What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Internal medicine
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Image segmentation, Segmentation and Image processing.
His Artificial intelligence study integrates concerns from other disciplines, such as Algorithm, Magnetic resonance imaging and Pattern recognition.
In his research on the topic of Computer vision, Adjacency list, Point, Theoretical computer science and User assistance is strongly related with Boundary.
The various areas that Jayaram K. Udupa examines in his Image segmentation study include Edge detection and Fuzzy logic.
Jayaram K. Udupa interconnects Fluid-attenuated inversion recovery, Brain tumor and Iterative method in the investigation of issues within Segmentation.
His work carried out in the field of Image processing brings together such families of science as Image quality, Scale, Image resolution, Histogram and Tomography.
His most cited work include:
- Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation (781 citations)
- New variants of a method of MRI scale standardization (571 citations)
- User-steered image segmentation paradigms: live wire and live lane (468 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Computer vision, Segmentation, Image segmentation and Pattern recognition are his primary areas of study.
The Image processing, Fuzzy logic, Object and Voxel research Jayaram K. Udupa does as part of his general Artificial intelligence study is frequently linked to other disciplines of science, such as Body region, therefore creating a link between diverse domains of science.
The study incorporates disciplines such as Visualization, Boundary, Magnetic resonance imaging and Scale in addition to Computer vision.
His study looks at the relationship between Magnetic resonance imaging and fields such as Nuclear medicine, as well as how they intersect with chemical problems.
His research integrates issues of Cognitive neuroscience of visual object recognition, Process, Anatomy and Medical imaging in his study of Segmentation.
In his study, Interpolation is inextricably linked to Algorithm, which falls within the broad field of Image segmentation.
He most often published in these fields:
- Artificial intelligence (55.88%)
- Computer vision (42.68%)
- Segmentation (31.96%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (55.88%)
- Segmentation (31.96%)
- Image segmentation (30.31%)
In recent papers he was focusing on the following fields of study:
Jayaram K. Udupa mostly deals with Artificial intelligence, Segmentation, Image segmentation, Pattern recognition and Body region.
Many of his studies on Artificial intelligence involve topics that are commonly interrelated, such as Computer vision.
His biological study spans a wide range of topics, including Boundary, Sagittal plane and Data set.
Jayaram K. Udupa focuses mostly in the field of Segmentation, narrowing it down to matters related to Ground truth and, in some cases, Interpolation.
His Image segmentation research incorporates themes from Image processing, Image registration, Magnetic resonance imaging and Modality.
His Pattern recognition research includes themes of PET-CT, Image, Metric and Medical imaging.
Between 2014 and 2021, his most popular works were:
- A hybrid method for airway segmentation and automated measurement of bronchial wall thickness on CT. (26 citations)
- Automatic anatomy recognition in whole-body PET/CT images. (18 citations)
- Lesion size measurement in femoral head necrosis. (17 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Internal medicine
- Radiology
His scientific interests lie mostly in Artificial intelligence, Segmentation, Image segmentation, Pattern recognition and Anatomy.
His Artificial intelligence study combines topics in areas such as Graph, Graph based, Sagittal plane and Computer vision.
His work on Radiographic Image Enhancement as part of general Computer vision study is frequently linked to Airway segmentation, bridging the gap between disciplines.
His work deals with themes such as Jaccard index, Airway, Metric and Medical imaging, which intersect with Segmentation.
His studies in Image segmentation integrate themes in fields like Cognitive neuroscience of visual object recognition, Image processing, Modality and Positron emission tomography, PET-CT.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Contextual image classification, Image, Reliability and Dice.
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