Ronald Dendere

Classification of Mycobacterium tuberculosis in Images of ZN-Stained Sputum Smears

Screening for tuberculosis (TB) in low- and middle-income countries is centered on the microscope. We present methods for the automated identification of Mycobacterium tuberculosis in images of Ziehl-Neelsen (ZN) stained sputum smears obtained using a bright-field microscope. We segment candidate bacillus objects using a combination of two-class pixel classifiers. The algorithm produces results that agree well with manual segmentations, as judged by the Hausdorff distance and the modified Williams index. The extraction of geometric-transformation-invariant features and optimization of the feature set by feature subset selection and Fisher transformation follow. Finally, different two-class object classifiers are compared. The sensitivity and specificity of all tested classifiers is above 95% for the identification of bacillus objects represented by Fisher-transformed features. Our results may be used to reduce technician involvement in screening for TB, and would be particularly useful in laboratories in countries with a high burden of TB, where, typically, ZN rather than auramine staining of sputum smears is the method of choice.

Automated focusing in bright-field microscopy for tuberculosis detection

Automated microscopy to detect Mycobacterium tuberculosis in sputum smear slides would enable laboratories in countries with a high tuberculosis burden to cope efficiently with large numbers of smears. Focusing is a core component of automated microscopy, and successful autofocusing depends on selection of an appropriate focus algorithm for a specific task. We examined autofocusing algorithms for bright‐field microscopy of Ziehl–Neelsen stained sputum smears. Six focus measures, defined in the spatial domain, were examined with respect to accuracy, execution time, range, full width at half maximum of the peak and the presence of local maxima. Curve fitting around an estimate of the focal plane was found to produce good results and is therefore an acceptable strategy to reduce the number of images captured for focusing and the processing time. Vollaths F4 measure performed best for full z‐stacks, with a mean difference of 0.27 μm between manually and automatically determined focal positions, whereas it is jointly ranked best with the Brenner gradient for curve fitting.

Automated detection of malaria in Giemsa-stained thin blood smears

The current gold standard of malaria diagnosis is the manual, microscopy-based analysis of Giemsa-stained blood smears, which is a time-consuming process requiring skilled technicians. This paper presents an algorithm that identifies and counts red blood cells (RBCs) as well as stained parasites in order to perform a parasitaemia calculation. Morphological operations and histogram-based thresholding are used to extract the red blood cells. Boundary curvature calculations and Delaunay triangulation are used to split clumped red blood cells. The stained parasites are classified using a Bayesian classifier with their RGB pixel values as features. The results show 98.5% sensitivity and 97.2% specificity for detecting infected red blood cells.

A review of cellphone microscopy for disease detection

The expansion in global cellphone network coverage coupled with advances in cellphone imaging capabilities present an opportunity for the advancement of cellphone microscopy as a low‐cost alternative to conventional microscopy for disease detection in resource‐limited regions. The development of cellphone microscopy has also benefitted from the availability of low‐cost miniature microscope components such as low‐power light‐emitting diodes and ball lenses. As a result, researchers are developing hardware and software techniques that would enable such microscopes to produce high‐resolution, diagnostic‐quality images. This approach may lead to more widespread delivery of diagnostic services in resource‐limited areas where there is a shortage of the skilled labour required for conventional microscopy and where prevalence of infectious and other diseases is still high. In this paper, we review current techniques, clinical applications and challenges faced in the field of cellphone microscopy.

Dual-Energy X-Ray Absorptiometry for Measurement of Phalangeal Bone Mineral Density on a Slot-Scanning Digital Radiography System

Objective: In this paper, we assess the feasibility of using two detectors in a slot-scanning digital radiography system to acquire images for measuring bone mineral density (BMD) of the middle phalanx of the middle finger using dual-energy X-ray absorptiometry (DXA). Methods: Simulations were used to evaluate the spectral separation of the low- and high-energy spectra and detective quantum efficiency was used for assessing image quality. Scan parameters were chosen to optimize spectral separation, image quality, and radiation dose. We introduce the measurement of volumetric BMD (vBMD) using basis material decomposition. We assess the accuracy of our methods by comparing measurements taken using bone images against reference data derived from subsequent incineration of the bones. In vivo scans were conducted to evaluate the system precision (repeatability) and agreement with a clinical densitometer. Results: Average errors for bone mineral content (BMC), areal BMD (aBMD), and vBMD were 4.85%, 5.49%, and 12.77%, respectively. Our system had good agreement with a clinical densitometer based on concordance correlation coefficient values of 0.92 and 0.98 for aBMD and BMC, respectively. Precision studies yielded coefficient of variation (CV) values of 1.35% for aBMD, 1.48% for BMC, and 1.80% for vBMD. The CV values of all measurements were within 2%, indicating that the methods have clinically acceptable precision. Conclusion: We conclude that our techniques yield bone measurements with high accuracy, clinically acceptable precision, and good agreement with a clinical densitometer. Significance: We have shown the clinical potential of phalangeal DXA measurements of aBMD and vBMD on a slot-scanning digital radiography system.

Monte-Carlo simulation of a slot-scanning X-ray imaging system

We present a method for simulating slot-scanning X-ray imaging using the general-purpose Monte Carlo simulation package PENELOPE and penEasy Imaging. Different phantoms can be defined with the PENGEOM package, which defines bodies as combinations of volumes limited by quadric surfaces. The source-detector geometry, the position of the object, the collimator, the X-ray tube properties, the detector material and the pixel dimensions are defined. The output of the time-delay integration detector is simulated using sequential slot outputs derived from penEasy Imaging. The simulations are validated using tungsten and aluminium test objects, which are both simulated and imaged. The simulations are compared to the X-ray images using standard image quality metrics. The MTF, NPS and DQE curves show that the real and simulated X-ray images are comparable in terms of spatial resolution, noise and frequency information. The implementation can be modified to suit alterations in the system being simulated.

Model-based segmentation of the middle phalanx in digital radiographic images of the hand

We present techniques for segmenting the middle phalanx of the middle finger in digital radiographic images using deformable models and active shape models (ASMs). The result of segmentation may be used in the estimation of bone mineral density which in turn may be used in the diagnosis of osteoporosis. A technique for minimizing user dependence is described. The segmentation accuracy of the two methods is assessed by comparing contours produced by the algorithms to those produced by manual segmentation, using the Hausdorff distance measure. The ASM technique produces more accurate segmentation.

Filtration to reduce paediatric dose for a linear slot-scanning digital X-ray machine

This paper describes modelling, application and validation of a filtration technique for a linear slot-scanning digital X-ray system to reduce radiation dose to paediatric patients while preserving diagnostic image quality. A dose prediction model was implemented, which calculates patient entrance doses using variable input parameters. Effective dose is calculated using a Monte Carlo simulation. An added filter of 1.8-mm aluminium was predicted to lower the radiation dose significantly. An objective image quality study was conducted using detective quantum efficiency (DQE). The PTW Normi 4FLU test phantom was used for quantitative assessment, showing that image contrast and spatial resolution were maintained with the proposed filter. A paediatric cadaver full-body imaging trial assessed the diagnostic quality of the images and measured the dose reduction using a 1.8-mm aluminium filter. Assessment by radiologists indicated that diagnostic quality was maintained with the added filtration, despite a reduction in DQE. A new filtration technique for full-body paediatric scanning on the Lodox Statscan has been validated, reducing entrance dose for paediatric patients by 36 % on average and effective dose by 27 % on average, while maintaining image quality.

Effect of aluminium filtration on dose and image quality in paediatric slot-scanning radiography

This paper examines the effect that a 1.8 mm aluminium filter has on paediatric patient dose and image quality for linear slot scanning radiography (LSSR). A dynamic dose prediction model for LSSR accurately predicted the dose reduction effects of added aluminium filtration. A cadaver imaging study was carried out to assess the effects of filtration on image quality. With 1.8 mm added aluminium filtration, no visible degradation to image contrast or clarity was found, and in some cases the aluminium filtration improved the image quality as judged by radiologists.

Monte-Carlo simulation of a slot-scanning digital mammography system for tomosynthesis

BACKGROUND Digital breast tomosynthesis (DBT) reconstructs planar slices of the breast based on two-dimensional angular projections. Early studies and clinical trials show that DBT is an improvement over full field digital mammography (FFDM) because it provides the radiologist with better image quality and more information. OBJECTIVE This paper presents a simulation system to model the performance of a slot-scanning FFDM and DBT system. METHODS A tissue-equivalent three dimensional (3D) breast phantom was constructed, validated for slot-scanning digital mammography and used in simulating digital breast tomosynthesis. The simulation system was validated by comparing images acquired with a slot-scanning mammography machine with simulated phantom images, using the edge-test method and image quality metrics modulation transfer function (MTF), noise power spectrum (NPS) and detective quantum efficiency (DQE). Different two-dimensional (2D) projections of the 3D phantom were simulated and the phantom was reconstructed using filtered backprojection. RESULTS Image quality metrics showed equivalence between simulated and real images. CONCLUSIONS The simulation tool is suitable for slot-scanning FFDM and DBT and may be used for the design and comparison of mammography systems.