Magnetic Resonance Image analysis through soft-computing
Visualization Segmentation and diagnostic tool for Magnetic Resonance images
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Magnetic Resonance (MR) images are acquired using a non-invasive technique called NMR and extensively used for the diagnosis of the human body. Today MR Imaging is widely used in defining the soft tissue anatomy and characterization. One of the major challenges in MR image analysis is that the diseased tissue is dynamic and changes with time, therapy or due to flawed MR system. Since the tissue boundaries in images vary due to many reasons, using soft Computing techniques is a better approach for exploring into the diagnosis problem. This book therefore, provides MR image analysis and interpre...
Magnetic Resonance (MR) images are acquired using a non-invasive technique called NMR and extensively used for the diagnosis of the human body. Today MR Imaging is widely used in defining the soft tissue anatomy and characterization. One of the major challenges in MR image analysis is that the diseased tissue is dynamic and changes with time, therapy or due to flawed MR system. Since the tissue boundaries in images vary due to many reasons, using soft Computing techniques is a better approach for exploring into the diagnosis problem. This book therefore, provides MR image analysis and interpretation schemes using a combination of neural networks,fuzzy rules and genetic algorithms. Specifically, application of soft-computing are considered for the development of a visualization tool, MR image segmentation scheme and a tool for diagnosis. The main objective of these tools is to improve the diagnostic value of the MR images. These tools are should help the Medical Image Processing professionals, clinician, radiologist and the oncologist to evaluate diseased condition and also use the same to monitor effects due to any interventional therapy.
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