A Review

Ovarian cancer is the most common cause of death among gynecological malignancies. We discuss different types of clinical and nonclinical features that are used to study and analyze the differences between benign and malignant ovarian tumors. Computer-aided diagnostic (CAD) systems of high accuracy are being developed as an initial test for ovarian tumor classification instead of biopsy, which is the current gold standard diagnostic test. We also discuss different aspects of developing a reliable CAD system for the automated classification of ovarian cancer into benign and malignant types. A brief description of the commonly used classifiers in ultrasound-based CAD systems is also given

A General Framework for Robust G-Invariance in G-Equivariant Networks

We introduce a general method for achieving robust group-invariance in group-equivariant convolutional neural networks ($G$-CNNs), which we call the $G$-triple-correlation ($G$-TC) layer. The approach leverages the theory of the triple-correlation on groups, which is the unique, lowest-degree polynomial invariant map that is also complete. Many commonly used invariant maps - such as the max - are incomplete: they remove both group and signal structure. A complete invariant, by contrast, removes only the variation due to the actions of the group, while preserving all information about the structure of the signal. The completeness of the triple correlation endows the $G$-TC layer with strong robustness, which can be observed in its resistance to invariance-based adversarial attacks. In addition, we observe that it yields measurable improvements in classification accuracy over standard Max $G$-Pooling in $G$-CNN architectures. We provide a general and efficient implementation of the method for any discretized group, which requires only a table defining the group's product structure. We demonstrate the benefits of this method for $G$-CNNs defined on both commutative and non-commutative groups - $SO(2)$, $O(2)$, $SO(3)$, and $O(3)$ (discretized as the cyclic $C8$, dihedral $D16$, chiral octahedral $O$ and full octahedral $O_h$ groups) - acting on $\mathbb{R}^2$ and $\mathbb{R}^3$ on both $G$-MNIST and $G$-ModelNet10 datasets

Segmentation and texture analysis with multimodel inference for the automatic detection of exudates in early diabetic retinopathy

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Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers

Primordial non-Gaussianity and Bispectrum Measurements in the Cosmic Microwave Background and Large-Scale Structure

The most direct probe of non-Gaussian initial conditions has come from bispectrum measurements of temperature fluctuations in the Cosmic Microwave Background and of the matter and galaxy distribution at large scales. Such bispectrum estimators are expected to continue to provide the best constraints on the non-Gaussian parameters in future observations. We review and compare the theoretical and observational problems, current results and future prospects for the detection of a non-vanishing primordial component in the bispectrum of the Cosmic Microwave Background and large-scale structure, and the relation to specific predictions from different inflationary models.Comment: 82 pages, 23 figures; Invited Review for the special issue "Testing the Gaussianity and Statistical Isotropy of the Universe" for Advances in Astronom

Supervised and unsupervised segmentation of textured images by efficient multi-level pattern classification

This thesis proposes new, efficient methodologies for supervised and unsupervised image segmentation based on texture information. For the supervised case, a technique for pixel classification based on a multi-level strategy that iteratively refines the resulting segmentation is proposed. This strategy utilizes pattern recognition methods based on prototypes (determined by clustering algorithms) and support vector machines. In order to obtain the best performance, an algorithm for automatic parameter selection and methods to reduce the computational cost associated with the segmentation process are also included. For the unsupervised case, the previous methodology is adapted by means of an initial pattern discovery stage, which allows transforming the original unsupervised problem into a supervised one. Several sets of experiments considering a wide variety of images are carried out in order to validate the developed techniques.Esta tesis propone metodologías nuevas y eficientes para segmentar imágenes a partir de información de textura en entornos supervisados y no supervisados. Para el caso supervisado, se propone una técnica basada en una estrategia de clasificación de píxeles multinivel que refina la segmentación resultante de forma iterativa. Dicha estrategia utiliza métodos de reconocimiento de patrones basados en prototipos (determinados mediante algoritmos de agrupamiento) y máquinas de vectores de soporte. Con el objetivo de obtener el mejor rendimiento, se incluyen además un algoritmo para selección automática de parámetros y métodos para reducir el coste computacional asociado al proceso de segmentación. Para el caso no supervisado, se propone una adaptación de la metodología anterior mediante una etapa inicial de descubrimiento de patrones que permite transformar el problema no supervisado en supervisado. Las técnicas desarrolladas en esta tesis se validan mediante diversos experimentos considerando una gran variedad de imágenes

Thermal Characterization of Infrared Images for Breast Cancer Detection

One of the most common diseases affecting women is breast cancer. As a result, diagnosis and monitoring of breast health is viewed as a lifesaving procedure. Thermography shows promise for diagnosing breast cancer especially for dense breasts on which mammography does not work well. It is a noninvasive and inexpensive method based on the principle that malignant tumors have a higher metabolic rate and therefore emit more heat. A new segmentation algorithm was developed for a recent database of thermal images obtained from patients lying in the prone position. The algorithm was successful in isolating the breast from rest of the body and background. The resulting segmentation was then analyzed using temperature profiles, where significant peaks in the profile indicated a region of interest. Lastly, approximate hot spots were found that indicated the location of a possible tumor

Characterizing Ice Cloud Particle Shape and Surface Roughness from Polarimetric Satellite Observations

The single scattering properties of ice cloud particles are inferred from spaceborne multi-angle satellite sensors with two newly developed noise-resilient retrieval techniques. The first presented method parameterizes the phase function and phase matrix elements by a few parameters to implement the maximum likelihood estimation in the retrieval system. The second method retrieves the renormalized phase function as a difference from a known phase function. The effect of noise is more predictable for both methods than the conventional “best-fit” method, which selects the best-fitting shape and surface roughness from a predetermined particle set. The first method is applied to the data from the Polarization and Directionality of the Earth’s Reflectance (POLDER) sensor. The retrieval results indicate that long column shape (ratio of basal face diameter to prism height greater than 9) with surface roughness parameter between 0.1 and 0.5 represents the extratropical observations well. Weak temperature dependence of the surface roughness is found in the extratropical data stratified by the cloud top temperature. The tropical retrieval was not successful, and the second method is applied to the Multi-angle Imaging Spectroradiometer (MISR) data. Short hexagonal column particles or their aggregates are found to match with estimated renormalized phase function. In addition to these results, the surface roughness simulation is summarized and the derivation of the δ-fit truncation technique for polarimetric radiative transfer is included

Characterizing Ice Cloud Particle Shape and Surface Roughness from Polarimetric Satellite Observations

The single scattering properties of ice cloud particles are inferred from spaceborne multi-angle satellite sensors with two newly developed noise-resilient retrieval techniques. The first presented method parameterizes the phase function and phase matrix elements by a few parameters to implement the maximum likelihood estimation in the retrieval system. The second method retrieves the renormalized phase function as a difference from a known phase function. The effect of noise is more predictable for both methods than the conventional “best-fit” method, which selects the best-fitting shape and surface roughness from a predetermined particle set. The first method is applied to the data from the Polarization and Directionality of the Earth’s Reflectance (POLDER) sensor. The retrieval results indicate that long column shape (ratio of basal face diameter to prism height greater than 9) with surface roughness parameter between 0.1 and 0.5 represents the extratropical observations well. Weak temperature dependence of the surface roughness is found in the extratropical data stratified by the cloud top temperature. The tropical retrieval was not successful, and the second method is applied to the Multi-angle Imaging Spectroradiometer (MISR) data. Short hexagonal column particles or their aggregates are found to match with estimated renormalized phase function. In addition to these results, the surface roughness simulation is summarized and the derivation of the δ-fit truncation technique for polarimetric radiative transfer is included