Search results for "Artificial Intelligence"
showing 10 items of 6122 documents
Fuzzy C-Means Segmentation on Brain MR Slices Corrupted by RF-Inhomogeneity
2007
Brain MR Images corrupted by RF-Inhomogeneity exhibit brightness variations in such a way that a standard Fuzzy C-Means (fcm) segmentation algorithm fails. As a consequence, modified versions of the algorithm can be found in literature, which take into account the artifact. In this work we show that the application of a suitable pre-processing algorithm, already presented by the authors, followed by a standard fcm segmentation achieves good results also. The experimental results ones are compared with those obtained using SPM5, which can be considered the state of the art algorithm oriented to brain segmentation and bias removal.
Illumination Correction on MR Images
2006
Objective. An important artifact corrupting Magnetic Resonance Images is the rf inhomogeneity, also called bias artifact. This anomaly produces an abnormal illumination fluctuation on the image, due to variations of the device magnetic field. This artifact is particularly strong on images acquired with a device specialized on upper and lower limbs due to their coil configuration. A method based on homomorphic filtering aimed to suppress this artifact was proposed by Guillemaud. This filter has two faults: it doesnt provide an indication about the cutoff frequency (cf) and introduces another illumination artifact on the edges of the foreground. This work is an improvement to this method because i…
Bright Retinal Lesions Detection using Color Fundus Images Containing Reflective Features
2009
Recently, the research community has developed many techniques to detect and diagnose diabetic retinopathy with retinal fundus images. This is a necessary step for the implementation of a large scale screening effort in rural areas where ophthalmologists are not available. In the United States of America, the incidence of diabetes is increasing among the young population. Retina fundus images of patients younger than 20 years old present a high amount of reflectance due to the Nerve Fibre Layer (NFL). Generally, the younger the patient the more the reflectance is visible. We are not aware of algorithms able to explicitly deal with this type of artifact.
Refitting Solutions Promoted by $$\ell _{12}$$ Sparse Analysis Regularizations with Block Penalties
2019
International audience; In inverse problems, the use of an l(12) analysis regularizer induces a bias in the estimated solution. We propose a general refitting framework for removing this artifact while keeping information of interest contained in the biased solution. This is done through the use of refitting block penalties that only act on the co-support of the estimation. Based on an analysis of related works in the literature, we propose a new penalty that is well suited for refitting purposes. We also present an efficient algorithmic method to obtain the refitted solution along with the original (biased) solution for any convex refitting block penalty. Experiments illustrate the good be…
Volumetric Bias Correction
2007
This paper presents a method to suppress the bias artifact, also known as RF-inhomogeneity, in Magnetic Resonance Imaging (MRI). This artifact produces illumination variations due to magnetic field fluctuations of the device. In the latest years many works have been devoted to face this problem. In this work we present the 3D version of a new approach to bias correction, which is called Exponential Entropy Driven Homomorphic Unsharp Masking (E2D-HUM). This technique has been already presented by some of the authors for the 2D case only. The description of the whole method is detailed, and some experimental results are reported.
Online detection and removal of eye blink artifacts from electroencephalogram
2021
Abstract The most prominent type of artifact contaminating electroencephalogram (EEG) signals are the eye blink (EB) artifacts, which could potentially lead to misinterpretation of the EEG signal. Online identification and elimination of eye blink artifacts are crucial in applications such a Brain-Computer Interfaces (BCI), neurofeedback, and epilepsy diagnosis. In this paper, algorithms that combine unsupervised eye blink artifact detection (eADA) with modified Empirical Mode Decomposition (FastEMD) and Canonical Correlation Analysis (CCA) are proposed, i.e., FastEMD-CCA2 and FastCCA, to automatically identify eye blink artifacts and remove them in an online setting. The average accuracy, …
Automated and Online Eye Blink Artifact Removal from Electroencephalogram
2019
Eyeblink artifacts often contaminates electroencephalogram (EEG) signals, which could potentially confound EEG's interpretation. A lot offline methods are available to remove this artifact, but an online solution is required to remove eyeblink artifacts in near real time for EEG signal to be beneficial in applications such as brain computer interface, (BCI). In this work, approaches that combines unsupervised eyeblink artifact detection with Empirical Mode Decomposition (EMD) and Canonical Correlation Analysis (CCA) are proposed to automatically identify eyeblink artifacts and remove them in an online setting. The proposed approaches are analysed and evaluated in terms of artifact removal a…
Unsupervised Eye Blink Artifact Identification in Electroencephalogram
2018
International audience; The most prominent type of artifact contaminating electroencephalogram (EEG) signals is the eye blink (EB) artifact. Hence, EB artifact detection is one of the most crucial pre-processing step in EEG signal processing before this artifact can be removed. In this work, an approach that identifies EB artifacts without human supervision and automated varying threshold setting is proposed and evaluated. The algorithm functions on the basis of correlation between two EEG electrodes, Fp1 and Fp2, followed by EB artifact threshold determination utilizing the amplitude displacement from the mean. The proposed approach is validated and evaluated in terms of accuracy and error…
An offline/real-time artifact rejection strategy to improve the classification of multi-channel evoked potentials
2008
The primary goal of this paper is to improve the classification of multi-channel evoked potentials (EPs) by introducing a temporal domain artifact detection strategy and using this strategy to (a) evaluate how the performance of classifiers is affected by artifacts and (b) show how the performance can be improved by detecting and rejecting artifacts in offline and real-time classification experiments. Using a pattern recognition approach, an artifact is defined in this study as any signal that may lead to inaccurate classifier parameter estimation and inaccurate testing. The temporal domain artifact detection tests include: a within-channel standard deviation (STD) test that can detect sign…
Artificial intelligence techniques for cancer treatment planning
1988
An artificial intelligence system, NEWCHEM, for the development of new oncology therapies is described. This system takes into account the most recent advances in molecular and cellular biology and in cell-drug interaction, and aims to guide experimentation in the design of new optimal protocols. Further work is being carried out, aimed to embody in the system all the basic knowledge of biology, physiopathology and pharmacology, to reason qualitatively from first principles so as to be able to suggest cancer therapies.