Application of Principal Component Analysis (PCA) to Multispectral Imaging Microscopy for Malaria Diagnosis

Abstract

Background

Multispectral 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. Methods

In 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. Results

Using 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. Conclusion

This 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. Keywords: Malaria diagnostics, Multispectral imaging, Principal component analysis, LED microscope