Segmentation and Extraction of MR Images using PCNN
Medical Image Analysis
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Image segmentation is described as partitioning an image into a finite number of semantically non-overlapping regions. In medical applications, it is a fundamental process in most systems that support medical diagnosis, surgical planning and treatments. Generally, this process is done manually by clinicians, which may be time-consuming and tedious. To alleviate the problem, a number of interactive segmentation methods have been proposed. Pulse Coupled Neural Networks (PCNN) is a self-organizing network that does not require training and the network was constructed by simulating the activities ...
Image segmentation is described as partitioning an image into a finite number of semantically non-overlapping regions. In medical applications, it is a fundamental process in most systems that support medical diagnosis, surgical planning and treatments. Generally, this process is done manually by clinicians, which may be time-consuming and tedious. To alleviate the problem, a number of interactive segmentation methods have been proposed. Pulse Coupled Neural Networks (PCNN) is a self-organizing network that does not require training and the network was constructed by simulating the activities of the mammal's visual cortex neurons. PCNN is unique from other techniques due to its synchronous pulsed output, adjustable threshold and controllable parameters. The visual cortex system of mammalians was the backbone for the development of PCNN. Cat's and guinea pig's visual cortex helped in developing some digital models. Research is going on image segmentation techniques in medical field using PCNN. This book described a comprehensive idea for the segmentation of MR Images based on Pulse Coupled Neural Networks
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