Automated tuberculosis diagnosis using fluorescence images from a mobile microscope.

Automated tuberculosis diagnosis using fluorescence images from a mobile microscope.
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DOI:
10.1007/978-3-642-33454-2_43
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发表时间:
2012
期刊:
LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
影响因子:
--
通讯作者:
Malik, Jitendra
Malik, Jitendra
中科院分区:
其他
文献类型:
--
作者:
Chang, Jeannette;Arbelaez, Pablo;Switz, Neil;Reber, Clay;Tapley, Asa;Davis, J. Lucian;Cattamanchi, Adithya;Fletcher, Daniel;Malik, Jitendra

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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, 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.
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