Search results for "Computer vision"
showing 10 items of 2353 documents
E-Fairs: a Cyber-Physical System for Aggregation and Economy of Scale in e-Commerce
2018
In recent years, the e-commerce arena has deeply changed because of the advent of new business models and the growing weight of huge global actors like Amazon. Some business models create competition between users, and the product price tends to rise (e.g., online auctions); other models, including group-buying, make users cooperate, and the price tends to go down. The present study extends the group-buying model and proposes a cyber-physical system called e-fair, in which both sellers and buyers are grouped to negotiate on a specific product or service. E-fairs minimize the global purchase price and the shipping resources respectively with the aggregation of demand and supply as well as or…
Automated approach for indirect immunofluorescence images classification based on unsupervised clustering method
2018
Autoimmune diseases (ADs) are a collection of many complex disorders of unknown aetiology resulting in immune responses to self-antigens and are thought to result from interactions between genetic and environmental factors. ADs collectively are amongst the most prevalent diseases in the U.S., affecting at least 7% of the population. The diagnosis of ADs is very complex, the standard screening methods provides seeking and recognizing of Antinuclear Antibodies (ANA) by Indirect ImmunoFluorescence (IIF) based on HEp-2 cells. In this paper an automatic system able to identify and classify the Centromere pattern is presented. The method is based on the grouping of centromeres present on the cell…
Artificial Neural Network Based Abdominal Organ Segmentations: A Review
2015
There are many neural network based abdominal organ segmentation approaches from medical images. Computed tomography images were mostly used in these approaches. Applied techniques are usually based on prior information regarding position, shape, and size of organs in these methods. In the literature, there are only a few neural network based techniques that were implemented to segment abdominal organs from magnetic resonance based images. In this paper, we present these methods and their results.
Comparative assessment of brain activity during depth perception of stereoscopic and volumetric images
2020
Recent advancements in visualization systems have triggered a growing demand for the objective and accurate comparison of user cognitive requirements when perceiving three-dimensional images demonstrated in different ways. In this work, we present the first comparative assessment of brain activity in subjects viewing stereoscopic images and volumetric images. Electroencephalography was employed to assess the short-term changes in event related potentials and neural oscillations which were further interpreted in terms of cognitive requirements for relative depth judgments. As a result, considerably higher activity have been registered in the beta band and gamma band in case of judging relati…
Segmentation of Positron Emission Tomography Images Using Multi-atlas Anatomical Magnetic Resonance Imaging (MRI)
2021
Positron emission tomography (PET), is a medical imaging technique, it provides information about the body’s cellular function rather than its anatomy. However, due to the functional nature of PET images, locating the anatomical structures in such an image remains a challenging task, indeed, PET images only provide very little anatomical information. Segmentation of PET images, therefore, requires the intervention of a medical expert. The expert proceeds to a manual segmentation of a volume slice by slice, which turns out to be very tedious and costly in terms of time. In this article, we present, evaluate, and make available a multi-atlas approach for automatically segmenting human brain P…
Exploring Frequency-dependent Brain Networks from ongoing EEG using Spatial ICA during music listening
2019
AbstractRecently, exploring brain activity based on functional networks during naturalistic stimuli especially music and video represents an attractive challenge because of the low signal-to-noise ratio in collected brain data. Although most efforts focusing on exploring the listening brain have been made through functional magnetic resonance imaging (fMRI), sensor-level electro- or magnetoencephalography (EEG/MEG) technique, little is known about how neural rhythms are involved in the brain network activity under naturalistic stimuli. This study exploited cortical oscillations through analysis of ongoing EEG and musical feature during free-listening to music. We used a data-driven method t…
Hybrid segmentation and virtual bronchoscopy based on CT images1
2004
Rationale and objectives Introduction of combination of the segmentation tool SegoMeTex and the virtual endoscopy system VIVENDI to perform virtual endoscopic inspections of the human lung. This virtual bronchoscopy system enables visualization of the tracheobronchial tree down to seventh generation. Furthermore, the modified virtual system visualizes hidden structures such as segmented vascular system or tumors. Materials and methods The segmentation is based on image data acquired by a multislice computed tomography scanner. SegoMeTex is used to segment the tracheobronchial tree by a hybrid system with minimal user action. Similarly, the complementary pulmonary arterial can be segmented, …
SIFT Texture Description for Understanding Breast Ultrasound Images
2014
Texture is a powerful cue for describing structures that show a high degree of similarity in their image intensity patterns. This paper describes the use of Self-Invariant Feature Transform (SIFT), both as low-level and high-level descriptors, applied to differentiate the tissues present in breast US images. For the low-level texture descriptors case, SIFT descriptors are extracted from a regular grid. The high-level texture descriptor is build as a Bag-of-Features (BoF) of SIFT descriptors. Experimental results are provided showing the validity of the proposed approach for describing the tissues in breast US images.
Image Processors for Digital Angiography Algorithms and Architectures
1986
After a period of experimental and clinical development,(1–9) digital processing of angiographic X-ray video image sequences is now routinely applied in clinical and research work. The clinical advantages offered by this approach have been discussed in several reports.(10–12) The primary application is the improved visualization of regions of the heart and circulation opacified by X-ray contrast material during angiographic and angiocardiographic examinations. More complex techniques are being developed for improved functional analysis based on digitized angiograms. Technically, the digital techniques also potentially offer improved means of acquiring, storing, and handling images when comp…
A Bottleneck Model of Imaging Systems for Digital Angiocardiography
1988
Some ten years ago, the performance of real-time digital subtraction angiocardiography was demonstrated for the first time [1]. Subtraction became in the years after 1980 nearly synonymous with digital angiography [2–4]. This image enhancement technique was certainly very efficient in paving the way for the digital approach to imaging. Relatively simple processors and memory structures could perform subtraction methods with some degree of success. However, even in that early stage, it was clear to many of those involved in the technical developments and in early clinical evaluations that subtraction was only one of many features that would motivate the change from traditional film technique…