3D medical volume segmentation using hybrid multiresolution statistical approaches

This article is available through the Brunel Open Access Publishing Fund. Copyright © 2010 S AlZu’bi and A Amira.3D volume segmentation is the process of partitioning voxels into 3D regions (subvolumes) that represent meaningful physical entities which are more meaningful and easier to analyze and usable in future applications. Multiresolution Analysis (MRA) enables the preservation of an image according to certain levels of resolution or blurring. Because of multiresolution quality, wavelets have been deployed in image compression, denoising, and classification. This paper focuses on the implementation of efficient medical volume segmentation techniques. Multiresolution analysis including 3D wavelet and ridgelet has been used for feature extraction which can be modeled using Hidden Markov Models (HMMs) to segment the volume slices. A comparison study has been carried out to evaluate 2D and 3D techniques which reveals that 3D methodologies can accurately detect the Region Of Interest (ROI). Automatic segmentation has been achieved using HMMs where the ROI is detected accurately but suffers a long computation time for its calculations

Novel techniques of computational intelligence for analysis of astronomical structures

Gravitational forces cause the formation and evolution of a variety of cosmological structures. The detailed investigation and study of these structures is a crucial step towards our understanding of the universe. This thesis provides several solutions for the detection and classification of such structures. In the first part of the thesis, we focus on astronomical simulations, and we propose two algorithms to extract stellar structures. Although they follow different strategies (while the first one is a downsampling method, the second one keeps all samples), both techniques help to build more effective probabilistic models. In the second part, we consider observational data, and the goal is to overcome some of the common challenges in observational data such as noisy features and imbalanced classes. For instance, when not enough examples are present in the training set, two different strategies are used: a) nearest neighbor technique and b) outlier detection technique. In summary, both parts of the thesis show the effectiveness of automated algorithms in extracting valuable information from astronomical databases

Deep Divergence-Based Approach to Clustering

A promising direction in deep learning research consists in learning representations and simultaneously discovering cluster structure in unlabeled data by optimizing a discriminative loss function. As opposed to supervised deep learning, this line of research is in its infancy, and how to design and optimize suitable loss functions to train deep neural networks for clustering is still an open question. Our contribution to this emerging field is a new deep clustering network that leverages the discriminative power of information-theoretic divergence measures, which have been shown to be effective in traditional clustering. We propose a novel loss function that incorporates geometric regularization constraints, thus avoiding degenerate structures of the resulting clustering partition. Experiments on synthetic benchmarks and real datasets show that the proposed network achieves competitive performance with respect to other state-of-the-art methods, scales well to large datasets, and does not require pre-training steps

Algoritmo Non-Local Means e Otimizações.

Image denoising is an important part of digital image processing, and many approaches were proposed to enhance the visualization of images. The Non-Local Means algorithm has great results in noise removal, although its computational complexity is high, making the algorithm not viable for practical use. Many papers have been presented with the objective of enhance the performance of the filter and reduce the complexity of the algorithm. This paper shows the algorithm introduced by Buades, Coll and Morel, as well as some approaches to optimize the algorithm.A remoção de ruído é uma parte importante dentro do processamento digital de imagens, e várias abordagens foram propostas para melhorar a visualização das imagens. O algoritmo Non-Local Means apresenta ótimos resultados na remoção de ruído, porém sua complexidade computacional é alta, tornando o algoritmo inviável em termos práticos. Com o objetivo de melhorar o desempenho da filtragem e reduzir a complexidade do algoritmo, vários trabalhos vêm sendo apresentados. Este trabalho apresenta o algoritmo introduzido por Buades, Coll e Morel, além de algumas propostas para otimização do algoritmo

Representation Learning: A Review and New Perspectives

The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, auto-encoders, manifold learning, and deep networks. This motivates longer-term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation and manifold learning

Second generation sparse models

Sparse data models, where data is assumed to be well represented as a linear combination of a few elements from a learned dictionary, have gained considerable attention in recent years, and their use has led to state-of-the-art results in many applications. The success of these models is largely attributed to two critical features: the use of sparsity as a robust mechanism for regularizing the linear coefficients that represent the data, and the flexibility provided by overcomplete dictionaries that are learned from the data. These features are controlled by two critical hyper-parameters: the desired sparsity of the coefficients, and the size of the dictionaries to be learned. However, lacking theoretical guidelines for selecting these critical parameters, applications based on sparse models often require hand-tuning and cross-validation to select them, for each application, and each data set. This can be both inefficient and ineffective. On the other hand, there are multiple scenarios in which imposing additional constraints to the produced representations, including the sparse codes and the dictionary itself, can result in further improvements. This thesis is about improving and/or extending current sparse models by addressing the two issues discussed above, providing the elements for a new generation of more powerful and flexible sparse models. First, we seek to gain a better understanding of sparse models as data modeling tools, so that critical parameters can be selected automatically, efficiently, and in a principled way. Secondly, we explore new sparse modeling formulations for effectively exploiting the prior information present in different scenarios. In order to achieve these goals, we combine ideas and tools from information theory, statistics, machine learning, and optimization theory. The theoretical contributions are complemented with applications in audio, image and video processing

Self-Guided Diffusion Models

Diffusion models have demonstrated remarkable progress in image generation quality, especially when guidance is used to control the generative process. However, guidance requires a large amount of image-annotation pairs for training and is thus dependent on their availability, correctness and unbiasedness. In this paper, we eliminate the need for such annotation by instead leveraging the flexibility of self-supervision signals to design a framework for self-guided diffusion models. By leveraging a feature extraction function and a self-annotation function, our method provides guidance signals at various image granularities: from the level of holistic images to object boxes and even segmentation masks. Our experiments on single-label and multi-label image datasets demonstrate that self-labeled guidance always outperforms diffusion models without guidance and may even surpass guidance based on ground-truth labels, especially on unbalanced data. When equipped with self-supervised box or mask proposals, our method further generates visually diverse yet semantically consistent images, without the need for any class, box, or segment label annotation. Self-guided diffusion is simple, flexible and expected to profit from deployment at scale

Patch-based Denoising Algorithms for Single and Multi-view Images

In general, all single and multi-view digital images are captured using sensors, where they are often contaminated with noise, which is an undesired random signal. Such noise can also be produced during transmission or by lossy image compression. Reducing the noise and enhancing those images is among the fundamental digital image processing tasks. Improving the performance of image denoising methods, would greatly contribute to single or multi-view image processing techniques, e.g. segmentation, computing disparity maps, etc. Patch-based denoising methods have recently emerged as the state-of-the-art denoising approaches for various additive noise levels. This thesis proposes two patch-based denoising methods for single and multi-view images, respectively. A modification to the block matching 3D algorithm is proposed for single image denoising. An adaptive collaborative thresholding filter is proposed which consists of a classification map and a set of various thresholding levels and operators. These are exploited when the collaborative hard-thresholding step is applied. Moreover, the collaborative Wiener filtering is improved by assigning greater weight when dealing with similar patches. For the denoising of multi-view images, this thesis proposes algorithms that takes a pair of noisy images captured from two different directions at the same time (stereoscopic images). The structural, maximum difference or the singular value decomposition-based similarity metrics is utilized for identifying locations of similar search windows in the input images. The non-local means algorithm is adapted for filtering these noisy multi-view images. The performance of both methods have been evaluated both quantitatively and qualitatively through a number of experiments using the peak signal-to-noise ratio and the mean structural similarity measure. Experimental results show that the proposed algorithm for single image denoising outperforms the original block matching 3D algorithm at various noise levels. Moreover, the proposed algorithm for multi-view image denoising can effectively reduce noise and assist to estimate more accurate disparity maps at various noise levels