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"Advances in Computational Techniques for Biomedical Image Analysis: Methods and Applications focuses on post-acquisition challenges such as image enhancement, detection of edges and objects, analysis of shape, quantification of texture and sharpness, and pattern analysis. It discusses the archiving and transfer of images, presents a selection of techniques for the enhancement of contrast and edges, for noise reduction and for edge-preserving smoothing. It examines various feature detection and segmentation techniques, together with methods for computing a registration or normalization transformation"--

Image analysis. --- Analysis of images --- Image interpretation --- Photographs --- Forensic sciences --- Imaging systems --- Inspection

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The interpretation of aerial and satellite imagery requires significant experience and expert knowledge and therefore is mainly performed by professional image interpreters. So far, automatic methods are not able to provide comparable results but they can be used to support the manual image interpretation process. This work shows how the benefits of manual and automatic image interpretation can be adequately combined in an interactive image interpretation system.

Luftbildauswertung --- Fernerkundung --- Szenenanalyse --- Unterstützungssysteme --- Bildverstehenremote sensing --- image understanding --- decision support systems --- image interpretation --- scene analysis

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"Given the rapid evolution of radiological imaging in the past four decades, medical image processing nowadays is an essential tool for clinical research. Applications range from research in neuroscience, biomechanics and biomedical engineering to clinical routine tasks such as the visualization of huge datasets provided by modern computed tomography systems in radiology, the manufacturing of patient-specific prostheses for orthopedic surgery, the precise planning of dose distributions in radiation oncology, the fusion of multimodal image data for therapymonitoring in internal medicine, and computer-aided neurosurgical interventions"--Provided by publisher.

Diagnostic Imaging --- Image Processing, Computer-Assisted --- Image Interpretation, Computer-Assisted --- methods --- Diagnostic imaging

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Artificial Intelligence --- Machine Learning --- Deep Learning --- Diagnostic Imaging --- Image Processing, Computer-Assisted --- Image Interpretation, Computer-Assisted.

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Brain Tumor MRI Image Segmentation Using Deep Learning Techniques offers a description of deep learning approaches used for the segmentation of brain tumors. The book demonstrates core concepts of deep learning algorithms by using diagrams, data tables and examples to illustrate brain tumor segmentation. After introducing basic concepts of deep learning-based brain tumor segmentation, sections cover techniques for modeling, segmentation and properties. A focus is placed on the application of different types of convolutional neural networks, like single path, multi path, fully convolutional network, cascade convolutional neural networks, Long Short-Term Memory - Recurrent Neural Network and Gated Recurrent Units, and more. The book also highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in brain tumor segmentation.

Brain --- Tumors --- Diagnosis. --- Magnetic resonance imaging. --- Brain Neoplasms --- Magnetic Resonance Imaging --- Image Interpretation, Computer-Assisted --- Image Processing, Computer-Assisted --- Deep Learning --- diagnostic imaging --- Magnetic Resonance Imaging. --- Image Interpretation, Computer-Assisted. --- Image Processing, Computer-Assisted. --- Deep Learning. --- diagnostic imaging.