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ADAPTATIVE ALGORITHMS FOR AUTOMATIC DETECTION OF WHITE MATTER HYPERINTENSITIES IN VASCULAR AND NEUROLOGICAL DISEASE: A SYSTEMATIC REVIEW

Investigación operacional, 2023-10, Vol.44 (4), p.554 [Peer Reviewed Journal]

COPYRIGHT 2023 Editorial Universitaria de la Republica de Cuba ;ISSN: 0257-4306

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  • Title:
    ADAPTATIVE ALGORITHMS FOR AUTOMATIC DETECTION OF WHITE MATTER HYPERINTENSITIES IN VASCULAR AND NEUROLOGICAL DISEASE: A SYSTEMATIC REVIEW
  • Author: Humara-Fonseca, Jessica ; Perodin-Hernandez, Janet ; Alvarez-Iglesias, Jose Enrique
  • Subjects: Algorithms ; Diagnostic imaging ; Medical imaging equipment ; Nervous system diseases
  • Is Part Of: Investigación operacional, 2023-10, Vol.44 (4), p.554
  • Description: This review article covered 20 years of published papers on the detection of lesions of vascular and neurological origin on Magnetic Resonance Imaging (MRI). The article provided concrete suggestions about pulse sequences and algorithms for the jointly detection of lesions in the white matter. The algorithm with the best adaptive features was Histogram. The results suggested to perform comprehensive studies that evaluate segmentation algorithms on different types of lesions. KEYWORDS: neuroimages, algorithm, segmentation, vascular and neurological diseases. MSC: AMS subject classifications Este articulo de revision abarca 20 anos de trabajos publicados sobre la deteccion de lesiones de origen vascular y neurologico en Imagenes de Resonancia Magnetica (IRM). El articulo proporciona sugerencias concretas sobre secuencias de pulso y algoritmos para la deteccion conjunta de lesiones en la materia blanca. El algoritmo con las mejores caracteristicas de adaptabilidad fue Histograma. Los resultados sugierieron realizar estudios integrales que evaluen algoritmos de segmentacion en diferentes tipos de lesiones. PALABRAS CLAVE: neuroimagenes, algoritmo, segmentacion, enfermedades vasculares y neurologicas.
  • Publisher: Editorial Universitaria de la Republica de Cuba
  • Language: English;Spanish
  • Identifier: ISSN: 0257-4306
  • Source: Alma/SFX Local Collection
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