Current Issue : January - March Volume : 2014 Issue Number : 1 Articles : 5 Articles
We propose a novel automated algorithm for classifying diagnostic categories of otitis media: acute otitis media, otitis media with\r\neffusion, and no effusion. Acute otitis media represents a bacterial superinfection of the middle ear fluid, while otitis media with\r\neffusion represents a sterile effusion that tends to subside spontaneously. Diagnosing children with acute otitis media is difficult,\r\noften leading to overprescription of antibiotics as they are beneficial only for children with acute otitis media. This underscores the\r\nneed for an accurate and automated diagnostic algorithm. To that end, we design a feature set understood by both otoscopists and\r\nengineers based on the actual visual cues used by otoscopists; we term this the otitis media vocabulary. We also design a process\r\nto combine the vocabulary terms based on the decision process used by otoscopists; we term this the otitis media grammar. The\r\nalgorithm achieves 89.9% classification accuracy, outperforming both clinicians who did not receive special training and state-ofthe-\r\nart classifiers....
Background. Although chick embryogenesis has been studied extensively, there has been growing interest in the investigation\r\nof skeletogenesis. In addition to improved poultry health and minimized economic loss, a greater understanding of skeletal\r\nabnormalities can also have implications for human medicine. True in vivo studies require noninvasive imaging techniques such as\r\nhigh-resolution microCT. However, the manual analysis of acquired images is both time consuming and subjective. Methods. We\r\nhave developed a system for automated image segmentation that entails object-based image analysis followed by the classification\r\nof the extracted image objects. For image segmentation, a rule set was developed using Definiens image analysis software. The\r\nclassification engine was implemented using the WEKA machine learning tool. Results. Our system reduces analysis time and\r\nobserver bias while maintaining high accuracy. Applying the system to the quantification of long bone growth has allowed us\r\nto present the first true in ovo data for bone length growth recorded in the same chick embryos. Conclusions. The procedures\r\ndeveloped represent an innovative approach for the automated segmentation, classification, quantification, and visualization of\r\nmicroCT images. MicroCT offers the possibility of performing longitudinal studies and thereby provides unique insights into the\r\nmorpho- and embryogenesis of live chick embryos....
Segmentation of specular reflections is an essential step in endoscopic image analysis; it affects all further processing steps\r\nincluding segmentation, classification, and registration tasks. The dichromatic reflectance model, which is often used for specular\r\nreflection modeling, is made for dielectric materials and not for human tissue.Hence, most recent segmentation approaches rely on\r\nthresholding techniques. In this work, we first demonstrate the limited accuracy that can be achieved by thresholding techniques\r\nand propose a hybridmethod which is based on closed contours and thresholding. The method has been evaluated on 269 specular\r\nreflections in 49 images which were taken from27 real laparoscopic interventions. Our method improves the average sensitivity by\r\n16% compared to the state-of-the-art thresholding methods....
We present a new methodology based on directional data clustering to represent white matter fiber orientations in magnetic\r\nresonance analyses for high angular resolution diffusion imaging.Aprobabilistic methodology is proposed for estimating intravoxel\r\nprincipal fiber directions, based on clustering directional data arising from orientation distribution function (ODF) profiles. ODF\r\nreconstructions are used to estimate intravoxel fiber directions using mixtures of von Mises-Fisher distributions. The method\r\nfocuses on clustering data on the unit sphere, where complexity arises from representing ODF profiles as directional data. The\r\nproposed method is validated on synthetic simulations, as well as on a real data experiment. Based on experiments, we show that\r\nby clustering profile data using mixtures of vonMises-Fisher distributions it is possible to estimate multiple fiber configurations in\r\na more robust manner than currently used approaches, without recourse to regularization or sharpening procedures.The method\r\nholds promise to support robust tractographic methodologies and to build realistic models of white matter tracts in the human\r\nbrain....
Interpolation has become a default operation in image processing and medical imaging and is one of the important factors in the\r\nsuccess of an intensity-based registration method. Interpolation is needed if the fractional unit ofmotion is not matched and located\r\non the high resolution (HR) grid. The purpose of this work is to present a systematic evaluation of eight standard interpolation\r\ntechniques (trilinear, nearest neighbor, cubic Lagrangian, quintic Lagrangian, hepatic Lagrangian, windowed Sinc, B-spline 3rd\r\norder, and B-spline 4th order) and to compare the effect of cost functions (least squares (LS), normalized mutual information\r\n(NMI), normalized cross correlation (NCC), and correlation ratio (CR)) for optimized automatic image registration (OAIR) on 3D\r\nspoiled gradient recalled (SPGR) magnetic resonance images (MRI) of the brain acquired using a 3T GE MR scanner. Subsampling\r\nwas performed in the axial, sagittal, and coronal directions to emulate three low resolution datasets. Afterwards, the low resolution\r\ndatasets were upsampled using different interpolation methods, and they were then compared to the high resolution data.Themean\r\nsquared error, peak signal to noise, joint entropy, and cost functions were computed for quantitative assessment of the method.\r\nMagnetic resonance image scans and joint histogram were used for qualitative assessment of the method....
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