What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
His primary scientific interests are in Artificial intelligence, Computer vision, Pattern recognition, Image processing and Algorithm.
His studies deal with areas such as Markov chain and Affine transformation as well as Artificial intelligence.
His work carried out in the field of Computer vision brings together such families of science as Function, Linear model and Interpolation.
His studies in Pattern recognition integrate themes in fields like Image, Image formation and Brain mapping.
His research investigates the connection with Image processing and areas like Markov random field which intersect with concerns in Stochastic optimization, Image compression and Computational complexity theory.
Michael Brady incorporates Robustness and Process in his research.
His most cited work include:
- IMPROVED OPTIMIZATION FOR THE ROBUST AND ACCURATE LINEAR REGISTRATION AND MOTION CORRECTION OF BRAIN IMAGES (7328 citations)
- Saliency, Scale and Image Description (1106 citations)
- MRI-Based Attenuation Correction for PET/MRI: A Novel Approach Combining Pattern Recognition and Atlas Registration (412 citations)
What are the main themes of his work throughout his whole career to date?
Michael Brady focuses on Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Algorithm.
His study in Image segmentation, Image processing, Image registration, Feature and Image falls under the purview of Artificial intelligence.
His Image segmentation research is mostly focused on the topic Scale-space segmentation.
Many of his studies on Computer vision involve topics that are commonly interrelated, such as Pattern recognition.
His Pattern recognition study combines topics from a wide range of disciplines, such as Salient, Breast cancer and Ground truth.
The various areas that Michael Brady examines in his Segmentation study include Contrast and Magnetic resonance imaging, Mr images.
He most often published in these fields:
- Artificial intelligence (51.69%)
- Computer vision (37.08%)
- Pattern recognition (18.73%)
What were the highlights of his more recent work (between 2010-2021)?
- Artificial intelligence (51.69%)
- Computer vision (37.08%)
- Radiology (5.62%)
In recent papers he was focusing on the following fields of study:
Michael Brady mostly deals with Artificial intelligence, Computer vision, Radiology, Image registration and Algorithm.
His research combines Pattern recognition and Artificial intelligence.
Michael Brady has researched Computer vision in several fields, including Mixture model, Differential geometry and Joint entropy.
His study in Radiology is interdisciplinary in nature, drawing from both Blood sampling and Cohort.
His research in Image registration intersects with topics in Landmark, Matching, Computed tomography, Biomedical engineering and Pattern recognition.
His Algorithm research incorporates elements of Image processing, Lymph node, Ideal and Lymph.
Between 2010 and 2021, his most popular works were:
- Imaging biomarker roadmap for cancer studies. (407 citations)
- MRF-Based Deformable Registration and Ventilation Estimation of Lung CT (147 citations)
- Bayesian Networks for Clinical Decision Support in Lung Cancer Care (81 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
Michael Brady spends much of his time researching Artificial intelligence, Computer vision, Image registration, Algorithm and Image processing.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Bioinformatics.
The study incorporates disciplines such as Computational complexity theory and Pattern recognition in addition to Computer vision.
His Image registration research includes elements of Molecular imaging, Positron emission tomography, Attenuation, Correction for attenuation and Motion correction.
In his research on the topic of Algorithm, Robustness, Steatosis, Noise and Imaging phantom is strongly related with Ideal.
His Image processing research is multidisciplinary, incorporating elements of Markov random field, Pixel, Scale-space segmentation and Microscope.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
