Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised Classification

We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or non-vessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and continuous two-dimensional Morlet wavelet transform responses taken at multiple scales. The Morlet wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces and compare its performance with the linear minimum squared error classifier. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE and STARE databases of manually labeled non-mydriatic images. On the DRIVE database, it achieves an area under the receiver operating characteristic (ROC) curve of 0.9598, being slightly superior than that presented by the method of Staal et al.Comment: 9 pages, 7 figures and 1 table. Accepted for publication in IEEE Trans Med Imag; added copyright notic

Brain tissue segmentation using q-entropy in multiple sclerosis magnetic resonance images

The loss of brain volume has been used as a marker of tissue destruction and can be used as an index of the progression of neurodegenerative diseases, such as multiple sclerosis. In the present study, we tested a new method for tissue segmentation based on pixel intensity threshold using generalized Tsallis entropy to determine a statistical segmentation parameter for each single class of brain tissue. We compared the performance of this method using a range of different q parameters and found a different optimal q parameter for white matter, gray matter, and cerebrospinal fluid. Our results support the conclusion that the differences in structural correlations and scale invariant similarities present in each tissue class can be accessed by generalized Tsallis entropy, obtaining the intensity limits for these tissue class separations. In order to test this method, we used it for analysis of brain magnetic resonance images of 43 patients and 10 healthy controls matched for gender and age. The values found for the entropic q index were 0.2 for cerebrospinal fluid, 0.1 for white matter and 1.5 for gray matter. With this algorithm, we could detect an annual loss of 0.98% for the patients, in agreement with literature data. Thus, we can conclude that the entropy of Tsallis adds advantages to the process of automatic target segmentation of tissue classes, which had not been demonstrated previously.Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)FAPESPCNP

Combining local regularity estimation and total variation optimization for scale-free texture segmentation

Texture segmentation constitutes a standard image processing task, crucial to many applications. The present contribution focuses on the particular subset of scale-free textures and its originality resides in the combination of three key ingredients: First, texture characterization relies on the concept of local regularity ; Second, estimation of local regularity is based on new multiscale quantities referred to as wavelet leaders ; Third, segmentation from local regularity faces a fundamental bias variance trade-off: In nature, local regularity estimation shows high variability that impairs the detection of changes, while a posteriori smoothing of regularity estimates precludes from locating correctly changes. Instead, the present contribution proposes several variational problem formulations based on total variation and proximal resolutions that effectively circumvent this trade-off. Estimation and segmentation performance for the proposed procedures are quantified and compared on synthetic as well as on real-world textures

Variation of the frictional anisotropy on ventral scales of snakes caused by nanoscale steps

The ventral scales of most snakes feature micron-sized fibril structures with nanoscale steps oriented towards the snake\u27s tail. We examined these structures by microtribometry as well as atomic force microscopy (AFM) and observed that the nanoscale steps of the micro-fibrils cause a frictional anisotropy, which varies along the snake\u27s body in dependence of the height of the nanoscale steps. A significant frictional behavior is detected when a sharp AFM tip scans the nanoscale steps up or down. Larger friction peaks appear during upward scans (tail to head direction), while considerably lower peaks are observed for downward scans (head to tail direction). This effect causes a frictional anisotropy on the nanoscale, i.e. friction along the head to tail direction is lower than in the opposite direction. The overall effect increases linearly with the step height of the micro-fibrils. Although the step heights are different for each snake, the general step height distribution along the body of the examined snakes follows a common pattern. The frictional anisotropy, induced by the step height distribution, is largest close to the tail, intermediate in the middle, and lower close to the head. This common distribution of frictional anisotropy suggests that snakes even optimized nanoscale features like the height of micro-fibrils through evolution in order to achieve optimal friction performance for locomotion. Finally, ventral snake scales are replicated by imprinting their micro-fibril structures into a polymer. As the natural prototype, the artificial surface exhibits frictional anisotropy in dependence of the respective step height. This feature is of high interest for the design of tribological surfaces with artificial frictional anisotropy

Fully automatic lesion boundary detection in ultrasound breast images

We propose a novel approach to fully automatic lesion boundary detection in ultrasound breast images. The novelty of the proposed work lies in the complete automation of the manual process of initial Region-of-Interest (ROI) labeling and in the procedure adopted for the subsequent lesion boundary detection. Histogram equalization is initially used to preprocess the images followed by hybrid filtering and multifractal analysis stages. Subsequently, a single valued thresholding segmentation stage and a rule-based approach is used for the identification of the lesion ROI and the point of interest that is used as the seed-point. Next, starting from this point an Isotropic Gaussian function is applied on the inverted, original ultrasound image. The lesion area is then separated from the background by a thresholding segmentation stage and the initial boundary is detected via edge detection. Finally to further improve and refine the initial boundary, we make use of a state-of-the-art active contour method (i.e. gradient vector flow (GVF) snake model). We provide results that include judgments from expert radiologists on 360 ultrasound images proving that the final boundary detected by the proposed method is highly accurate. We compare the proposed method with two existing stateof- the-art methods, namely the radial gradient index filtering (RGI) technique of Drukker et. al. and the local mean technique proposed by Yap et. al., in proving the proposed method’s robustness and accuracy

Continuum description of living matter

Living systems are one of the most complex forms of matter. Among their many different aspects, one unifying property of any living matter is activity. Active materials continually extract energy from their surroundings to move, grow, and self-replicate. To perform these functions, active units exert forces on their environment and this raises the question of the extent to which the behaviour of biological systems can be understood by measuring forces and flows and using the generic physical theories of active matter. At the microscale, several important aspects of the dynamics of living systems can be described by the theories of active nematics. We discuss, in the second chapter of this thesis, that long-range interactions in active suspensions at the microscale have a nematic symmetry, and this makes active nematic models a powerful tool for studying living systems. In this thesis, we extract essential features from living systems in experiments to build continuum active nematic models that are able to capture the behaviour of the experiments. Using the continuum models, we then perform numerical simulations and linear stability analyses to explore the dynamical steady states of the model, and compare them with the ones in the experiments. We show that the conventional active nematic models need to be modified and evolved, according to the active system under study, to allow us to capture the patterns observed in different experiments. Our results indicate that fractal-like patterns that form at the interface of bacterial droplets cannot be explained by the conventional active biphasic models. We suggest that the presence of an active layer at the bacterial interface produces the cell orientation and flow patterns in the experiments. We then construct a continuum description for living tissues and demonstrate that introducing new continuum fields associated with cell area and aspect ratio and decoupling the direction of the active force from the cell elongation, introduces new phases to the system. Analysing experimental data on MDCK cells, we use our model to understand the misalignment in the cell shape and stress orientation in the experiments. Finally, we extend our study to three dimensions and show that active materials with extensile activity promote formation of twist-type defects and three-dimensional flows