An Evaluation of Image Enhancement Techniques for Nailfold Capillary Skeletonisation

Nailfold capillaroscopy (NC) is a routine technique used to assess the characteristics and morphology of nailfold capillaries. Observation of micro-blood vessels in the nailfold is important for diagnosing diseases that lead to morphological changes of capillaries such as scleroderma, Raynaud's phenomenon and other connective tissue diseases. In order to support a computer-aided diagnosis approach to analysing NC images, several approaches have been proposed in the literature aiming to extract capillaries. In general, such capillary skeletonisation algorithms involve an image pre-processing step, followed by binarisation and finally extraction and definition of the capillary skeletons. Since image denoising and enhancement in the pre-processing step can have a major impact on the subsequent analysis, in this paper, we evaluate the performance of five enhancement techniques for the purpose for nailfold capillary skeletonisation. In particular, we investigate the α-trimmed filter, bilateral filter, bilateral enhancer, anisotropic diffusion filter and non-local means and integrate them with three capillary extraction algorithms from the literature. We report visual and quantitative performance on a set of diverse NC images. The obtained results indicate that a relatively simple α-trimmed filter, combined with a skeletonisation algorithm incorporating a difference-of-Gaussian approach to address non-uniform lighting and an iterative rule-based skeletonisation procedure, leads to the best results when comparing the obtained skeletonisations to a manually obtained ground truth

Multi-dimensional local binary pattern texture descriptors and their application for medical image analysis

Texture can be broadly stated as spatial variation of image intensities. Texture analysis and classification is a well researched area for its importance to many computer vision applications. Consequently, much research has focussed on deriving powerful and efficient texture descriptors. Local binary patterns (LBP) and its variants are simple yet powerful texture descriptors. LBP features describe the texture neighbourhood of a pixel using simple comparison operators, and are often calculated based on varying neighbourhood radii to provide multi-resolution texture descriptions. A comprehensive evaluation of different LBP variants on a common benchmark dataset is missing in the literature. This thesis presents the performance for different LBP variants on texture classification and retrieval tasks. The results show that multi-scale local binary pattern variance (LBPV) gives the best performance over eight benchmarked datasets. Furthermore, improvements to the Dominant LBP (D-LBP) by ranking dominant patterns over complete training set and Compound LBP (CM-LBP) by considering 16 bits binary codes are suggested which are shown to outperform their original counterparts. The main contribution of the thesis is the introduction of multi-dimensional LBP features, which preserve the relationships between different scales by building a multi-dimensional histogram. The results on benchmarked classification and retrieval datasets clearly show that the multi-dimensional LBP (MD-LBP) improves the results compared to conventional multi-scale LBP. The same principle is applied to LBPV (MD-LBPV), again leading to improved performance. The proposed variants result in relatively large feature lengths which is addressed using three different feature length reduction techniques. Principle component analysis (PCA) is shown to give the best performance when the feature length is reduced to match that of conventional multi-scale LBP. The proposed multi-dimensional LBP variants are applied for medical image analysis application. The first application is nailfold capillary (NC) image classification. Performance of MD-LBPV on NC images is highest, whereas for second application, HEp-2 cell classification, performance of MD-LBP is highest. It is observed that the proposed texture descriptors gives improved texture classification accuracy