This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Features: highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing; discusses the insightful research experience of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.
Computer-aided diagnosis systems are currently at the heart of many clinical protocols. This book puts forward a hierarchical architecture for the design of a robust and efficient CAD tool for breast cancer detection. It focuses on the reduction of false alarms rate through the identification of image regions of foremost interest (potential cancerous areas). The dynamic range of the image is stretched to enhance the contrast between tissues and background and favors accurate breast region extraction. Then follow pectoral muscle segmentation since it regularly tampers breast tissue analysis. Extracting pectoral muscle tissues is both hard and challenging due to its overlap with dense tissues. To overcome this difficulty, a validation process followed by a refinement strategy is proposed to detect and correct the segmentation imperfections. In the last chapter dealing with breast density analysis, to address the inter-variability in gray levels distributions, an optimized gray level transport map is introduced for contrast standardization. With this technique, dense region areas computed using simple thresholding are highly correlated to density classes from an annotated dataset.
In the present era of information technology and rapid transmission of multimedia content, the internet is increasingly playing an important role in the offering of multimedia resources through digital networks. Digital media has disadvantage of being prone to easy illegal copying methods such as tampering, piracy, fraud and counterfeiting. Video watermarking is process of inserting watermarks in a video sequence in order to protect the video from illegal copying and identify manipulations. Due to inherent redundancies between frames video signals are commonly affected by frame dropping attacks. Since the main aspects of information hiding are capacity security and robustness. Digital watermarks are helpful in various applications like copyright management, content authentication, fingerprinting, tamper detection and broadcast monitoring. Many digital watermarking algorithms have been proposed in spatial and transform domains. Spatial domain techniques are very simple requires low computational complexities but still have relatively low-bit capacity and are not resistant enough to image processing operations.
This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches -which are based on optimization techniques - together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models. All major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methods. The latest trends: Sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling. Case studies - protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization and echo cancellation, show how the theory can be applied. MATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code.
This book provides comprehensive coverage of the field of outlier analysis from a computer science point of view. It integrates methods from data mining, machine learning, and statistics within the computational framework and therefore appeals to multiple communities. The chapters of this book can be organized into three categories: Basic algorithms: Chapters 1 through 7 discuss the fundamental algorithms for outlier analysis, including probabilistic and statistical methods, linear methods, proximity-based methods, high-dimensional (subspace) methods, ensemble methods, and supervised methods. Domain-specific methods: Chapters 8 through 12 discuss outlier detection algorithms for various domains of data, such as text, categorical data, time-series data, discrete sequence data, spatial data, and network data. Applications: Chapter 13 is devoted to various applications of outlier analysis. Some guidance is also provided for the practitioner. The second edition of this book is more detailed and is written to appeal to both researchers and practitioners. Significant new material has been added on topics such as kernel methods, one-class support-vector machines, matrix factorization, neural networks, outlier ensembles, time-series methods, and subspace methods. It is written as a textbook and can be used for classroom teaching.