0000000000360022

AUTHOR

Ahmed Ben Hamida

showing 2 related works from this author

Factor analysis-based approach for early uptake automatic quantification of breast cancer by 18F-FDG PET images sequence

2014

International audience; Factor Analysis of Medical Image Sequences (FAMIS) is recognized as one pioneer successfully used approach for analyzing especially dynamic images' sequence for estimating kinetics and associated compartments having a physiological meaning. Some studies tried to extend the exploring of this approach to analyze Positron Emission Tomography (PET) image modality for dynamic sequences. PET images with 18F-fluorodesoxyglucose (18F-FDG) is the gold standard for in vivo, evaluation of tumor glucose metabolism and is widely used in clinical oncology. In this paper, a novel approach is proposed to obtain an automated quantification method for early accumulation of 18F-FDG tra…

Health InformaticsStandardized uptake value[SDV.IB.MN]Life Sciences [q-bio]/Bioengineering/Nuclear medicine[SDV.IB.MN] Life Sciences [q-bio]/Bioengineering/Nuclear medicine[ SDV.IB.MN ] Life Sciences [q-bio]/Bioengineering/Nuclear medicine030218 nuclear medicine & medical imaging18f fdg pet03 medical and health sciencessymbols.namesake0302 clinical medicineBreast cancermedicineComputingMilieux_MISCELLANEOUSSequencemedicine.diagnostic_testbusiness.industryCancerPattern recognitionGold standard (test)medicine.diseasePearson product-moment correlation coefficient3. Good healthPositron emission tomography030220 oncology & carcinogenesisSignal ProcessingsymbolsArtificial intelligencebusinessNuclear medicineBiomedical Signal Processing and Control
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A classification approach to prostate cancer localization in 3T Multi-Parametric MRI

2016

International audience; Multiparametric-magnetic resonance imaging (mp-MRI) has demonstrated, in many studies, its potential in prostate cancer detection and analysis. We propose a supervised classification approach based on mp-MRI data base of 20 patients, in order to localize prostate cancer and to achieve a cartographic representation of the prostate voxels based on classification results. Proposed method provides a computer aided detection (CAD) software for prostatic cancer. For that, we have extracted varied features providing functional, anatomical and metabolic information helping the classifier to distinguish between three different classes ("Healthy", "Benign" and "Pathologic"). W…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[SPI] Engineering Sciences [physics][INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputer scienceSVMFeature extractionWord error ratecomputer.software_genre030218 nuclear medicine & medical imaging03 medical and health sciencesProstate cancer[SPI]Engineering Sciences [physics]0302 clinical medicine[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingProstateVoxelmedicine[ SPI ] Engineering Sciences [physics]Computer visionProstate cancermedicine.diagnostic_testbusiness.industryPattern recognitionMagnetic resonance imagingSpectramedicine.disease3. Good healthRandom forestSupport vector machinemedicine.anatomical_structuremp-MRIArtificial intelligencebusinesscomputer030217 neurology & neurosurgeryRandom forest
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