ANALYSIS OF MAMMOGRAM FOR DETECTION OF BREAST CANCER USING WAVELET STATISTICAL FEATURES

Early detection of breast cancer increases the survival rate and increases the treatment options. One of the most powerful techniques for early detection of breast cancer is based on digital mammogram. A system can be developed for assisting the analysis of digital mammograms using log-Gabor wavelet statistical features. The proposed system involves three major steps called Pre-processing, Processing, and Feature extraction. In pre-processing, the digital mammogram can be de-noised using efficient decision-based algorithm. In processing stage, the suspicious Region of Interest (ROI) can be cropped and convolved with log-Gabor filter for four different orientations. Then gray level co-occurrence matrix (GLCM)can be constructed for log-Gabor filter output at four different orientations and from that first order statistical features and second order statistical features can be extracted to analyze whether the mammogram as normal or benign or malignant. The proposed method can allow the radiologist to focus rapidly on the relevant parts of the mammogram and it can increase the effectiveness and efficiency of radiology clinics

Visualizing Information on a Sphere

We describe a method for the visualization of information units on spherical domains which is employed in the banking industry for risk analysis, stock prediction and other tasks. The system is based on a quantification of the similarity of related objects that governs the parameters of a mass-spring system. Unlike existing approaches we initialize all information units onto the inner surface of two concentric spheres and attach them with springs to the outer sphere. Since the spring stiffnesses correspond to the computed similarity measures, the system converges into an energy minimum which reveals multidimensional relations and adjacencies in terms of spatial neighborhoods. Depending on the application scenario our approach supports different topological arrangements of related objects. In order to cope with large data sets we propose a blobby clustering mechanism that enables encapsulation of similar objects by implicit shapes. In addition, we implemented various interaction techniques allowing semantic analysis of the underlying data sets. Our prototype system IVORY is written in JAVA, and its versatility is illustrated by an example from financial service providers

Overcomplete Image Representations for Texture Analysis

Advisor/s: Dr. Boris Escalante-Ramírez and Dr. Gabriel Cristóbal. Date and location of PhD thesis defense: 23th October 2013, Universidad Nacional Autónoma de México.In recent years, computer vision has played an important role in many scientific and technological areas mainlybecause modern society highlights vision over other senses. At the same time, application requirements and complexity have also increased so that in many cases the optimal solution depends on the intrinsic charac-teristics of the problem; therefore, it is difficult to propose a universal image model. In parallel, advances in understanding the human visual system have allowed to propose sophisticated models that incorporate simple phenomena which occur in early stages of the visual system. This dissertation aims to investigate characteristicsof vision such as over-representation and orientation of receptive fields in order to propose bio-inspired image models for texture analysis

New contributions in overcomplete image representations inspired from the functional architecture of the primary visual cortex = Nuevas contribuciones en representaciones sobrecompletas de imágenes inspiradas por la arquitectura funcional de la corteza visual primaria

The present thesis aims at investigating parallelisms between the functional architecture of primary visual areas and image processing methods. A first objective is to refine existing models of biological vision on the base of information theory statements and a second is to develop original solutions for image processing inspired from natural vision. The available data on visual systems contains physiological and psychophysical studies, Gestalt psychology and statistics on natural images The thesis is mostly centered in overcomplete representations (i.e. representations increasing the dimensionality of the data) for multiple reasons. First because they allow to overcome existing drawbacks of critically sampled transforms, second because biological vision models appear overcomplete and third because building efficient overcomplete representations raises challenging and actual mathematical problems, in particular the problem of sparse approximation. The thesis proposes first a self-invertible log-Gabor wavelet transformation inspired from the receptive field and multiresolution arrangement of the simple cells in the primary visual cortex (V1). This transform shows promising abilities for noise elimination. Second, interactions observed between V1 cells consisting in lateral inhibition and in facilitation between aligned cells are shown efficient for extracting edges of natural images. As a third point, the redundancy introduced by the overcompleteness is reduced by a dedicated sparse approximation algorithm which builds a sparse representation of the images based on their edge content. For an additional decorrelation of the image information and for improving the image compression performances, edges arranged along continuous contours are coded in a predictive manner through chains of coefficients. This offers then an efficient representation of contours. Fourth, a study on contour completion using the tensor voting framework based on Gestalt psychology is presented. There, the use of iterations and of the curvature information allow to improve the robustness and the perceptual quality of the existing method. La presente tesis doctoral tiene como objetivo indagar en algunos paralelismos entre la arquitectura funcional de las áreas visuales primarias y el tratamiento de imágenes. Un primer objetivo consiste en mejorar los modelos existentes de visión biológica basándose en la teoría de la información. Un segundo es el desarrollo de nuevos algoritmos de tratamiento de imágenes inspirados de la visión natural. Los datos disponibles sobre el sistema visual abarcan estudios fisiológicos y psicofísicos, psicología Gestalt y estadísticas de las imágenes naturales. La tesis se centra principalmente en las representaciones sobrecompletas (i.e. representaciones que incrementan la dimensionalidad de los datos) por las siguientes razones. Primero porque permiten sobrepasar importantes desventajas de las transformaciones ortogonales; segundo porque los modelos de visión biológica necesitan a menudo ser sobrecompletos y tercero porque construir representaciones sobrecompletas eficientes involucra problemas matemáticos relevantes y novedosos, en particular el problema de las aproximaciones dispersas. La tesis propone primero una transformación en ondículas log-Gabor auto-inversible inspirada del campo receptivo y la organización en multiresolución de las células simples del cortex visual primario (V1). Esta transformación ofrece resultados prometedores para la eliminación del ruido. En segundo lugar, las interacciones observadas entre las células de V1 que consisten en la inhibición lateral y en la facilitación entre células alineadas se han mostrado eficientes para extraer los bordes de las imágenes naturales. En tercer lugar, la redundancia introducida por la transformación sobrecompleta se reduce gracias a un algoritmo dedicado de aproximación dispersa el cual construye una representación dispersa de las imágenes sobre la base de sus bordes. Para una decorrelación adicional y para conseguir más altas tasas de compresión, los bordes alineados a lo largo de contornos continuos están codificado de manera predictiva por cadenas de coeficientes, lo que ofrece una representacion eficiente de los contornos. Finalmente se presenta un estudio sobre el cierre de contornos utilizando la metodología de tensor voting. Proponemos el uso de iteraciones y de la información de curvatura para mejorar la robustez y la calidad perceptual de los métodos existentes

New wavelet based space-frequency analysis methods applied to the characterisation of 3-dimensional engineering surface textures.

The aim of this work was to use resources coming from the field of signal and image processing to make progress solving real problems of surface texture characterisation. A measurement apparatus like a microscope gives a representation of a surface textures that can be seen as an image. This is actually an image representing the relief of the surface texture. From the image processing point of view, this problem takes the form of texture analysis. The introduction of the problem as one of texture analysis is presented as well as the proposed solution: a wavelet based method for texture characterisation. Actually, more than a simple wavelet transform, an entire original characterisation method is described. A new tool based on the frequency normalisation of the well-known wavelet transform has been designed for the purpose of this study and is introduced, explained and illustrated in this thesis. This tool allows the drawing of a real space-frequency map of any image and especially textured images. From this representation, which can be compared to music notation, simple parameters are calculated. They give information about texture features on several scales and can be compared to hybrid parameters commonly used in surface roughness characterisation. Finally, these parameters are used to feed a decision-making system. In order to come back to the first motivation of the study, this analysis strategy is applied to real engineered surface characterisation problems. The first application is the discrimination of surface textures, which superficially have similar characteristics according to some standard parameters. The second application is the monitoring of a grinding process. A new approach to the problem of surface texture analysis is introduced. The principle of this new approach, well known in image processing, is not to give an absolute measure of the characteristics of a surface, but to classify textures relative to each other in a space where the distance between them indicates their similarity