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This book covers the state-of-the-art techniques of machine learning and their applications in the medical field. It presents several Computer-aided diagnosis (CAD) systems, which have played an important role in the diagnosis of several diseases in the past decade e.g., cancer detection, resulting in the development of several successful systems.

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This work represents recent advancement in cloud-based medical imaging. It looks at the integration of AI, Cloud Computing, and medical imaging. Special focus is given to the intricacies of designing cloud-based imaging systems. The book also examines issues surrounding big data in medical imaging applications.

This book provides a brief introduction to medical imaging, stochastic modeling, and model-guided image analysis. It presents the latest state of the art in stochastic modeling for medical image analysis while incorporating fully tested experimental results throughout. This valuable resource details efficient stochastic modeling techniques, incl

There is an urgent need to develop and integrate new statistical, mathematical, visualization, and computational models with the ability to analyze Big Data in order to retrieve useful information to aid clinicians in accurately diagnosing and treating patients.

This book covers the state-of-the-art techniques of machine learning and their applications in the medical field. It presents several Computer-aided diagnosis (CAD) systems, which have played an important role in the diagnosis of several diseases in the past decade e.g., cancer detection, resulting in the development of several successful systems.

As one of the most important tasks in biomedical imaging, image segmentation provides the foundation for quantitative reasoning and diagnostic techniques. A large variety of different imaging techniques, each with its own physical principle and characteristics (e.g., noise modeling), often requires modality-specific algorithmic treatment. In rec

There is an urgent need to develop and integrate new statistical, mathematical, visualization, and computational models with the ability to analyze Big Data in order to retrieve useful information to aid clinicians in accurately diagnosing and treating patients"--

Level set methods are numerical techniques which offer remarkably powerful tools for understanding, analyzing, and computing interface motion in a host of settings. When used for medical imaging analysis and segmentation, the function assigns a label to each pixel or voxel and optimality is defined based on desired imaging properties. This often includes a detection step to extract specific objects via segmentation. This allows for the segment...

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This will be a comprehensive overview of the latest research, technologies and innovations in minimally invasive imaging of the prostate gland and prostate cancer imaging. A major focus will be 3D image segmentation along with computer-aided-diagnosis techniques for prostate cancer.

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With the advances in image guided surgery for cancer treatment, the role of image segmentation and registration has become very critical. The central engine of any image guided surgery product is its ability to quantify the organ or segment the organ whether it is a magnetic resonance imaging (MRI) and computed tomography (CT), X-ray, PET, SPECT, Ultrasound, and Molecular imaging modality. Sophisticated segmentation algorithms can help the phy...

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