Parasitic diseases such as malaria remain a mayor burden on global health. One of the biggest challenges still to be overcome is that of inadequate diagnoses. This research explores the opportunities that Hyperspectral Imaging yields in this field. The first goal is to estimate the spectral signature of Malaria parasites in non-stained or Giemsa-stained thin smear blood samples and of Schistosoma parasite eggs in urine samples. For this different endmember extraction algorithms are combined with various methods of pre-processing and dimensionallity reduction. The used endmember extraction methods are pure pixel index (PPI), NFINDR, Statistics Based and simplex identification via split augmented Lagrangian (SISAL). For denoising Savitzky Golay and 3 dimensional gaussian filtering is used and the dimensionallity reduction is done with PCA, ICA or HySime. The resulting spectral signatures of the algorithms are validated by inspecting the endmember locations, spectra and abundance maps. They have furthermore been compared by the classification performance where the spectral signatures are used in the feature derivation. This is done by deriving a detection map using OSP or CEM detection and then using the SVM or random forest classifiers to classify cells as being infected or not. These performances are furthermore compared to RGB image based classification.

In case of the stained Malaria sample the four endmember extraction methods are shown to be applicable to various degrees. Firstly, the PPI method is shown to be inconsistent, resulting in different spectra each run. Secondly, the statistics based method unable to separate the spectral signatures of the red blood cells and thirdly the background. Thirdly, The NFINDR method seems to work well considering the endmember locations, spectra and abundance maps, but leads to a low classification performance. The research concludes that Sisal made the most accurate estimations of the spectral signature of the parasite. The results from all the validation methods are in line with expectations. Furthermore, the use of this spectral signature in the feature derivation process results in the highest classification performance. This performance is also shown to be significantly higher compared to using either the first principal component of the full hyperspectral data or the RGB images. Applying the same methods to the Schistosoma sample it is found that some of the methods, though interestingly not Sisal, are able to to create an abundance map in which the egg is separated from the background. However, none of them are able to separate the egg and the white blood cell and detection maps using these signatures did not show the egg more clearly than the first principal component did. None of the methods are found to be able to extract the spectral signature of the unstained Malaria parasite.

Finally, a hypothetical multispectral microscope is proposed which images at the wavelengths where the spectral signature of the parasite in a stained sample has the biggest difference in light transmittance to the other endmembers. This setup is simulated from the available hyperspectral data and its classification performance is compared to classification performance using the full hyperspectral data and using the RGB images which are simulated from the same data. The classification using the discriminative wavelengths is found to outperform both in terms of sensitivity and specificity. This implies that the images at these specific wavelengths provide more discriminative power and such a multispectral setup could provide a significant advantage over RGB imaging.

The ability to locate specific objects within images is an essential step in various computer vision based engineering applications. Image segmentation is the task of dividing an image into "segments" that are uniform as well as homogeneous with respect to some characteristics, for example grey tone or texture as in Haralick et al. This thesis seeks to perform image segmentation using a Deep Learning (DL) approach in the area of warehouse automation, specifically focusing on an order picking use case of Vanderlande Industries (VI). Generally in literature, DL algorithms for image segmentation are split into two main classes: algorithms for RGB images and algorithms for RGB-D images. RGB stands for the Red, Green, and Blue values of a pixel. RGB-D stands for the Red, Green, Blue, and Depth values of a pixel. The depth value in this case differs from the RGB values in that it does not give a value for a colour intensity, but rather it gives a value for physical distance between the camera and the object it is capturing. The challenge addressed by this thesis focuses on whether the introduction of depth data results in a substantially better performance than using RGB-only images, based on a data-set provided by VI. Also, this thesis looks into the maximum allowed deviation along the X-axis in the registration of the depth data to the RGB images. Two networks from literature were investigated and implemented in MATLAB for this purpose: the SegNet architecture proposed by Badrinarayanan et al. and the FuseNet architecture proposed by Hazirbas et al. Through experiments we have found that, for this use case, the introduction of complementary depth data leads to an improvement over the use of RGB-only images. We also find that, for this use case, the maximum allowed deviation along the X-axis in the registration of the depth data to the RGB images is approximately equal to 1.67 millimetres. The results in this thesis seem to indicate that investing in acquiring an additional depth band does have a positive effect on the accuracy of image segmentation for order picking in warehouse automation.

This study focuses on automated malaria diagnosis in low quality blood smear images, captured by a low-cost smartphone based microscope system. The aim is to localize and classify the healthy and infected erythrocytes (red blood cells) in order to evaluate the parasitaemia in an infected blood smear. Due to the lower quality of the smartphone microscope system compared to traditional high-end light microscopes, conventional algorithms fail to process these images. We propose a framework using a convolutional neural network as a pixel classifier to localize the erythrocytes. Afterwards we classify them accordingly, using a convolutional neural network as an object classifier. Such a system can offer in the-field malaria diagnosis without human intervention or can act as an aid for human experts to lower workload and increase diagnosis accuracy. The algorithm successfully localizes the erythrocytes with an average sensitivity of 97.31% and precision of 92.21%. Classification performed inadequate, in terms of low agreement with two human experts. This can be due to the low image quality or the small amount of training data available at the time.