Medical Image Segmentation


Handbook of Biomedical Image Analysis: Segmentation Models

Handbook of Biomedical Image Analysis: Segmentation Models
Handbook of Biomedical Image Analysis: Segmentation Models
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Empirical Evaluation Methods in Computer Vision by Henrik I.Christensen,

Empirical Evaluation Methods in Computer Vision by Henrik I.Christensen,
This book provides comprehensive coverage of methods for the empirical evaluation of computer vision techniques. The practical use of computer vision requires empirical evaluation to ensure that the overall system has a guaranteed performance. The book contains articles that cover the design of experiments for evaluation, range image segmentation, the evaluation of face recognition medical image segmentation and diffusion methods, image matching using correlation methods, medical image segmentation and the performance of medical image processing algorithms.
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Segmentation (image processing) - In image analysis, segmentation is the partition of a digital image into multiple regions (sets of pixels), according to some criterion.

Image segment - In computer vision segmentation of an image is the division of a given (digital) image into contiguous regions. In current computer vision algorithms the similarity of image parts is usually defined in terms of color and texture.

X-ray image intensifier - An X-ray image intensifier (XRII) refers to a special image intensifier device used in medical imaging involving x rays. It consists of an input window, input phosphor, photocathode, vacuum and electron optics, output phosphor and output window.

Thresholding (image processing) - Thresholding is the simplest method of image segmentation. Individual pixels in a grayscale image are marked as 'object' pixels if their value is greater than some threshold value (assuming an object to be brighter than the background) and as 'background' pixels otherwise.

medicalimagesegmentation

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