title: Automated tuberculosis diagnosis using fluorescence images from a mobile microscope.
abstract: 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.
authors.affiliation: UC Berkeley, Department of Electrical Engineering and Computer Sciences, USA.
authors.forename: Jeannette
authors.initials: J
authors.lastname: Chang
authors.forename: Pablo
authors.initials: P
authors.lastname: Arbeláez
authors.forename: Neil
authors.initials: N
authors.lastname: Switz
authors.forename: Clay
authors.initials: C
authors.lastname: Reber
authors.forename: Asa
authors.initials: A
authors.lastname: Tapley
authors.forename: J Lucian
authors.initials: JL
authors.lastname: Davis
authors.forename: Adithya
authors.initials: A
authors.lastname: Cattamanchi
authors.forename: Daniel
authors.initials: D
authors.lastname: Fletcher
authors.forename: Jitendra
authors.initials: J
authors.lastname: Malik
mesh_terms: D004867:Equipment Design; D019544:Equipment Failure Analysis; D006801:Humans; D008856:Microscopy, Fluorescence / Q000295:instrumentation*; D009169:Mycobacterium tuberculosis / Q000166:cytology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D019095:Point-of-Care Systems; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013183:Sputum / Q000166:cytology* / Q000382:microbiology*; D014376:Tuberculosis / Q000382:microbiology* / Q000473:pathology*