Methodological Review: Symmetry and asymmetry analysis and its implications to computer-aided diagnosis: A review of the literature <s> Research in brain pathology detection and segmentation <s> Abstract The visualization of 3D phenomena and the extraction of quantitative information from magnetic resonance (MR) image data require efficient semiautomated or automated segmentation techniques. The application of multivariate statistical classification to the segmentation of dual-echo volume data of the human head into tissue types (grey matter, white matter and fluid spaces) is studied in this paper. Tests of the radiometric variability of tissue classes within the data volume demonstrate the improvement of the image acquisition technology and the suitability of statistical methods to perform brain tissue segmentation. Supervised classification is successfully applied to a study of 16 MR volume images of the human head, illustrating the robustness of this method in segmenting brain (white and grey matter) and cerebrospinal fluid (CSF). To avoid subjective criteria involved in the supervised approach, ISODATA clustering as well as clustering based on nonparametric probability density estimation were tested. Both methods performed well (success rates 93.8% and 87.5%, respectively), indicating that the classification procedure can be completely automated. The reproducibility and reliability of supervised and unsupervised classfication were studied by comparing results of segmentation performed by five expert operators. Results suggest that the interoperator and intraoperator variations could be reduced using automated clustering techniques. The accuracy of the volume calculations was quantified by applying the MR imaging and segmentation process to a phantom resembling shape and tissue characteristics of brain tissue. The segmented brain objects are displayed using 3D surface rendering. <s> BIB001 </s> Methodological Review: Symmetry and asymmetry analysis and its implications to computer-aided diagnosis: A review of the literature <s> Research in brain pathology detection and segmentation <s> Supervised segmentation methods from three families of pattern recognition techniques were used to segment multispectral MRI data. Studied were the maximum likelihood method (MLM), k-nearest neighbors (k-NN), and a back-propagation artificial neural net (ANN). Performance was measured in terms of execution speed, and stability for the selection of training data, namely, region of interest (ROI) selection, and interslice and interpatient classifications. MLM proved to have the smallest execution times, but demonstrated the least stability. k-NN showed the best stability for training data selection. To evaluate the segmentation techniques, multispectral images were used of normal volunteers and patients with gliomas, the latter with and without MR contrast material. All measures applied indicated that k-NN provides the best results. <s> BIB002 </s> Methodological Review: Symmetry and asymmetry analysis and its implications to computer-aided diagnosis: A review of the literature <s> Research in brain pathology detection and segmentation <s> This paper has reviewed, with somewhat variable coverage, the nine MR image segmentation techniques itemized in Table II. A wide array of approaches have been discussed; each has its merits and drawbacks. We have also given pointers to other approaches not discussed in depth in this review. The methods reviewed fall roughly into four model groups: c-means, maximum likelihood, neural networks, and k-nearest neighbor rules. Both supervised and unsupervised schemes require human intervention to obtain clinically useful results in MR segmentation. Unsupervised techniques require somewhat less interaction on a per patient/image basis. Maximum likelihood techniques have had some success, but are very susceptible to the choice of training region, which may need to be chosen slice by slice for even one patient. Generally, techniques that must assume an underlying statistical distribution of the data (such as LML and UML) do not appear promising, since tissue regions of interest do not usually obey the distributional tendencies of probability density functions. The most promising supervised techniques reviewed seem to be FF/NN methods that allow hidden layers to be configured as examples are presented to the system. An example of a self-configuring network, FF/CC, was also discussed. The relatively simple k-nearest neighbor rule algorithms (hard and fuzzy) have also shown promise in the supervised category. Unsupervised techniques based upon fuzzy c-means clustering algorithms have also shown great promise in MR image segmentation. Several unsupervised connectionist techniques have recently been experimented with on MR images of the brain and have provided promising initial results. A pixel-intensity-based edge detection algorithm has recently been used to provide promising segmentations of the brain. This is also an unsupervised technique, older versions of which have been susceptible to oversegmenting the image because of the lack of clear boundaries between tissue types or finding uninteresting boundaries between slightly different types of the same tissue. To conclude, we offer some remarks about improving MR segmentation techniques. The better unsupervised techniques are too slow. Improving speed via parallelization and optimization will improve their competitiveness with, e.g., the k-nn rule, which is the fastest technique covered in this review. Another area for development is dynamic cluster validity. Unsupervised methods need better ways to specify and adjust c, the number of tissue classes found by the algorithm. Initialization is a third important area of research. Many of the schemes listed in Table II are sensitive to good initialization, both in terms of the parameters of the design, as well as operator selection of training data.(ABSTRACT TRUNCATED AT 400 WORDS) <s> BIB003 </s> Methodological Review: Symmetry and asymmetry analysis and its implications to computer-aided diagnosis: A review of the literature <s> Research in brain pathology detection and segmentation <s> This article surveys deformable models, a promising and vigorously researched computerassisted medical image analysis technique. Among model-based techniques, deformable models offer a unique and powerful approach to image analysis that combines geometry, physics, and approximation theory. They have proven to be effective in segmenting, matching, and tracking anatomic structures by exploiting (bottom-up) constraints derived from the image data together with (top-down) knowledge about the location, size, and shape of these structures. Deformable models are capable of accommodating the significant variability of biological structures over time and across different individuals. Furthermore, they support highly intuitive interaction mechanisms that, when necessary, allow medical scientists and practitioners to bring their expertise to bear on the model-based image interpretation task. This article reviews the rapidly expanding body of work on the development and application of deformable models to problems of fundamental importance in medical image analysis, including segmentation, shape representation, matching, and motion tracking. <s> BIB004 </s> Methodological Review: Symmetry and asymmetry analysis and its implications to computer-aided diagnosis: A review of the literature <s> Research in brain pathology detection and segmentation <s> We developed a three-dimensional (3D) digitized atlas of the human brain to visualize spatially complex structures. It was designed for use with magnetic resonance (MR) imaging data sets. Thus far, we have used this atlas for surgical planning, model-driven segmentation, and teaching. We used a combination of automated and supervised segmentation methods to define regions of interest based on neuroanatomical knowledge. We also used 3D surface rendering techniques to create a brain atlas that would allow us to visualize complex 3D brain structures. We further linked this Information to script files in order to preserve both spatial information and neuroanatomical knowledge. We present here the application of the atlas for visualization in surgical planning far model-driven segmentation and for the teaching of neuroanatomy. This digitized human brain has the potential to provide important reference information for the planning of surgical procedures. It can also serve as a powerful teaching tool, since spatial relationships among neuroanatomical structures can be more readily envisioned when the user is able to view and rotate the structures in 3D space. Moreover, each element of the brain atlas is associated with a name tag, displayed by a user controlled pointer. The atlas holds a major promise as a template for model-driven segmentation. Using this technique, many regions of interest can be characterized simultaneously on new brain images. <s> BIB005 </s> Methodological Review: Symmetry and asymmetry analysis and its implications to computer-aided diagnosis: A review of the literature <s> Research in brain pathology detection and segmentation <s> Image segmentation plays a crucial role in many medical-imaging applications, by automating or facilitating the delineation of anatomical structures and other regions of interest. We present a critical appraisal of the current status of semi-automated and automated methods for the segmentation of anatomical medical images. Terminology and important issues in image segmentation are first presented. Current segmentation approaches are then reviewed with an emphasis on the advantages and disadvantages of these methods for medical imaging applications. We conclude with a discussion on the future of image segmentation methods in biomedical research. <s> BIB006 </s> Methodological Review: Symmetry and asymmetry analysis and its implications to computer-aided diagnosis: A review of the literature <s> Research in brain pathology detection and segmentation <s> A companion to the Insight Toolkit An introduction to the theory of modern medical image processing, including the analysis of data from - X-ray computer tomography, - magnetic resonance imaging, - nuclear medicine, - and ultrasound. Using an algorithmic approach, and providing the mathematical, statistical, or signal processing as needed for background, the authors describe the principles of all methods implemented in the Insight Toolkit (ITK), a freely available, open- source, object-oriented library. The emphasis is on providing intuitive descriptions of the principles and illustrative examples of results from the leading filtering, segmentation, and registration methods. This book covers the mathematical foundations of important techniques such as: - Statistical pattern recognition, - PDE-based nonlinear image filtering, - Markov random fields, - Level set methods, - Deformable models, - Mutual information, image-based registration - Non-rigid image data fusion With contributions from: Elsa Angelini, Brian Avants, Stephen Aylward, Ting Chen, Jeffrey Duda, Jim Gee, Luis Ibanez, Celina Imielinska, Yinpeng Jin, Jisung Kim, Bill Lorensen, Dimitris Metaxas, Lydia Ng, Punam Saha, George Stetten, Tessa Sundaram, Jay Udupa, Ross Whitaker, Terry Yoo, and Ying Zhuge. The Insight Toolkit is part of the Visible Human Project from the National Library of Medicine, with support from NIDCR, NINDS, NIMH, NEI, NSF, TATRC, NCI, and NIDCD. <s> BIB007 </s> Methodological Review: Symmetry and asymmetry analysis and its implications to computer-aided diagnosis: A review of the literature <s> Research in brain pathology detection and segmentation <s> Abstract This paper presents a new fully automatic model-based segmentation algorithm, which combines level-set methods to model the shape of brain structures and their variation with active appearance modeling to generate images that are used to drive the segmentation. The new algorithm incorporates multi-modality images to improve the segmentation performance and the recursive least square (RLS) algorithm is adopted to minimize the difference between test image and the one synthesized from the shape and appearance modeling. When compared with manual segmentation, the 2D and 3D experiments demonstrate that the new algorithm is computationally efficient and robust and is promising for automatic segmentation of the lateral ventricles. <s> BIB008 </s> Methodological Review: Symmetry and asymmetry analysis and its implications to computer-aided diagnosis: A review of the literature <s> Research in brain pathology detection and segmentation <s> Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets has shown that the new procedure improves the segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies. <s> BIB009
Medical image segmentation, that is a process of identifying and delineating anatomical structures and other objects in medical images, still largely remains an open problem, in spite of several decades of research from various imaging modalities BIB006 . There are many brain segmentation approaches which range from lowlevel image operation such as thresholding, edge detection, mathematical morphology  , to more sophisticated image processing methods such as statistical classification BIB001 BIB002 , active contours BIB004 , level set BIB008 , fuzzy connectedness  , and hybrid segmentation methods BIB007 . A number of review papers BIB003 can be found in this domain. However, clinical image analysis indicates that to successfully differentiate between organ and tumor tissue, image information alone is insufficient  . For example, if a tumor shows inadequate contrast against the healthy brain tissue, the active contour classification can not be achieved without the manual selection of the seeds to initialize segmentation; hence the method is not fully automated. Other statistical classification methods are also limited due to overlapping intensity distributions of healthy tissue, tumor, and surrounding edema. One taxonomy of medical image segmentation techniques can be presented in Fig. 3 . A structure displayed in an image can be segmented and delineated by region-based techniques, boundary-based techniques, or the hybrid of both. Structures that possess discontinuous borders or overlapping internal composition, that are common occurrence in medical images, are likely tocomplicate the segmentation task. Regionbased methods further branch into data-driven methods and knowledge-driven methods. Data-driven methods are often supervised and statistical in nature. In data-driven, unsupervised algorithms, techniques such as thresholding and morphology operator, often work as a complementary tool or are intertwined with supervised statistical classifier. Data-driven methods do not explicitly utilize anatomical knowledge in the segmentation tasks. Knowledge-driven methods, on the other hand, use prior anatomical knowledge about the properties of a structure to guide the segmentation process. These priors are encoded into rules, or transformed into a geographical template (atlas) with tissue labels assigned. For example, in the digital atlas-based segmentation methods BIB005 BIB009 , prior knowledge about normal brain anatomy including the size, shape and location of anatomical structures, is employed. Kaus et al. proposed an adaptive template moderated classification (ATMC) method that combines the statistical classification with anatomical knowledge  . The algorithm involves an iterative process of classification of patient's data and nonlinear registration to match the anatomical templates of a digital atlas with the brain anatomy of the patient. Although knowledge-driven methods have shown many advantages over data-driven methods, and the value of symmetry information in interpreting brain pathologies has been fully recognized, most existent knowledgedriven methods to date, surprisingly, rarely use the knowledge about brain symmetry to their advantages. This is because knowledge representation usually requires manual extraction of meaningful information from a population of data and subsequent data encoding of extracted information into a database. This can be a cumbersome process which demands training of a large set of data. In addition, the characteristics of tumors and other brain pathologies are highly variable and heterogeneous, thus representing prior knowledge is not always possible. Encoding symmetry knowledge about the brain can be further complicated by the normal variance of anatomical asymmetries.