Fast and Accurate Texture Recognition with Multilayer Convolution and Multifractal Analysis

International audienceA fast and accurate texture recognition system is presented. The new approach consists in extracting locally and globally invariant representations. The locally invariant representation is built on a multi-resolution convolutional net- work with a local pooling operator to improve robustness to local orientation and scale changes. This representation is mapped into a globally invariant descriptor using multifractal analysis. We propose a new multifractal descriptor that cap- tures rich texture information and is mathematically invariant to various complex transformations. In addition, two more techniques are presented to further im- prove the robustness of our system. The first technique consists in combining the generative PCA classifier with multiclass SVMs. The second technique consists of two simple strategies to boost classification results by synthetically augment- ing the training set. Experiments show that the proposed solution outperforms existing methods on three challenging public benchmark datasets, while being computationally efficient

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Longitudinal Brain Tumor Tracking, Tumor Grading, and Patient Survival Prediction Using MRI

This work aims to develop novel methods for brain tumor classification, longitudinal brain tumor tracking, and patient survival prediction. Consequently, this dissertation proposes three tasks. First, we develop a framework for brain tumor segmentation prediction in longitudinal multimodal magnetic resonance imaging (mMRI) scans, comprising two methods: feature fusion and joint label fusion (JLF). The first method fuses stochastic multi-resolution texture features with tumor cell density features, in order to obtain tumor segmentation predictions in follow-up scans from a baseline pre-operative timepoint. The second method utilizes JLF to combine segmentation labels obtained from (i) the stochastic texture feature-based and Random Forest (RF)-based tumor segmentation method; and (ii) another state-of-the-art tumor growth and segmentation method known as boosted Glioma Image Segmentation and Registration (GLISTRboost, or GB). With the advantages of feature fusion and label fusion, we achieve state-of-the-art brain tumor segmentation prediction. Second, we propose a deep neural network (DNN) learning-based method for brain tumor type and subtype grading using phenotypic and genotypic data, following the World Health Organization (WHO) criteria. In addition, the classification method integrates a cellularity feature which is derived from the morphology of a pathology image to improve classification performance. The proposed method achieves state-of-the-art performance for tumor grading following the new CNS tumor grading criteria. Finally, we investigate brain tumor volume segmentation, tumor subtype classification, and overall patient survival prediction, and then we propose a new context- aware deep learning method, known as the Context Aware Convolutional Neural Network (CANet). Using the proposed method, we participated in the Multimodal Brain Tumor Segmentation Challenge 2019 (BraTS 2019) for brain tumor volume segmentation and overall survival prediction tasks. In addition, we also participated in the Radiology-Pathology Challenge 2019 (CPM-RadPath 2019) for Brain Tumor Subtype Classification, organized by the Medical Image Computing & Computer Assisted Intervention (MICCAI) Society. The online evaluation results show that the proposed methods offer competitive performance from their use of state-of-the-art methods in tumor volume segmentation, promising performance on overall survival prediction, and state-of-the-art performance on tumor subtype classification. Moreover, our result was ranked second place in the testing phase of the CPM-RadPath 2019

A Multifractal-based Wavefront Phase Estimation Technique for Ground-based Astronomical Observations

International audienceTurbulence in the Earth's atmosphere interferes with the propagation of planar wavefronts from outer space resulting in a phase distorted non-planar wavefront. This phase distortion is responsible for the refractive blurring of images accounting to the loss in spatial resolution power of ground-based telescopes. The technology widely used to remove this phase distortion is Adaptive Optics (AO). In AO, an estimate of the distorted phase is provided by a wavefront sensor (WFS) in the form of low-resolution slope measurements of the wavefront. The estimate is then used to create a corrected wavefront, that (approximately) removes the phase distortion from the incoming wavefronts. Phase reconstruction from WFS measurements is done by solving large linear systems followed by interpolating the low-resolution phase to its desired high-resolution. In this paper, we propose an alternate technique to wavefront phase reconstruction using concepts derived from the Microcanonical Multiscale Formalism (MMF), which is a specific approach to multifractality. We take into account an a priori information of the wavefront phase, provided by the multifractal exponents. Then through the framework of multiresolution analysis and wavelet transform, we address the problem of phase reconstruction from low-resolution WFS measurements. Comparison, in terms of reconstruction quality, with classical techniques in AO proves the superiority of our approach

The utility of complex soil reflectance image properties for soil mapping

This investigation is concerned with the application of complex quantitative analysis to remotely sensed data for mapping soils. The major aim of this thesis is to examine, by means of illustrative examples, the utility of complex image metrics in the detection, differentiation, and partitioning of satellite images of soil landscapes. Satellite images have been widely used for soil mapping. In order to realise the maximum potential of satellite imagery, improvements are needed both in visual presentation of such images, and in their automatic classification, in order to reveal the complex properties of soil landscape. A Landsat TM image of the Al-Ahsa area of Saudi Arabia was used in the investigation. It presents an ideal region for remote sensing studies due to the absence of vegetation cover and the existence of different type of landforms in a region of low topography. Three techniques for modelling complex elements of images were used and evaluated; Fast Fourier Transform (FFT), Artificial Neural Network Analysis (ANN), Fractal and Multifractal Analysis. The FFT technique developed in this thesis isolates spatial frequency components in specific wavebands. The inverse FFT images are enhanced to (i) display optimised zoning of the image, and (ii) to display specific features. This technique partitions images into major zones that are different zones from the standard soil maps. The ANN technique developed is a non-linear measure of image texture. It shows difference within an image. The texture model is trained on areas selected on the basis of the existing soil map. Substitution analysis of training areas allows an assessment of image zones and boundaries. The texture image is displayed by linear contrast stretch. Zonation does not correspond with published maps or with FFT zonation. The fractal method is based on the local fractal dimension that is used as a texture measure based on a moving pre-set size filter over the entire image. The resulting images do not give zones but shows clear patterns of complexity such as spatial transitions. It is possible to derive areas of similar patterns of transition in complexity. There are implications of these results for soil mapping at the theoretical and practical levels. The implications of the theoretical level are about the existences of soil units defined following the classical approach. In the practical level, the classical approach would be abandoned. There is at present nowhere near the same support of the ideas to complement the traditional mapping approach and raise awareness that soils are inherently complex. The study has important implications for classical theory and practice of soil mapping

Multifractal techniques for analysis and classification of emphysema images

This thesis proposes, develops and evaluates different multifractal methods for detection, segmentation and classification of medical images. This is achieved by studying the structures of the image and extracting the statistical self-similarity measures characterized by the Holder exponent, and using them to develop texture features for segmentation and classification. The theoretical framework for fulfilling these goals is based on the efficient computation of fractal dimension, which has been explored and extended in this work. This thesis investigates different ways of computing the fractal dimension of digital images and validates the accuracy of each method with fractal images with predefined fractal dimension. The box counting and the Higuchi methods are used for the estimation of fractal dimensions. A prototype system of the Higuchi fractal dimension of the computed tomography (CT) image is used to identify and detect some of the regions of the image with the presence of emphysema. The box counting method is also used for the development of the multifractal spectrum and applied to detect and identify the emphysema patterns. We propose a multifractal based approach for the classification of emphysema patterns by calculating the local singularity coefficients of an image using four multifractal intensity measures. One of the primary statistical measures of self-similarity used in the processing of tissue images is the Holder exponent (α-value) that represents the power law, which the intensity distribution satisfies in the local pixel neighbourhoods. The fractal dimension corresponding to each α-value gives a multifractal spectrum f(α) that was used as a feature descriptor for classification. A feature selection technique is introduced and implemented to extract some of the important features that could increase the discriminating capability of the descriptors and generate the maximum classification accuracy of the emphysema patterns. We propose to further improve the classification accuracy of emphysema CT patterns by combining the features extracted from the alpha-histograms and the multifractal descriptors to generate a new descriptor. The performances of the classifiers are measured by using the error matrix and the area under the receiver operating characteristic curve (AUC). The results at this stage demonstrated the proposed cascaded approach significantly improves the classification accuracy. Another multifractal based approach using a direct determination approach is investigated to demonstrate how multifractal characteristic parameters could be used for the identification of emphysema patterns in HRCT images. This further analysis reveals the multi-scale structures and characteristic properties of the emphysema images through the generalized dimensions. The results obtained confirm that this approach can also be effectively used for detecting and identifying emphysema patterns in CT images. Two new descriptors are proposed for accurate classification of emphysema patterns by hybrid concatenation of the local features extracted from the local binary patterns (LBP) and the global features obtained from the multifractal images. The proposed combined feature descriptors of the LBP and f(α) produced a very good performance with an overall classification accuracy of 98%. These performances outperform other state-of-the-art methods for emphysema pattern classification and demonstrate the discriminating power and robustness of the combined features for accurate classification of emphysema CT images. Overall, experimental results have shown that the multifractal could be effectively used for the classifications and detections of emphysema patterns in HRCT images