Medical Image Recognition, Segmentation and Parsing: Machine Learning and Multiple Object ApproachesThis book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of- the-art approaches based on machine learning, for recognizing or detecting, parsing or segmenting, a cohort of anatomical structures from a medical image. Written by top experts in Medical Imaging, this book is ideal for university researchers and industry practitioners in medical imaging who want a complete reference on key methods, algorithms and applications in medical image recognition, segmentation and parsing of multiple objects. Learn:
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Medical Image Recognition, Segmentation and Parsing: Machine Learning and ... S. Kevin Zhou No preview available - 2015 |
Common terms and phrases
accuracy active contour active shape model AdaBoost airway algorithm anatomical structures annotated aortic approach atlas atlases autoencoder automated automatic birth event boundary brain candidate cardiac cell centerline classifier colon Comaniciu computed tomography Computer Vision context contour dataset deformable model denotes detector distance efficient error estimation evaluation example feature learning framework FreeSurfer function global gradient graph IEEE IEEE Trans image registration image segmentation iteration label fusion landmark detection layer learning-based level set liver LOGISMOS lung machine learning manual measurement Medical Image Medical Image Computing mesh method MGDM MICCAI mitosis multiple NeuroImage node object detection optimal organ parameters parsing patch performance phase contrast microscopy polyp probabilistic problem random forest regions registration robust samples scans search space Section shape model shown in Figure sparse spatial Springer statistical surface target image tree valve vector vertebrae volume voxel weights Zheng Zhou