Image processing techniques that are used to process images of the human body (or parts) for clinical purposes or medical science. Medical Imaging incorporates Radiology, Magnetic Resonance Imaging, Nuclear medicine, medical Ultrasonography or Ultrasound, Endoscopy, Elastography, Tactile Imaging, Thermography and medical photography.

Image Segmentation is the process of partitioning a digital image into multiple segments (objects). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze.

Image Denoising (image noise reduction) is the process of removing additive or multiplicative noise from digital images.

Image Compression is to reduce irrelevance and redundancy of the image data in order to be able to store or transmit data in an efficient form. Image Compression could be lossy or lossless compression.

Image Recognition is the process of identifying and detecting an object or a feature in a digital image or video.

By using the National Library of Medicine Insight Segmentation and Registration Toolkit (ITK), ITK is an open-source, cross-platform system. A project has been accomplished to register and segment a 3D brain image by using ITK. Some function from the 3D Slicer software tool have been used in this project.

In this project, graph based image segmentation (Graph-cut algorithm) has been used for segmentaing objects from stereo images.

Patch-based image denoising techniques have been implmented for the studying the efficiency of patch-based image desnoing; including, BM3D, K-SVD, NL-Means and others.

Different lossless image compression algorithms have been implemented and developed. Thees algorithms include LZ family, PPM, BWT.

© 2009 - 2016 Monagi Alkinani

 Department of Computer Science
 Middlesex College, Western University,
 London, Ontario, Canada, N6A 5B7

 malkinan@uwo.ca

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