Search results for " computer vision."
showing 10 items of 347 documents
Automatic Classification of Bright Retinal Lesions via Deep Network Features
2018
The diabetic retinopathy is timely diagonalized through color eye fundus images by experienced ophthalmologists, in order to recognize potential retinal features and identify early-blindness cases. In this paper, it is proposed to extract deep features from the last fully-connected layer of, four different, pre-trained convolutional neural networks. These features are then feeded into a non-linear classifier to discriminate three-class diabetic cases, i.e., normal, exudates, and drusen. Averaged across 1113 color retinal images collected from six publicly available annotated datasets, the deep features approach perform better than the classical bag-of-words approach. The proposed approaches…
Complete End-To-End Low Cost Solution To a 3D Scanning System with Integrated Turntable
2017
3D reconstruction is a technique used in computer vision which has a wide range of applications in areas like object recognition, city modelling, virtual reality, physical simulations, video games and special effects. Previously, to perform a 3D reconstruction, specialized hardwares were required. Such systems were often very expensive and was only available for industrial or research purpose. With the rise of the availability of high-quality low cost 3D sensors, it is now possible to design inexpensive complete 3D scanning systems. The objective of this work was to design an acquisition and processing system that can perform 3D scanning and reconstruction of objects seamlessly. In addition…
Dimensionality Reduction via Regression in Hyperspectral Imagery
2015
This paper introduces a new unsupervised method for dimensionality reduction via regression (DRR). The algorithm belongs to the family of invertible transforms that generalize Principal Component Analysis (PCA) by using curvilinear instead of linear features. DRR identifies the nonlinear features through multivariate regression to ensure the reduction in redundancy between he PCA coefficients, the reduction of the variance of the scores, and the reduction in the reconstruction error. More importantly, unlike other nonlinear dimensionality reduction methods, the invertibility, volume-preservation, and straightforward out-of-sample extension, makes DRR interpretable and easy to apply. The pro…
Fast PET Scan Tumor Segmentation Using Superpixels, Principal Component Analysis and K-Means Clustering
2018
Positron Emission Tomography scan images are extensively used in radiotherapy planning, clinical diagnosis, assessment of growth and treatment of a tumor. These all rely on fidelity and speed of detection and delineation algorithm. Despite intensive research, segmentation remained a challenging problem due to the diverse image content, resolution, shape, and noise. This paper presents a fast positron emission tomography tumor segmentation method in which superpixels are extracted first from the input image. Principal component analysis is then applied on the superpixels and also on their average. Distance vector of each superpixel from the average is computed in principal components coordin…
Myocardial Infarction Quantification from Late Gadolinium Enhancement MRI Using Top-Hat Transforms and Neural Networks
2019
Significance: Late gadolinium enhanced magnetic resonance imaging (LGE-MRI) is the gold standard technique for myocardial viability assessment. Although the technique accurately reflects the damaged tissue, there is no clinical standard for quantifying myocardial infarction (MI), demanding most algorithms to be expert dependent. Objectives and Methods: In this work a new automatic method for MI quantification from LGE-MRI is proposed. Our novel segmentation approach is devised for accurately detecting not only hyper-enhanced lesions, but also microvascular-obstructed areas. Moreover, it includes a myocardial disease detection step which extends the algorithm for working under healthy scans.…
Domain-specific transfer learning in the automated scoring of tumor-stroma ratio from histopathological images of colorectal cancer
2023
Tumor-stroma ratio (TSR) is a prognostic factor for many types of solid tumors. In this study, we propose a method for automated estimation of TSR from histopathological images of colorectal cancer. The method is based on convolutional neural networks which were trained to classify colorectal cancer tissue in hematoxylin-eosin stained samples into three classes: stroma, tumor and other. The models were trained using a data set that consists of 1343 whole slide images. Three different training setups were applied with a transfer learning approach using domain-specific data i.e. an external colorectal cancer histopathological data set. The three most accurate models were chosen as a classifie…
Local electrical characterisation of human atrial fibrillation
2002
The rate of success of radio-frequency catheter ablation in the treatment of atrial fibrillation may be significantly improved by evaluating the local electrical properties of the atrial tissue. The aim of this study is the development of an automatic procedure for the characterisation of the local electrical activity during atrial fibrillation and the comparison of its performance with the manual analysis. The adopted procedures were the semi-automatic measurement of the local fibrillation intervals (A-A intervals) and the manual electrogram classification following the criteria suggested by Wells (1978) or Konings (1997). Two methods have been used: Principal Component Analysis and Cluste…
Testing the X-IFU calibration requirements: an example for quantum efficiency and energy resolution
2018
With its array of 3840 Transition Edge Sensors (TESs) operated at 90 mK, the X-Ray Integral Field Unit (X-IFU) on board the ESA L2 mission Athena will provide spatially resolved high-resolution spectroscopy (2.5 eV FWHM up to 7 keV) over the 0.2 to 12 keV bandpass. The in-flight performance of the X-IFU will be strongly affected by the calibration of the instrument. Uncertainties in the knowledge of the overall system, from the filter transmission to the energy scale, may introduce systematic errors in the data, which could potentially compromise science objectives - notably those involving line characterisation e.g. turbulence velocity measurements - if not properly accounted for. Defining…
The performance of the ATHENA X-ray Integral Field Unit
2018
The X-ray Integral Field Unit (X-IFU) is a next generation microcalorimeter planned for launch onboard the Athena observatory. Operating a matrix of 3840 superconducting Transition Edge Sensors at 90 mK, it will provide unprecedented spectro-imaging capabilities (2.5 eV resolution, for a field of view of 5') in the soft X-ray band (0.2 up to 12 keV), enabling breakthrough science. The definition of the instrument evolved along the phase A study and we present here an overview of its predicted performances and their modeling, illustrating how the design of the X-IFU meets its top-level scientific requirements. This article notably covers the energy resolution, count-rate capability, quantum …
Comments on “Measurement of dimensionless Chezy coefficient in step-pool reach (Case study of Dizin River in Iran)” by Torabizadeh A., Tahershamsi A.…
2018
This paper is a comment on a previous published paper.