Spatial recurrence analysis: a sensitive and fast detection tool in digital mammography.
Efficient diagnostics of breast cancer requires fast digital mammographic image processing. Many breast lesions, both benign and malignant, are barely visible to the untrained eye and requires accurate and reliable methods of image processing. We propose a new method of digital mammographic image analysis that meets both needs. It uses the concept of spatial recurrence as the basis of a spatial recurrence quantification analysis, which is the spatial extension of the well-known time recurrence analysis. The recurrence-based quantifiers are able to evidence breast lesions in a way as good as the best standard image processing methods available, but with a better control over the spurious fragments in the image.
Departamento de Física, Universidade Federal do Paraná, 81531-990 Curitiba, Paraná, Brazil.
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Prado
Departamento de Física, Universidade Federal do Paraná, 81531-990 Curitiba, Paraná, Brazil.
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Galuzio
Departamento de Física, Universidade Federal do Paraná, 81531-990 Curitiba, Paraná, Brazil.
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Lopes
Departamento de Física, Universidade Federal do Paraná, 81531-990 Curitiba, Paraná, Brazil.
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Viana
D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D008327:Mammography / Q000295:instrumentation / Q000379:methods