ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2012
Automated tuberculosis diagnosis using fluorescence images from a mobile microscope.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 69 citations in OpenAlex.
- A Study ofDiagnostics (Basel, Switzerland) · 2023Article
- A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches.Artificial intelligence review · 2023Article
- Three-dimensional imaging of biological cells using surface plasmon coupled emission.Journal of biomedical optics · 2022Article
- Recent trends in smartphone-based detection for biomedical applications: a review.Analytical and bioanalytical chemistry · 2021Review
- Nuclear morphometrics and chromatin condensation patterns as disease biomarkers using a mobile microscope.PloS one · 2019Article
- Artificial Neural Networks for Prediction of Tuberculosis Disease.Frontiers in microbiology · 2019Article
- Microfluidic Preconcentration Chip with Self-Assembled Chemical Modified Surface for Trace Carbonyl Compounds Detection.Sensors (Basel, Switzerland) · 2018Article
- Distinguishing between whole cells and cell debris using surface plasmon coupled emission.Biomedical optics express · 2018Article
- Low cost automated whole smear microscopy screening system for detection of acid fast bacilli.PloS one · 2018Article
- Computer Vision Malaria Diagnostic Systems-Progress and Prospects.Frontiers in public health · 2017Review
- Feasibility of the TBDx automated digital microscopy system for the diagnosis of pulmonary tuberculosis.PloS one · 2017Article
- Review of Telemicrobiology.Archives of pathology & laboratory medicine · 2016Review
- A Review of Automatic Methods Based on Image Processing Techniques for Tuberculosis Detection from Microscopic Sputum Smear Images.Journal of medical systems · 2016Review
- A multicenter clinical evaluation of Mycobacterium tuberculosis IgG/IgM antibody detection using the colloidal gold method.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2014Article
- Tuberculosis disease diagnosis using artificial immune recognition system.International journal of medical sciences · 2014Article
- Quantitative imaging with a mobile phone microscope.PloS one · 2014Article
- Mobile digital fluorescence microscopy for diagnosis of tuberculosis.Journal of clinical microbiology · 2013Article
- Article
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Authors and funding
9 authors at 5 institutions in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.