Methodological Review: Symmetry and asymmetry analysis and its implications to computer-aided diagnosis: A review of the literature <s> Introduction <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> BIB001 </s> Methodological Review: Symmetry and asymmetry analysis and its implications to computer-aided diagnosis: A review of the literature <s> Introduction <s> A system that automatically segments and labels glioblastoma-multiforme tumors in magnetic resonance images (MRIs) of the human brain is presented. The MRIs consist of T1-weighted, proton density, and T2-weighted feature images and are processed by a system which integrates knowledge-based (KB) techniques with multispectral analysis. Initial segmentation is performed by an unsupervised clustering algorithm. The segmented image, along with cluster centers for each class are provided to a rule-based expert system which extracts the intracranial region. Multispectral histogram analysis separates suspected tumor from the rest of the intracranial region, with region analysis used in performing the final tumor labeling. This system has been trained on three volume data sets and tested on thirteen unseen volume data sets acquired from a single MRI system. The KB tumor segmentation was compared with supervised, radiologist-labeled "ground truth" tumor volumes and supervised K-nearest neighbors tumor segmentations. The results of this system generally correspond well to ground truth, both on a per slice basis and more importantly in tracking total tumor volume during treatment over time. <s> BIB002 </s> Methodological Review: Symmetry and asymmetry analysis and its implications to computer-aided diagnosis: A review of the literature <s> Introduction <s> We describe an automated 3-D segmentation system for in vivo brain magnetic resonance images (MRI). Our segmentation method combines a variety of filtering, segmentation, and registration techniques and makes maximum use of the available a priori biomedical expertise, both in an implicit and an explicit form. We approach the issue of boundary finding as a process of fitting a group of deformable templates (simplex mesh surfaces) to the contours of the target structures. These templates evolve in parallel, supervised by a series of rules derived from analyzing the template's dynamics and from medical experience. The templates are also constrained by knowledge on the expected textural and shape properties of the target structures. We apply our system to segment four brain structures (corpus callosum, ventricles, hippocampus, and caudate nuclei) and discuss its robustness to imaging characteristics and acquisition noise. <s> BIB003 </s> Methodological Review: Symmetry and asymmetry analysis and its implications to computer-aided diagnosis: A review of the literature <s> Introduction <s> BACKGROUND ::: The quality of medical reports on diagnostic procedures has a considerable impact on the quality of medical care. Handwritten or otherwise unstructured reports tend to be incomplete, whereas structured questionnaires are of limited flexibility and not considered case-adequate. Thus, medical reports of this kind may promote an incomplete and misleading documentation and, therefore, be problematic with respect to their reliability. ::: ::: ::: METHODS ::: SonoConsult (SC), an expert system for structured and case-adequate documentation of sonographic findings with an additional diagnostic component, was evaluated with respect to user acceptance and suitability for enhancing the quality of reports and supporting sonographic beginners. The expectations and the attitudes of the users toward the program were evaluated by anonymous questionnaires. The documentation of findings and the diagnostic conclusions in 103 free text reports made by experienced examiners were evaluated by subjecting their information to a subsequent input into SC. Free text reports were checked for information that was asked by SC but not mentioned in the reports. In a series of 150 cases, the system diagnoses were blinded during input of findings into SC-questionnaires and the examiners' diagnostic conclusions were compared with the uncovered SC-diagnoses with respect to forgotten diagnoses. ::: ::: ::: RESULTS ::: The structured and data-driven acquisition of information by the program was well accepted by the users. However, only a medium interest in the system-delivered diagnoses was noted. The program-generated reports were characterized by a more detailed description of the findings and a higher number of diagnoses in comparison to the unstructured reports before introduction of SC as the only documentation system. When unaware of the system diagnoses, information was entered into the questionnaires, and SC generated some diagnoses which were not mentioned by the examiners in their conclusions. The possibility to inspect the system diagnoses led to an enhancement of the number of diagnoses the examiners mentioned in their conclusions. By contrast, the examiners meant that the influence of the program on their conclusions was minimal or dispensable. Beginners in sonography acknowledged that the program led them to perform a complete examination in an adequate sequence. ::: ::: ::: CONCLUSION ::: An expert system for the data-driven, case-adequate information acquisition of abdominal ultrasound examinations may enhance the quality of the reports and, potentially, of the examinations at the same time. In addition, it may help beginners to learn a structured problem- and finding-adequate examination sequence. <s> BIB004
The advances in computer technologies, over the last decade or so, are catalyzing the development of modern computerized schemes for lesion detection in radiological images. One major challenge, however, is that computers generally lack sufficient perceptibility and intelligence in terms of discovering pathological patterns, which hinders the decision making process. Since it is known that anatomical knowledge plays an indispensable role in computer vision and artificial intelligence BIB002 BIB001 BIB003 , integrating anatomical knowledge into the computer system holds great promise for facilitating decision making and improving patient care. Based on the assumption that the brain exhibits a high level of bi-fold symmetry ( Fig. 1 ) and that this symmetry is violated in the presence of pathological conditions, many researchers have been motivated to construct a symmetry-based paradigm for automatic localization and segmentation of brain lesions. The framework of this methodology is based on the hypothesis that the systematic correlation between asymmetry and pathologies can be a key to the improvement of existing detection algorithms. Integrating symmetry and asymmetry information as the prior knowledge or heuristics into a computer-aided diagnostic (CAD)  BIB004  system, ought to enhance the system performance in the analysis of brain pathologies. To correctly quantify asymmetric patterns in brain images, however, the symmetry axis, or the symmetry plane, needs to be appropriately oriented in space. This enables the system to correct the possible misalignment of radiological scans, and to evaluate hemisphere-wise asymmetry. Therefore, this review paper has two main focuses. In the first part, the existing state-of-the-art methodologies for identifying the symmetry axis/plane of a given set of brain images will be investigated. In the second part, the discriminating capacity of symmetry/asymmetry in the context of extracting pathological findings in various radiological applications will be explored. In other words, to achieve the goal of using asymmetry as a pathologic index to assist CAD, we need to first solve an image registration problem, followed by a pattern recognition and segmentation problem. With respect to research significance and clinical significance, the ideas discussed in this paper, for a set of particular neuro-applications, might have much more general applicability for CAD in many other highly symmetrical parts of the human body, including the breasts and the limbs; more details can be found in Section 6.