Search results for "Computer Vision and Pattern Recognition"
showing 10 items of 997 documents
The fundamental theory of optimal "Anti-Bayesian" parametric pattern classification using order statistics criteria
2013
Author's version of an article in the journal: Pattern Recognition. Also available from the publisher at: http://dx.doi.org/10.1016/j.patcog.2012.07.004 The gold standard for a classifier is the condition of optimality attained by the Bayesian classifier. Within a Bayesian paradigm, if we are allowed to compare the testing sample with only a single point in the feature space from each class, the optimal Bayesian strategy would be to achieve this based on the (Mahalanobis) distance from the corresponding means. The reader should observe that, in this context, the mean, in one sense, is the most central point in the respective distribution. In this paper, we shall show that we can obtain opti…
A supervised learning framework of statistical shape and probability priors for automatic prostate segmentation in ultrasound images
2013
Prostate segmentation aids in prostate volume estimation, multi-modal image registration, and to create patient specific anatomical models for surgical planning and image guided biopsies. However, manual segmentation is time consuming and suffers from inter-and intra-observer variabilities. Low contrast images of trans rectal ultrasound and presence of imaging artifacts like speckle, micro-calcifications, and shadow regions hinder computer aided automatic or semi-automatic prostate segmentation. In this paper, we propose a prostate segmentation approach based on building multiple mean parametric models derived from principal component analysis of shape and posterior probabilities in a multi…
Discrimination of retinal images containing bright lesions using sparse coded features and SVM
2015
Diabetic Retinopathy (DR) is a chronic progressive disease of the retinal microvasculature which is among the major causes of vision loss in the world. The diagnosis of DR is based on the detection of retinal lesions such as microaneurysms, exudates and drusen in retinal images acquired by a fundus camera. However, bright lesions such as exudates and drusen share similar appearances while being signs of different diseases. Therefore, discriminating between different types of lesions is of interest for improving screening performances. In this paper, we propose to use sparse coding techniques for retinal images classification. In particular, we are interested in discriminating between retina…
Synergy of features enables detection of texture defined figures
2006
Traditional theories of early visual processing suggest that elementary visual features are handled in parallel by independent neural pathways. We studied the interaction of orientation and spatial frequency in the discrimination of Gabor random fields. Target textures differed from reference textures either in mean feature value, showing an edge-like transition between both textures (edge defined), or in the degree of feature homogeneity with smooth transitions (region defined). Irrespective of the kind of texture definition, we found strong cue summation for targets defined by both cues simultaneously, provided two conditions were fulfilled. First, they were barely discriminable when defi…
A method for automatic forensic facial reconstruction based on dense statistics of soft tissue thickness.
2019
In this paper, we present a method for automated estimation of a human face given a skull remain. The proposed method is based on three statistical models. A volumetric (tetrahedral) skull model encoding the variations of different skulls, a surface head model encoding the head variations, and a dense statistic of facial soft tissue thickness (FSTT). All data are automatically derived from computed tomography (CT) head scans and optical face scans. In order to obtain a proper dense FSTT statistic, we register a skull model to each skull extracted from a CT scan and determine the FSTT value for each vertex of the skull model towards the associated extracted skin surface. The FSTT values at p…
Deep Learning for fully automatic detection, segmentation, and Gleason Grade estimation of prostate cancer in multiparametric Magnetic Resonance Imag…
2021
The emergence of multi-parametric magnetic resonance imaging (mpMRI) has had a profound impact on the diagnosis of prostate cancers (PCa), which is the most prevalent malignancy in males in the western world, enabling a better selection of patients for confirmation biopsy. However, analyzing these images is complex even for experts, hence opening an opportunity for computer-aided diagnosis systems to seize. This paper proposes a fully automatic system based on Deep Learning that takes a prostate mpMRI from a PCa-suspect patient and, by leveraging the Retina U-Net detection framework, locates PCa lesions, segments them, and predicts their most likely Gleason grade group (GGG). It uses 490 mp…
Contrast sensitivity of the visual system in speckle imagery
1994
The contrast sensitivity function (CSF) of the whole visual system is determined with the use of coherent diffuse illumination. This function provides supplementary data about the effect of speckle on the ability of the visual system to perceive the spatial information contained in an image. The results show that speckle not only prevents perception of the finest details (highest frequencies) but also reduces the visibility of lower frequencies (especially where contrast is low). The difference between the CSF's determined with and without speckle is quantitatively very important. And the ratio between the two CSF's is a measure of the retinal ability to perceive contrast in the presence of…
A spline-based non-linear diffeomorphism for multimodal prostate registration.
2012
This paper presents a novel method for non-rigid registration of transrectal ultrasound and magnetic resonance prostate images based on a non-linear regularized framework of point correspondences obtained from a statistical measure of shape-contexts. The segmented prostate shapes are represented by shape-contexts and the Bhattacharyya distance between the shape representations is used to find the point correspondences between the 2D fixed and moving images. The registration method involves parametric estimation of the non-linear diffeomorphism between the multimodal images and has its basis in solving a set of non-linear equations of thin-plate splines. The solution is obtained as the least…
Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge
2014
Contains fulltext : 137969.pdf (Publisher’s version ) (Open Access) Prostate MRI image segmentation has been an area of intense research due to the increased use of MRI as a modality for the clinical workup of prostate cancer. Segmentation is useful for various tasks, e.g. to accurately localize prostate boundaries for radiotherapy or to initialize multi-modal registration algorithms. In the past, it has been difficult for research groups to evaluate prostate segmentation algorithms on multi-center, multi-vendor and multi-protocol data. Especially because we are dealing with MR images, image appearance, resolution and the presence of artifacts are affected by differences in scanners and/or …
Visual acuity and color discrimination in patients with cataracts.
2020
Color vision tests can give information about pathological changes in eye structures. The purpose of our research was to study the color vision sensitivity and visual acuity changes before and after cataract surgery. We used a saturated Farnsworth D15 color vision arrangement test to check color sensitivity changes in confusion line directions. The test is easily perceptible (essential to eldery patients), and it is possible to check color sensitivity changes in tritan, protan, and deutan confusion line directions. The results were analyzed in several ways: by summing the color differences between adjacent caps according to Bowman and averaging the color difference vectors according to Ving…