Methodological Review: Symmetry and asymmetry analysis and its implications to computer-aided diagnosis: A review of the literature <s> Research in brain symmetry and asymmetry analysis <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> BIB001 </s> Methodological Review: Symmetry and asymmetry analysis and its implications to computer-aided diagnosis: A review of the literature <s> Research in brain symmetry and asymmetry analysis <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> BIB002 </s> Methodological Review: Symmetry and asymmetry analysis and its implications to computer-aided diagnosis: A review of the literature <s> Research in brain symmetry and asymmetry analysis <s> To improve thediagnostic performance of PETas an aidin evaluating patients suspected ofhawig@4Jzhalmer's disease, we devaloped a fullyautomated method whichgenerates compre hensiveimagepresentations andobjective diagnostic induces. Methods:Ruodne-18-fluorodeoxyglucose PETimage setswere collected from37 patientswithprobable Alzheimer's disease (induding questionable andmilddementia), 22normalsubjects and 5 patientswith cerebrovascular disease.Following star eotectic anatomic standardization, metabolic activityonanmdi vidual'sPETimageset was extracted to a set of predefined surface @s (three-dimensional stereotactic surface projection, 3D-SSP),wh@h was used inthe subsequentanalysis.Anormal database wascreatedby averaging extracted datasets of the normalsubjects. Patients' datasets werecompared indMdually withthenormal database bycalculating a Z-score ona pixel-by pixelbasisandweredisplayed in 3D-SSPviewsfor visualin spections. Diagnostic indiceswerethengenerated basedon averaged Z-scores for the association cortices.Results:Pat temsandseventiesof metabolicreductionin patientswith prob ableAlzheumer's diseasewereseenin the standard 3D-SSP @news of extracted rawdataandstatistical Z-scores. Whendus cnminating patientswfth probable@4Jzhelmer's diseasefrom normalsubjects, diagnostic indicesof the parietalassodation cortexand unilaterallyaveragedparietal-temporal-frontal cortex showed sensitMties of95%and97%,respectively, witha spec @rflcity of 100%.Neitherindexyieldedfalse-positive resultsfor cerebrovascular disease. Conclusion:3D-SSP enables quanti tativedataextraction andreliable localization ofmetabolic abnor malitiesby meansof stereo@iccoordinates. The proposed methodis a promising approach forinterpreting functional brain PETscans. <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 symmetry and asymmetry analysis <s> Without controversy, magnetic resonance (MR) imaging achieves exquisite approximation of gross anatomy. This has been appreciated from the onset of the se of MR technology to image the brain. However, what has been clinically lacking in the first decade of MR use is a rapid yet automated means to quantify various neuroanatomic structures. As such, much of the interpretation of MR imaging has been based primarily on a qualitative rather than quantitative analysis (see Osborn, 1994). <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 symmetry and asymmetry analysis <s> We present a completely automated procedure for measuring left and right hemispheric asymmetry in cerebral grey and white matter volumes from MR images using a chain of state-of-the-art image analysis algorithms. After bias correction and tissue classification, left and right hemispheres are separated by non-rigid registration to a template image in which both hemispheres have been carefully segmented. Volume renderings of each hemisphere separately demonstrate the high quality of the resulting segmentations. Because all steps in the procedure are completely automated and do not require user specified parameters, the results are highly reproducible and consistent. We present quantitative results obtained from a database of MR images of 40 schizophrenic patients and 31 normal controls. <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 symmetry and asymmetry analysis <s> We used 3D texture analysis approach to examine the structural brain asymmetry. The method is based on extended, multi-sort cooccurrence matrices that employ intensity, gradient and anisotropy image features in a uniform way. The asymmetry in normals and in patients with pathological findings was evaluated on a large sample of 310 MRI-TI images with the focus on the asymmetry differences associated with gender. The analysis revealed higher asymmetry in males comparing females (t = 7.23, p < 10/sup -6/ in young and t = 2.65, p = 0.012 in elderly subjects). Separate analysis of white and gray matters showed significant asymmetry increase with age (F = 21.43, p < 10/sup -6/) of gray but not white matter. <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 symmetry and asymmetry analysis <s> We present an improvement of an automated generic methodology for symmetry identification, asymmetry quantification, and segmentation of brain pathologies, utilizing the inherent bi-fold mirror symmetry in brain imagery. In the pipeline of operations starting with detection of the symmetry axis, hemisphere-wise cross registration, statistical correlation and quantification of asymmetries, we segment a target brain pathology. The detection of pathological difference left to right in brain imagery is complicated by normal variations as well as geometric misalignment in anatomical structures between two hemispheres. Introducing hemisphere-wise registration and spatial correlation makes our approach perform robustly in the presence of normal asymmetries and systematic artifacts such as bias field and acquisition noise. <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 symmetry and asymmetry analysis <s> Rationale and Objectives. An automated method for identification and segmentation of acute/subacute ischemic stroke, using the inherent bi-fold symmetry in brain images, is presented. An accurate and automated method for localization of acute ischemic stroke could provide physicians with a mechanism for early detection and potentially faster delivery of effective stroke therapy. Materials and Methods. Segmentation of ischemic stroke was performed on magnetic resonance (MR) images of subacute rodent cerebral ischemia. Eight adult male Wistar rats weighing 225‐300 g were anesthetized with halothane in a mix of 70% nitrous oxide/30% oxygen. Animal core temperature was maintained at 37°C during the entire surgical procedure, including occlusion of the middle cerebral artery (MCA) and the 90-minute post-reperfusion period. To confirm cerebral ischemia, transcranial measurements of cerebral blood flow were performed with laser-Doppler flowmetry, using 15-mm flexible fiberoptic Doppler probes attached to the skull over the MCA territory. Animal MR scans were performed at 1.5 T using a knee coil. Three experts performed manual tracing of the stroke regions for each rat, using the histologicstained slices to guide delineation of stroke regions. A strict tracing protocol was followed that included multiple (three) tracings of each stroke region. The volumetric MR image data were processed for each rat by computing the axis of symmetry and extracting statistical dissimilarities. A nonparametric Wilcoxon rank sum test operating on paired windows in opposing hemispheres identified seeds in the pixels exhibiting statistically significant bi-fold mirror asymmetry. Two brain reference maps were used for analysis: an absolute difference map (ADM) and a statistical difference map (SDM). Although an ADM simply displays the absolute difference by subtracting one brain hemisphere from its reflection, SDM highlights regions by labeling pixels exhibiting statistically significant asymmetry. Results. To assess the accuracy of the proposed segmentation method, the surrogate ground truth (the stroke tracing data) was compared to the results of our proposed automated segmentation algorithm. Three accuracy segmentation metrics were utilized: true-positive volume fraction (TPVF), false-positive volume fraction (FPVF), and false-negative volume fraction (FNVF). The mean value of the TPVF for our segmentation method was 0.8877; 95% CI 0.7254 to 1.0500; the mean FPVF was 0.3370, 95% CI ‐ 0.0893 to 0.7633; the mean FNVF was 0.1122, 95% CI ‐ 0.0502 to 0.2747. <s> BIB008
Human head presents a high level of symmetry; however, it is not perfectly symmetrical. Morphological and functional difference between the hemispheres makes the brain slightly asymmetrical. Different aspects of anatomical asymmetry of human brain have been studied. For example, it is recognized that the right frontal lobe is larger than the left frontal lobe, while the right occipital lobe is smaller than its left counterpart  (see Fig. 1 ). The gross volumetric asymmetries in hemispheres in total, brain compartments, and different intracranial structures were reported in a number of works. For example, 200 normal brains MRI had been studied to contribute to a normative volumetric database and their relations with age and gender were examined BIB004 . It was found that an asymmetry in the depth of central sulcus has its relationship with handedness and gender  . Steinmetz reported a strong correlation between left hemisphere dominance for righthandedness and a large left planum temporal  . Those studies suggested morphological asymmetries is associated with functional variations in human brain populations. On the other hand, some pathology is strongly linked with abnormalities of brain asymmetry. For example, bilateral reduction of metabolic activity in parietal, temporal, and prefrontal regions is known to be an important feature for diagnosing the Alzheimer's disease BIB003 . A method of analysis and visualization of cerebral brain asymmetry was reported in  . Some research group also tried to quantitatively estimate brain asymmetries. In 2004, Lee et al. investigated hemispheric asymmetry and calculated the fractal dimension (FD) of the 3D skeletonized volume, which represented the cortical folding pattern  . They also measured volumes of gray matter and white matter and obtained the hemispheric asymmetries of each measurement. They used MRI dataset from 62 normal adults' brain. This study discovered among normal brains, although hemisphere-wise cortical folding pattern (obtained from FD) presented significant asymmetry, the volumes of WM and GM showed no significant asymmetric changes. In another paper BIB006 , Kovalev et al. investigated structural brain asymmetry in normal volunteers and in patients with pathological findings using 3D volumetric texture analysis. The algorithm is based on multi-sort co-occurrence matrices that employ intensity, gradient and anisotropy image features. By calculating L1 (normalized sum of absolute element-by-element differences) distance, matrices computed for the left and right hemispheres are treated as two feature vectors. It was found that pathological brains are significantly more asymmetric and the variation of asymmetry degree is much wider. It also reported significant gender-related asymmetry difference. Maes et al., measured the hemispheric asymmetry in cerebral grey and white matter volumes from MR images BIB005 . The grey and white matter segmentation was conducted through non-rigid registration with the labeled template image. Their findings indicated a slight difference between grey matter volumes left and right, both in normal and schizophrenics. Their study only intended to compare the volume difference of grey and white matters between left and right, hemispheres, it is not applicable to neuro-images where a large lesion is present. Volkau et al. evaluated asymmetry as a scalar value, using the Jeffery divergence measure  . A threshold is set to T = 0.007 to achieve minimum number of false positives and false negatives. This study demonstrated the significant statistic dissymmetry in normal brains compared to tumor brains; it however may only provide a gross estimation of asymmetries for it only employed one scalar value to represent dissymmetry. The algorithm does not have the capability for localizing pathologies. Thirion et al. stated that ''the quantification of abnormal dissimilarity can be a powerful tool to detect abnormalities"  . They computed a dense 3D dissymmetry fields and compared the relative dissymmetry measure of a patient to a population (see Fig. 4 ). Thus a 3D significance map was acquired. It aimed to answer the question if a brain tumor is significantly more dissymmetrical than the same region in a normal population. This has been the most systematic discussion to date to compare volumetric dissymmetry between abnormal and normal population. It is worthwhile to notice that acquire large sample of normal brains as control for inter-patient fusion may not be straightforward to implement in a clinical setting BIB001 . Liu et al. proposed a statistical method for detection and segmentation of acute/subacute ischemic stroke. This technique has been demonstrated and validated on MR images to detect rodent cerebral ischemic stroke, and the results have been compared with post-mortem histological sections (see Fig. 5 ). They make use of statistical analysis (non-parametric statistical test) on paired windows across hemispheres to identify statistically significant differences between hemispheres BIB008 . It should be noted that, for any of these algorithms, the tolerance to normal asymmetries is critical to the success of abnormal asymmetry detection. Normal anatomical asymmetries between hemispheres can be statistically significant. Slight geographical misalignment between left and right hemispheres and patient-specific asymmetric patterns in regions of the head can complicate the procedure in differentiating pathological asymmetries from anatomical asymmetries. If there appears a non-trivial normal variation between two hemispheres, it is highly likely that the method will detect unwanted artifacts together with pathological asymmetries. In another word, asymmetry analysis as a tool for pathology detection cannot be achieved without differentiating normal asymmetries from abnormal asymmetries. In the rodent ischemia model BIB008 , authors pointed out four major types of asymmetries imbedded in brain radiological images should be taken into considerations: (1) normal asymmetry in the image signal intensity, BIB002 abnormal asymmetry in the image signal intensity, e.g., the presence of tumor; (3) noise due to geometrical misalignment; (4) noise due to in-homogeneity of the signals, e.g., bias fields. In order to eliminate type (1) and type (3) artifacts, a non-rigid registration process between hemispheres is recommended as a preprocessing step BIB007 . By doing so, points formerly misaligned will be mapped to their counterparts by a set of geometric affine transformations. Plus, standard noise deduction algorithm may have the promise to remove errors introduced by type (4) noises. Despite the promising results in the preliminary studies, these techniques are still in its infancy: more solid evaluation is required in order to verify the robustness and efficacy of these algorithms.