technologies also accelerated
the development of various multidimensional imaging modalities that
possess a great potential for providing, in addition to structural information,
dynamic, and functional information on biochemical and pathophysiologic processes
or organs of the human body. There is no doubt that substantial progress
has been achieved in delivering health care more efficiently and in improving
disease management, and that diagnostic imaging techniques have played a decisive
role in routine clinical practice in almost all disciplines of contemporary
medicine. With further development of functional imaging techniques, in conjunction
with continuing progress in molecular biology and functional genomics,
it is anticipated that we will be able to visualize and determine the actual molecular
errors in a specific disease very soon, and be able to incorporate this biological
information into clinical management of that particular group of patients.
This is definitely not achievable with the use of structural imaging techniques.
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*
Preface
In Chapter 1 we present in detail a framework for fully automated brain tissue
classification. The framework consists of a sequence of fully automated state
of the art image registration (both rigid and nonrigid) and image segmentation
algorithms. Models of the spatial distribution of brain tissues are combined with
models of expected tissue intensities, including correction of MR bias fields and
estimation of partial voluming. We also demonstrate how this framework can
be applied in the presence of lesions.
Chapter 2 presents the intravascular ultrasound (IVUS), which is a tomographic
imaging technique that has provided unique tool for observation and supervision
of vessel structures and exact vascular dimensions. In this way, it has
contributed to the better understanding of the coronary content and processes:
vascular remodelling, plaque morphology, and evolution, etc. Most investigators
are convinced that the best way to detect plaque ruptures is by IVUS sequences.
At the same time, cardiologists confirm that due to the “speckle nature” of IVUS
images, conventional IVUS imaging is difficult to clearly diagnose potentially
vulnerable plaques due to the image resolution, lack of contours, speckle motion,
etc. Advanced automatic classification techniques can significantly help
the physicians take decisions about different classes of tissue morphology. The
characterization of tissue and plaque involves different problems. Image feature
space determines the reliable descriptions that should be sufficiently expressive
to capture differences between different classes but at the same time should not
increase unnecessarily the complexity of the classification problem. We consider
and compare a wide set of different feature spaces (Gabor filters, DOG filters,
cooccurrence matrices, binary local patterns, etc). In particular, we show that
the binary local patterns represent an optimal description of ultrasound regions
that at the same time allow real-time processing of images. After reviewing the
IVUS classification works available in the bibliography, we present a comparison
between classical and advanced classification techniques (principal component
analysis, linear discriminant analysis, nonparametric discriminant analysis,
Kernel principal component analysis, Kernel fisher analysis, etc.). The classification
“goodness” of IVUS regions can be significantly improved by applying
multiple classifiers (boosting, adaboost, etc.). The result of the classification
techniques represents a map of classified pixels that still need to be organized
in regions. The technique of snakes (deformable models) is a convenient way to
organize regions of pixels with similar characteristics. Incorporating the classification
map or the likelihood map into the snake framework, allows to organize
pixels into compact image regions representing different plaque zones of IVUS
images.
Chapter 3 is dedicated to functional imaging techniques. The last few decades
of the twentieth century have witnessed significant advances in multidimensional
medical imaging, which enabled us to view noninvasively, the anatomic
structure of internal organs with unprecedented precision and to recognize any
gross pathology of organs and diseases without the need to “open” the body. This
marked a new era of medical diagnostics with many invasive and potentially
morbid procedures being substituted by noninvasive cross-sectional imaging.
Continuing advances in instrumentation and computer technologies also accelerated
the development of various multidimensional imaging modalities that
possess a great potential for providing, in addition to structural information,
dynamic, and functional information on biochemical and pathophysiologic processes
or organs of the human body. There is no doubt that substantial progress
has been achieved in delivering health care more efficiently and in improving
disease management, and that diagnostic imaging techniques have played a decisive
role in routine clinical practice in almost all disciplines of contemporary
medicine. With further development of functional imaging techniques, in conjunction
with continuing progress in molecular biology and functional genomics,
it is anticipated that we will be able to visualize and determine the actual molecular
errors in a specific disease very soon, and be able to incorporate this biological
information into clinical management of that particular group of patients.
This is definitely not achievable with the use of structural imaging techniques.
Download
*