Search results for "46 Information and Computing Sciences"

showing 2 items of 2 documents

A multimodal retina-iris biometric system using the Levenshtein distance for spatial feature comparison

2020

Abstract The recent developments of information technologies, and the consequent need for access to distributed services and resources, require robust and reliable authentication systems. Biometric systems can guarantee high levels of security and multimodal techniques, which combine two or more biometric traits, warranting constraints that are more stringent during the access phases. This work proposes a novel multimodal biometric system based on iris and retina combination in the spatial domain. The proposed solution follows the alignment and recognition approach commonly adopted in computational linguistics and bioinformatics; in particular, features are extracted separately for iris and…

Biometric systemComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONspatial domain biometric featuresbiometric authentication system4603 Computer Vision and Multimedia Computation46 Information and Computing SciencesmedicineIris (anatomy)multimodal systemRetinabusiness.industrymultimodal retina-iris biometric systemLevenshtein distancePattern recognitionbiometric recognition systemQA75.5-76.95Levenshtein distanceretina and iris featuresmedicine.anatomical_structureFeature (computer vision)Electronic computers. Computer scienceSignal ProcessingComputer Vision and Pattern RecognitionArtificial intelligencebusinessSoftware
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CNN-Based Prostate Zonal Segmentation on T2-Weighted MR Images: A Cross-Dataset Study

2020

Prostate cancer is the most common cancer among US men. However, prostate imaging is still challenging despite the advances in multi-parametric magnetic resonance imaging (MRI), which provides both morphologic and functional information pertaining to the pathological regions. Along with whole prostate gland segmentation, distinguishing between the central gland (CG) and peripheral zone (PZ) can guide toward differential diagnosis, since the frequency and severity of tumors differ in these regions; however, their boundary is often weak and fuzzy. This work presents a preliminary study on deep learning to automatically delineate the CG and PZ, aiming at evaluating the generalization ability o…

Urologic DiseasesComputer scienceContext (language use)32 Biomedical and Clinical Sciences-Convolutional neural networkDeep convolutional neural networks Prostate zonal segmentation Cross-dataset generalizationProstate cancer46 Information and Computing SciencesProstateDeep convolutional neural networksmedicineAnatomical MRISegmentationProstate zonal segmentation; Prostate cancer; Anatomical MRI; Deep convolutional neural networks; Cross-dataset generalization;3202 Clinical SciencesCancerSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniProstate cancerSettore INF/01 - Informaticamedicine.diagnostic_testbusiness.industryDeep learningINF/01 - INFORMATICAMagnetic resonance imagingPattern recognitionmedicine.disease3211 Oncology and Carcinogenesismedicine.anatomical_structureCross-dataset generalizationProstate zonal segmentationBiomedical ImagingArtificial intelligenceDeep convolutional neural networkbusinessT2 weightedAnatomical MRI; Cross-dataset generalization; Deep convolutional neural networks; Prostate cancer; Prostate zonal segmentation
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