Methodological Review: Symmetry and asymmetry analysis and its implications to computer-aided diagnosis: A review of the literature <s> Neuro-images: misalignment in clinical settings <s> This paper focuses on the evaluation of an ideal midsagittal plane (iMSP) extraction algorithm. The algorithm was developed for capturing the iMSP from 3D normal and pathological neural images. The main challenges are the drastic structural asymmetry that often exists in pathological brains, and the sparse, nonisotropic data sampling that is common in clinical practice. A simple edge-based, cross-correlation approach is presented that decomposes the iMSP extraction problem into discovery of symmetry axes from 2D slices, followed by robust estimation of 3D plane parameters. The algorithm’s tolerance to brain asymmetries, input image offsets and image noise is quantitatively measured. It is found that the algorithm can extract the iMSP from input 3D images with (1) large asymmetrical lesions; (2) arbitrary initial yaw and roll angle errors; and (3) low signal-to-noise level. Also, no significant difference is found between the iMSP computed by the algorithm and the midsagittal plane estimated by two trained neuroradiologists. <s> BIB001 </s> Methodological Review: Symmetry and asymmetry analysis and its implications to computer-aided diagnosis: A review of the literature <s> Neuro-images: misalignment in clinical settings <s> We present a new method to automatically compute, reorient, and recenter the mid-sagittal plane in anatomical and functional three-dimensional (3-D) brain images. This iterative approach is composed of two steps. At first, given an initial guess of the mid-sagittal plane (generally, the central plane of the image grid), the computation of local similarity measures between the two sides of the head allows to identify homologous anatomical structures or functional areas, by way of a block matching procedure. The output is a set of point-to-point correspondences: the centers of homologous blocks. Subsequently, we define the mid-sagittal plane as the one best superposing the points on one side and their counterparts on the other side by reflective symmetry. Practically, the computation of the parameters characterizing the plane is performed by a least trimmed squares estimation. Then, the estimated plane is aligned with the center of the image grid, and the whole process is iterated until convergence. The robust estimation technique we use allows normal or abnormal asymmetrical structures or areas to be treated as outliers, and the plane to be mainly computed from the underlying gross symmetry of the brain. The algorithm is fast and accurate, even for strongly tilted heads, and even in presence of high acquisition noise and bias field, as shown on a large set of synthetic data. The algorithm has also been visually evaluated on a large set of real magnetic resonance (MR) images. We present a few results on isotropic as well as anisotropic anatomical (MR and computed tomography) and functional (single photon emission computed tomography and positron emission tomography) real images, for normal and pathological subjects. <s> BIB002 </s> Methodological Review: Symmetry and asymmetry analysis and its implications to computer-aided diagnosis: A review of the literature <s> Neuro-images: misalignment in clinical settings <s> We propose a novel method to automatically compute the symmetry plane and correct the 3D orientation of patient brain images. Many images of the brain are clinically unreadable because of the misalignment of the patient’s head in the scanner. We proposed an algorithm that represents the brain volume as a re-parameterized surface point cloud where each location has been parameterized by its elevation (latitude), azimuth (longitude) and radius. The removal of the interior contents of the brain makes this approach perform robustly in the presence of the brain pathologies e.g. tumor, stroke and bleed. Thus, we decompose the symmetry plane computation problem into a surface matching routine. The search for the best matching surface is implemented in a multi-resolution paradigm so as to decrease computational time considerably. Spatial affine transform then is performed to rotate the 3D brain images and align them within the coordinate system of the scanner. The corrected brain volume is re-sliced such that each planar image represents the brain at the same axial level. <s> BIB003
A common phenomenon in radiological scanning is that many neuro-imaging devices produce disoriented brain images; the scanned brain images are somewhat tilted and distorted BIB001 . Tilt and distortion can mislead visual inspection, and often yield false clinical interpretation, since slices of the brain images are no longer representing homologous structures within the same coronal or axial level BIB002 . The tilt of the head is often observed in the device during the scanning process, however, is not always tractable. Common reasons include, but are not limited to, immobility of patients, inexperience of the technicians, and imprecision of calibration systems BIB003 . Correcting the tilt of the head is equivalent to realigning the mid-sagittal plane (MSP) with the center of the image lattice. The MSP is defined as the plane that best separates the brain into two halves BIB002 . It is evident that the re-adjustment of the MSP from the geometrical misrepresentation yields more sensible data assessment either by a human expert or a computer program that is based upon hemisphere-wise cross referencing.