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Malaria is a worldwide health problem with 225 million infections each year. A fast and easy-to-use method, with high performance is required to differentiate malaria from non-malarial fevers. Manual examination of blood smears is currently the gold standard, but it is time-consuming, labour-intensive, requires skilled microscopists and the sensitivity of the method depends heavily on the skills of the microscopist. We propose an easy-to-use, quantitative cartridge-scanner system for vision-based malaria diagnosis, focusing on low malaria parasite densities. We have used special finger-prick cartridges filled with acridine orange to obtain a thin blood film and a dedicated scanner to image the cartridge. Using supervised learning, we have built a Plasmodium falciparum detector. A two-step approach was used to first segment potentially interesting areas, which are then analysed in more detail. The performance of the detector was validated using 5,420 manually annotated parasite images from malaria parasite culture in medium, as well as using 40 cartridges of 11,780 images containing healthy blood. From finger prick to result, the prototype cartridge-scanner system gave a quantitative diagnosis in 16 min, of which only 1 min required manual interaction of basic operations. It does not require a wet lab or a skilled operator and provides parasite images for manual review and quality control. In healthy samples, the image analysis part of the system achieved an overall specificity of 99.999978% at the level of (infected) red blood cells, resulting in at most seven false positives per microlitre. Furthermore, the system showed a sensitivity of 75% at the cell level, enabling the detection of low parasite densities in a fast and easy-to-use manner. A field trial in Chittagong (Bangladesh) indicated that future work should primarily focus on improving the filling process of the cartridge and the focus control part of the scanner., (© 2013 The Authors Journal of Microscopy © 2013 Royal Microscopical Society.)

In traditional cancer diagnosis, (histo)pathological images of biopsy samples are visually analysed by pathologists. However, this judgment is subjective and leads to variability among pathologists. Digital scanners may enable automated objective assessment, improved quality and reduced throughput time. Nucleus detection is seen as the corner stone for a range of applications in automated assessment of (histo)pathological images. In this paper, we propose an efficient nucleus detector designed with machine learning. We applied colour deconvolution to reconstruct each applied stain. Next, we constructed a large feature set and modified AdaBoost to create two detectors, focused on different characteristics in appearance of nuclei. The proposed modification of AdaBoost enables inclusion of the computational cost of each feature during selection, thus improving the computational efficiency of the resulting detectors. The outputs of the two detectors are merged by a globally optimal active contour algorithm to refine the border of the detected nuclei. With a detection rate of 95% (on average 58 incorrectly found objects per field-of-view) based on 51 field-of-view images of Her2 immunohistochemistry stained breast tissue and a complete analysis in 1 s per field-of-view, our nucleus detector shows good performance and could enable a range of applications in automated assessment of (histo)pathological images., (© 2012 The Authors Journal of Microscopy © 2012 Royal Microscopical Society.)