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Automated Tuberculosis Diagnosis Using Fluorescence Images from a Mobile Microscope

Chang, Jeannette ; Arbeláez, Pablo ; Switz, Neil ; Reber, Clay ; Tapley, Asa ; Davis, J. Lucian ; Cattamanchi, Adithya ; Fletcher, Daniel ; Malik, Jitendra Delingette, Hervé ; Golland, Polina ; Ayache, Nicholas ; Mori, Kensaku

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012, p.345-352 [Periódico revisado por pares]

Berlin, Heidelberg: Springer Berlin Heidelberg

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  • Título:
    Automated Tuberculosis Diagnosis Using Fluorescence Images from a Mobile Microscope
  • Autor: Chang, Jeannette ; Arbeláez, Pablo ; Switz, Neil ; Reber, Clay ; Tapley, Asa ; Davis, J. Lucian ; Cattamanchi, Adithya ; Fletcher, Daniel ; Malik, Jitendra
  • Delingette, Hervé ; Golland, Polina ; Ayache, Nicholas ; Mori, Kensaku
  • Assuntos: Average Precision ; Candidate Object ; Gaussian Mixture Model ; Sputum Smear ; Template Match
  • É parte de: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012, p.345-352
  • Descrição: In low-resource areas, the most common method of tuberculosis (TB) diagnosis is visual identification of rod-shaped TB bacilli in microscopic images of sputum smears. We present an algorithm for automated TB detection using images from digital microscopes such as CellScope [2], a novel, portable device capable of brightfield and fluorescence microscopy. Automated processing on such platforms could save lives by bringing healthcare to rural areas with limited access to laboratory-based diagnostics. Our algorithm applies morphological operations and template matching with a Gaussian kernel to identify candidate TB-objects. We characterize these objects using Hu moments, geometric and photometric features, and histograms of oriented gradients and then perform support vector machine classification. We test our algorithm on a large set of CellScope images (594 images corresponding to 290 patients) from sputum smears collected at clinics in Uganda. Our object-level classification performance is highly accurate, with Average Precision of 89.2%±2.1%. For slide-level classification, our algorithm performs at the level of human readers, demonstrating the potential for making a significant impact on global healthcare.
  • Editor: Berlin, Heidelberg: Springer Berlin Heidelberg
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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