Nailfold capillaroscopy (NC) is a medical imaging modality used to assess the characteristics and morphology of micro-blood vessels in the nailfold. NC is of particular importance in diagnosing diseases that lead to morphological changes of capillaries such as scleroderma, Raynaud's phenomenon and other connective tissue diseases. In order to provide a computer-aided diagnosis approach to analysing NC images, in this paper, we present a skeletonisation algorithm of captured nailfold capillaries. Our approach first enhances the image using an a-trimmed filter, and then performs a binarisation step based on difference of Gaussian filtering and thresholding, followed by iterative thinning to extract the capillary skeletons. We show that the proposed algorithm works well and gives significantly superior performance compared to previous approaches.

Texture classification algorithms are utilised in various image analysis and medical imaging applications. A number of high performing texture algorithms are based on the concept of local binary patterns (LBP) characterising the relationships of pixels to their local neighbourhood. LBP descriptors are simple to calculate, are invariant to intensity changes and can be calculated in a rotation invariant manner as well as at different scales. Incorporating variance information, leading to LBP variance (LBPV) texture descriptors, has been claimed to lead to more versatile and more effective texture features. In this paper, we investigate this in more detail, benchmarking and contrasting the classification performance of several LBP and LBPV descriptors for generic image texture classification as well as two medical tasks. We show that while LBPV-based methods typically lead to improved classification performance this is not always so and that thus the inclusion of variance information is task dependent.

Texture is an important characteristic of images and hence used in a variety of computer vision applications. A group of high performing texture algorithms is based on the concept of local binary patterns (LBP) which describe the relationship of pixels to their local neighbourhoods. A rotation invariant form of this descriptor is typically employed since especially for textured surfaces rotation cannot be controlled. Since conventional LBP discards the magnitude information between the centre pixel and neighbouring pixels, Compound LBP (CM-LBP), a variant of LBP, integrates this information by introducing a 16-bit LBP code. The feature length of CM-LBP is then reduced by splitting this 16-bits into two 8-bit codes. However, this approach does not allow for rotation invariant mappings as in conventional LBP, and CM-LBP hence cannot be applied to images under rotation, thus severly limiting the application of the method. In this paper, we address this problem and present rotation invariant and uniform mappings for CM-LBP. We evaluate our new texture descriptor on Outex and Brodatz benchmark datasets and show it to lead to a significantly improved classification performance compared to CM-LBP.

Indirect immunofluorescence (IIF) imaging is an important technique for detecting antinuclear antibodies in HEp-2 cells and therefore employed in the diagnosis of autoimmune diseases and other important pathological conditions involving the immune system. Here, HEp-2 cells are categorised into different groups, which allow to make implications about different autoimmune diseases. Traditionally, this categorisation is performed manually by an expert and is hence both subjective and time intensive. In this paper, we present an effective method for classification of HEp-2 cells in which we first extract local binary pattern (LBP) texture features in form of multi-dimensional LBP (MD-LBP) histograms and then employ a multiple kernel learning approach to classification that integrates a multitude of support vector kernels generated by sampling the feature space. We evaluate our algorithm on the ICPR 2012 HEp-2 contest benchmark dataset, and demonstrate that our employed texture features are indeed useful for the differentiation of HEp-2 cells and that our multiple kernel learning based classification approach outperforms single kernel classification schemes. Our algorithm is shown to provide super performance compared to all techniques that were entered in the competition and to rival results obtained by a human expert.

Nailfold capillaroscopy (NC) is a non-invasive imaging technique employed to assess the condition of blood capillaries in the nailfold and is particularly useful for diagnosis of scleroderma spectrum disorders and Raynaud’s phenomenon. Diagnosis is based on the identification of particular scleroderma patterns in the images which are typically grouped into early, active and late patterns. In this paper, we present a computer vision approach to recognising scleroderma patterns in NC images. Following a preprocessing step to enhance image quality, we extract texture information in a holistic way rather than trying to extract and measure individual capillaries. As texture features we employ multi-dimensional LPB variance descriptors which capture multi-resolution texture and local contrast information. Our experimental results confirm our approach to work well and to outperform an earlier approach.

Indirect immunofluorescence (IIF) imaging is an important technique for detecting antinuclear antibodies in HEp-2 cells and therefore employed in the diagnosis of autoimmune diseases and other important pathological conditions involving the immune system. HEp-2 cells are often categorised into six groups (homogeneous, fine speckled, coarse speckled, nucleolar, cytoplasmic, and centromere cells), which in turn give indications on different autoimmune diseases. While traditionally this classification is performed manually thus representing a subjective and laborous task, recently there is significant interest in computer vision based approaches to automatically categorise HEp-2 cell images and thus provide an objective and fast alternative. Various algorithms have been proposed for this purpose in which texture information often plays a dominant role. In this paper, we also employ texture descriptors for automated classification of HEp-2 cells but choose descriptors that allow to take into account the fuzzy nature of the images and the noise present in them. In particular, we employ a fuzzy version of the well known local binary pattern (LBP) paradigm, coupled with support vector machine (SVM) based classification. We benchmark our approach on the ICPR 2012 HEp-2 contest benchmark dataset and show it to provide excellent classification performance, outperforming not only conventional LBP features but also all algorithms that were entered in the competition as well as the performance of a human expert.

Nailfold capillaroscopy (NC) is a non-invasive imaging technique employed to assess the condition of blood capillaries in the nailfold, and is routinely used for the detection of scleroderma spectral disorders, Raynaud's phenomenon and other connective tissue diseases. In this chapter, we present computer-aided approaches for capillary inspection, in particular focussing on the tasks of image enhancement, binarisation and skeletonisation. We evaluate the performance of a number image enhancement/noise removal techniques for NC images, as a pre-cursor to edge detection aimed at identifying capillaries. Results show that bilateral filters and enhancers provide the best overall image quality. Following noise removal, NC images typically get converted into binary form. For this purpose, we employed a difference-of-Gaussian approach before thresholding. The final stage is that of skeletonisation, which can be effectively performed using a rule-based thinning algorithm. Thus the complete imaging pipeling of pre-processing, binarisation and skeletonisation is represented in this chapter.

Texture features are important in many computer vision applications. LBP is a simple yet powerful texture descriptor that is based on the concept of local binary patterns which describe the relationships of pixels to their local neighbourhood. These relationships are encoded in binary form, and the resulting patterns are then typically used to build histograms over an image or image region. It is observed that only relatively few of these patterns occur frequently in images. Dominant LBP (D-LBP) is a variant of LBP based on these dominant LBP patterns. D-LBP re-arranges the histogram bins in descending order of frequency and then selects the first few dominant patterns as texture features. By doing so, however, it discards the information of which patterns are selected. In this paper, we propose an improved Dominant LBP algorithm that preserves the pattern information and show it, based on an extensive set of experiments on several Outex benchmark datasets, to outperform D-LBP for texture classification.

Nailfold capillaroscopy (NC) is a non-invasive imaging technique employed to assess the condition of blood capillaries in the nailfold. It is particularly useful for early detection of scleroderma spectrum disorders and evaluation of Raynaud's phenomenon. While automated approaches to analysing NC images are relatively rare, they are typically based on extraction and analysis of individual capillaries from the images in order to assign a patient to one of the commonly employed scleroderma patterns. In this chapter, we present a different approach that does not rely on individual capillaries but performs interpretation in a holistic way based on information gathered from an image or a selected image region. In particular, our algorithm employs texture analysis to characterise the underlying patterns, coupled with a classification stage to first identify patterns in fingers, and then, through a voting strategy, reach a decision for a patient. Experimental results on a set of NC images with known ground truth demonstrate the efficacy of the proposed approach.

Morphological alternations of blood capillaries in the finger nailfold are indicative of underlying connective tissue diseases. This requires close observation of the capillaries, which can be conducted using nailfold capillaroscopy (NC) which is a standard method for diagnosing diseases such as scleroderma or Raynaud’s phenomenon. Typically, detection of NC scleroderma patterns (early, active, and late) is performed through manual inspection by an expert. In this paper, we present an automated method of analysing nailfold capillaroscopy images and categorising them into NC patterns. A carefully chosen set of texture features is extracted from the images which we then employ in a pattern classification stage. For the latter, we apply an ensemble classifier to arrive at decisions for each captured finger, which in a final stage are aggregated to form a diagnosis for the patient. Experimental results on a set of 56 NC images from 16 subjects demonstrate the accuracy and usefulness of our presented approach.

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.

Nailfold capillaroscopy (NC) is a valuable method for observing micro blood vessel characteristics and is particularly useful for early detection of scleroderma spectrum disorders and evaluation of Raynaud’s phenomenon. Diagnosis involves the recognition of early, active and late patterns, also known as NC patterns or scleroderma (SD) patterns, in the captured NC images/image sequences. NC assessment is typically performed by manual inspection, which is subjective, requires extensive experience, and is a time consuming task. Computerised automation can help to address these problems, yet relatively little work is reported in the literature on such approaches. In this paper, we present a review of work in computerised nailfold capillaroscopy. We discuss semi-automatic, image and video based NC techniques, and in particular image enhancement methods, capillary extraction algorithms and parameter measurement methods.

Indirect immunofluorescence imaging is a fundamental technique for detecting antinuclear antibodies in HEp-2 cells and consequently important for the diagnosis of autoimmune diseases and other important pathological conditions involving the immune system. HEp-2 cells can be categorised into six groups: homogeneous, fine speckled, coarse speckled, nucleolar, cytoplasmic, and Centro mere cells, which give indications on different autoimmune diseases. In the literature, various algorithms have been proposed for automatic classification of HEp-2 cells based typically on shape features, texture features and classification algorithms. Local binary pattern (LBP) features are simple yet powerful texture descriptors, which encode the neighbours of a pixels into a binary pattern. While over the years a variety of LBP algorithms have been introduced, only a few descriptors are utilised in the context of HEp-2 cell classification. In this paper, we benchmarked eight rotation invariant LBP variants and a total of 16 descriptors on the ICPR 2012 HEp-2 contest benchmark dataset. We found rotation invariant multi-dimensional LBP features to lead to the best classification performance.

Indirect immunofluorescence imaging is commonly employed for screening of antinuclear antibodies based on HEp-2 cells which is used for diagnosing autoimmune diseases and other important pathological conditions involving the immune system. For this purpose, observed HEp-2 cells are categorised into homogeneous, fine speckled, coarse speckled, nucleolar, cytoplasmic, and centromere cells. Typically, this categorisation is performed manually by an expert and is hence both time consuming and subjective. In this paper, we present a method for automatically classifiying HEp-2 cells using multi-scale texture information in conjunction with an ensemble classification system. We extract multi-dimensional local binary pattern (MD-LBP) texture features of the cell area, which we then compactify using principal component analysis (PCA). PCA-projected features of reduced dimensionality are then employed as input for the subsequent classification stage. For classification, we use a margin distribution based bagging pruning (MAD-Bagging) classifier ensemble. We evaluate our algorithm on the ICPR 2012 HEp-2 contest benchmark dataset, and demonstrate it to give excellent performance, superior to all algorithms that were entered in the competition.

Texture analysis and classification have received significant research interest and have been shown to be essential in many computer vision systems and applications. Local binary patterns (LBP) are powerful yet simple texture descriptors which 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 description. Furthermore, local contrast information can be integrated into LBP leading to LBP variance (LBPV) features. In conventional LBP methods, the histograms corresponding to different radii are simply concatenated resulting in a loss of information between different resolutions and added ambiguity. Multi-dimensional LBPV (MD-LBPV) preserves the relationships between the scales by building a multi-dimensional histogram of LBPV patterns and can lead to improved texture classification. In this paper, we address the relatively large feature length of MD-LBPV descriptors, and show that feature reduction based on discrete cosine transform (DCT) combined with principal component analysis (PCA) can yield effective and compact texture descriptors with high classification accuracy.

Texture recognition plays an important role in many computer vision tasks including segmentation, scene understanding and interpretation, medical imaging and object recognition. In some situations, the correct identification of particular textures is more important compared to others, for example recognition of enemy uniforms for automatic defense systems, or isolation of textures related to tumors in medical images. Such cost-sensitive texture classification is the focus of this paper, which we address by reformulating the classification problem as a cost minimisation problem. We do this by constructing a cost-sensitive classifier ensemble that is tuned using a genetic algorithm. Based on experimental results obtained on several Outex datasets with cost definitions, we show our approach to work well in comparison with canonical classification methods and the ensemble approach to lead to better performance compared to single predictors.

Indirect immunofluorescence imaging is employed to identify antinuclear antibodies in HEp-2 cells which founds the basis for diagnosing autoimmune diseases and other important pathological conditions involving the immune system. Six categories of HEp-2 cells are generally considered, namely homogeneous, fine speckled, coarse speckled, nucleolar, cyto-plasmic, and centromere cells. Typically, this categorisation is performed manually by an expert and is hence both time consuming and subjective. In this paper, we present a method for automatically classifiying HEp-2 cells using texture information in conjunction with a suitable classification system. In particular, we extract multidimensional local binary pattern (MD-LBP) texture features to characterise the cell area. These then form the input for a classification stage, for which we employ a margin distribution based bagging pruning (MAD-Bagging) classifier ensemble. We evaluate our algorithm on the ICPR 2012 HEp-2 contest benchmark dataset, and demonstrate it to give excellent performance, superior to all algorithms that were entered in the competition.

Texture analysis and classification play an important role in many multimedia and computer vision applications. Local binary patterns (LBP) form a simple yet powerful texture descriptor characterising local neighbourhood properties, and consequently LBP variants are widely employed. In this paper, we demonstrate that through appropriate construction of a multiple classifier system, improved texture classification based on LBP features is possible. In particular, we employ a classifier ensemble where each classifier (a support vector machine) is trained in conjunction with a different feature selection method. The ensemble is then pruned based on a diversity measure, and the remaining models are combined using a neural fuser. Experimental results, obtained on Outex benchmark datasets and employing four LBP variants, confirm that our proposed approach leads to statistically significantly improved texture classification.