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BackgroundMultispectral imaging microscopy is a novel microscopic technique that integrates spectroscopy with optical imaging to record both spectral and spatial information of a specimen. This enables acquisition of a large and more informative dataset than is achievable in conventional optical microscopy. However, such data are characterized by high signal correlation and are difficult to interpret using univariate data analysis techniques.MethodsIn this work, the development and application of a novel method which uses principal component analysis (PCA) in the processing of spectral images obtained from a simple multispectral-multimodal imaging microscope to detect Plasmodium parasites in unstained thin blood smear for malaria diagnostics is reported. The optical microscope used in this work has been modified by replacing the broadband light source (tungsten halogen lamp) with a set of light emitting diodes (LEDs) emitting thirteen different wavelengths of monochromatic light in the UV–vis-NIR range. The LEDs are activated sequentially to illuminate same spot of the unstained thin blood smears on glass slides, and grey level images are recorded at each wavelength. PCA was used to perform data dimensionality reduction and to enhance score images for visualization as well as for feature extraction through clusters in score space.ResultsUsing this approach, haemozoin was uniquely distinguished from haemoglobin in unstained thin blood smears on glass slides and the 590–700 spectral range identified as an important band for optical imaging of haemozoin as a biomarker for malaria diagnosis.ConclusionThis work is of great significance in reducing the time spent on staining malaria specimens and thus drastically reducing diagnosis time duration. The approach has the potential of replacing a trained human eye with a trained computerized vision system for malaria parasite blood screening.

We describe Envelope Function Approximation (EFA) bandstructure calculations based on a 4-band electron (EL), heavy-hole (HH), light-hole (LH) and split-off hole (SO) effective mass Hamiltonian, with Burt-Foreman hermitianisation, which can handle III-V quantum well structures that incorporate ultra-narrow epi-layers. The model takes into account the coupling of EL, HH, LH and SO bands and is suitable for describing quantum wells tuned to the 1.0 - 1.55 µm window exploited by optical fibre communication devices. We have used the multi-band solver to calculate the bandstructure of an illustrative InGaAsSb-AlGaSb non-square quantum well that incorporates 6Å potential “spikes” in its well region. Calculations based on the Burt-Foreman hermitianised Hamiltonian and those based on a Hamiltonian with standard “symmetrised” hermitianisation are presented and compared. When coupling to the conduction band is excluded from the calculation, the latter formulation leads to anomalous electron-like curvature of the dispersion curves for our spiked non-square quantum well structure.

Malaria is the leading cause of morbidity and mortality in tropical and subtropical countries. Conventional microscopy is the Gold standard in the diagnosis of the disease. However, it is prone to some shortcomings which include time consumption and difficultness in reproducing results. Alternative diagnosis techniques which yield superior results are quite expensive and hence inaccessible to developing countries where the disease is prevalent. Thus in this work, an accurate, speedy and affordable system of malaria detection using stained thin blood smear images was developed. The method uses Artificial Neural Network (ANN) to test for the presence of plasmodium parasites in thin blood smear images. Images of infected and non-infected erythrocytes were acquired, pre-processed, relevant features extracted from them and eventually diagnosis was made based on the features extracted from the images. Diagnosis entailed detection of plasmodium parasites. Classification accuracy of 95.0% in detection of infected erythrocyte was achieved with respect to results obtained by expert microscopists. The study revealed that artificial neural network (ANN) classifiers trained with colour features of infected stained thin blood smear images are suitable for detection. It was further shown that ANN classifiers can be trained to perform image segmentation.

The accuracy, reliability, speed and cost of the methods used for malaria diagnosis are key to the diseases’ treatment and eventual eradication. However, improvement in any one of these requirements can lead to deterioration of the rest due to their interdependence. We propose an optical method that provides fast detection of malaria-infected red blood cells (RBCs) at a lower cost. The method is based on the combination of deconvolution, topography and three-dimensional (3D) refractive index reconstruction of the malaria-infected RBCs by use of the transport of intensity equation. Using our method, healthy RBCs were identified by their biconcave shape, quasi-uniform spatial distribution of their refractive indices and quasi-uniform concentration of hemoglobin. The values of these optical and biochemical parameters were found to be in agreement with the values reported in the literature. Results for the malaria-infected RBCs were significantly different from those of the healthy RBCs. The topography of the cells and their optical and biochemical parameters enabled identification of their stages of infection. This work introduces a significant method of analyzing malaria-infected RBCs at a lower cost and without the use of fluorescent labels for the parasites.