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2001, Biomedical engineering series, ISBN 9780849300646, 382 p., [8] p. of plates
Book
Medical physics (Lancaster), ISSN 0094-2405, 09/2015, Volume 42, Issue 9, pp. 5301 - 5309
Purpose: Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body... 
biomedical MRI | Medical image quality | Biomedical modeling | regression analysis | Scintigraphy | Tissues | Digital computing or data processing equipment or methods, specially adapted for specific applications | Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging | Clinical applications | brain [18F]FDG PET prediction | medical image processing | [18F]FDG | Positron emission tomography (PET) | Medical image segmentation | biological tissues | Biological material, e.g. blood, urine; Haemocytometers | brain | medical disorders | regression forest | Measuring half‐life of a radioactive substance | Magnetic resonance imaging | Image data processing or generation, in general | Medical image reconstruction | General statistical methods | positron emission tomography | positron emission tomography (PET) | [ 18 F]FDG | brain [ 18 F]FDG PET prediction | FDG-PET | PROSTATE | RANDOM FORESTS | AUTO-CONTEXT | [F-18]FDG | ATTENUATION CORRECTION | CLASSIFICATION | FEATURE-SELECTION | DISEASE | SEGMENTATION | FRAMEWORK | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | brain [F-18]FDG PET prediction | Magnetic Resonance Imaging | Brain - diagnostic imaging | Humans | Fluorodeoxyglucose F18 | Positron-Emission Tomography - methods | Radiation Dosage | Imaging, Three-Dimensional | DIAGNOSIS | BIOMEDICAL RADIOGRAPHY | RADIATION PROTECTION AND DOSIMETRY | CEREBROSPINAL FLUID | FLUORODEOXYGLUCOSE | ANIMAL TISSUES | RADIATION DOSES | ALGORITHMS | 60 APPLIED LIFE SCIENCES | IMAGE PROCESSING | NMR IMAGING | IMAGES | POSITRON COMPUTED TOMOGRAPHY | ITERATIVE METHODS | BRAIN | FLUORINE 18 | Nuclear Medicine Physics
Journal Article
Magnetic resonance in medicine, ISSN 1522-2594, 2008, Volume 60, Issue 3, pp. 674 - 682
Journal Article
SAE technical paper series, Volume 2009-01-2261.
This study outlines a protocol for image data collection acquired from human volunteers... 
Human factors | Magnetic resonance imaging (MRI) | Finite element analysis | Injuries
eJournal
Medical physics (Lancaster), ISSN 2473-4209, 2015, Volume 42, Issue 11, pp. 6520 - 6528
Purpose: To identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response to neoadjuvant chemotherapy. Methods... 
biomedical MRI | image classification | Segmentation | image fusion | sensitivity analysis | Medical image contrast | Digital computing or data processing equipment or methods, specially adapted for specific applications | image enhancement | Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging | feature extraction | image segmentation | Clinical applications | quantitative image feature analysis | data acquisition | tumor response to neoadjuvant chemotherapy | tumours | medical image processing | dynamic contrast‐enhanced breast magnetic resonance imaging | assessment of breast cancer prognosis | Image enhancement or restoration, e.g. from bit‐mapped to bit‐mapped creating a similar image | Data acquisition and logging | Medical image segmentation | Biological material, e.g. blood, urine; Haemocytometers | Testing procedures | Chemotherapy | Image analysis | Image data processing or generation, in general | Machine learning | Edge enhancement | cancer | Medical magnetic resonance imaging | neural nets | dynamic contrast-enhanced breast magnetic resonance imaging | MAMMOGRAPHY | DIAGNOSIS | CONTRAST-ENHANCED MRI | SOLID TUMORS | CANCER-SOCIETY GUIDELINES | VARIANCE | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | SCHEMES | Reproducibility of Results | Humans | Image Interpretation, Computer-Assisted - methods | Middle Aged | Magnetic Resonance Imaging - methods | Treatment Outcome | Antineoplastic Agents - administration & dosage | Drug Monitoring - methods | Breast Neoplasms - drug therapy | Machine Learning | Algorithms | Breast Neoplasms - pathology | Sensitivity and Specificity | Female | Image Enhancement - methods | Pattern Recognition, Automated - methods | Neural Networks (Computer) | NEURAL NETWORKS | MAMMARY GLANDS | NEOPLASMS | IMAGES | 60 APPLIED LIFE SCIENCES | DATASETS | CHEMOTHERAPY | Magnetic Resonance Physics
Journal Article
2002, ISBN 9780471237662, 313
eBook
IEEE transactions on medical imaging, ISSN 1558-254X, 2015, Volume 34, Issue 10, pp. 1993 - 2024
Journal Article
IEEE transactions on medical imaging, ISSN 1558-254X, 2018, Volume 37, Issue 2, pp. 384 - 395
Journal Article
IEEE transactions on medical imaging, ISSN 0278-0062, 2008, Volume 27, Issue 4, pp. 425 - 441
Journal Article
Journal Article
Pattern recognition letters, ISSN 0167-8655, 2017, Volume 94, pp. 87 - 95
....•A comparative study between level set and active contour segmentation is presented. Image processing plays an important role in various medical applications to support the computerized disease examination... 
DESIGN | ALGORITHMS | MODEL | ACTIVE CONTOURS | TSALLIS ENTROPY | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Brain | Study and teaching | Magnetic resonance imaging | Gliomas | Brain tumors
Journal Article