H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 68 Citations 29,941 325 World Ranking 922 National Ranking 553

Research.com Recognitions

Awards & Achievements

2006 - Fellow of the Indian National Academy of Engineering (INAE)

2002 - IEEE Fellow For contributions to medical image analysis and computer vision.

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Internal medicine
  • Computer vision

Milan Sonka mainly investigates Artificial intelligence, Segmentation, Image segmentation, Computer vision and Image processing. His Artificial intelligence research integrates issues from Tree, Ultrasound and Pattern recognition. The concepts of his Segmentation study are interwoven with issues in Voxel, Magnetic resonance imaging, Radiology and Airway.

He interconnects Retina, Graph, Edge detection and Active appearance model in the investigation of issues within Image segmentation. His Computer vision research incorporates elements of Matching, Biplane angiography, Retinal and Medical imaging. His research on Image processing also deals with topics like

  • Region growing most often made with reference to Lung volumes,
  • Segmentation-based object categorization together with Binary image, Digital image processing, Feature detection, Automatic image annotation and Directed graph.

His most cited work include:

  • Image Processing: Analysis and Machine Vision (3335 citations)
  • 3D Slicer as an image computing platform for the Quantitative Imaging Network. (2428 citations)
  • Retinal Imaging and Image Analysis (752 citations)

What are the main themes of his work throughout his whole career to date?

Milan Sonka mainly focuses on Artificial intelligence, Segmentation, Computer vision, Image segmentation and Pattern recognition. While the research belongs to areas of Artificial intelligence, he spends his time largely on the problem of Graph, intersecting his research to questions surrounding Algorithm. Milan Sonka has included themes like Voxel, Optical coherence tomography, Radiology, Pixel and Biomedical engineering in his Segmentation study.

His study on Optical coherence tomography also encompasses disciplines like

  • Retinal that connect with fields like Retina,
  • Glaucoma which is related to area like Optic nerve. He usually deals with Radiology and limits it to topics linked to Coronary arteries and Lumen. His Computer vision study integrates concerns from other disciplines, such as Principal component analysis and Medical imaging.

He most often published in these fields:

  • Artificial intelligence (47.68%)
  • Segmentation (38.94%)
  • Computer vision (37.05%)

What were the highlights of his more recent work (between 2014-2022)?

  • Segmentation (38.94%)
  • Artificial intelligence (47.68%)
  • Optical coherence tomography (16.12%)

In recent papers he was focusing on the following fields of study:

His primary scientific interests are in Segmentation, Artificial intelligence, Optical coherence tomography, Computer vision and Pattern recognition. His study on Image segmentation is often connected to Knee mri as part of broader study in Segmentation. His Artificial intelligence research includes elements of Algorithm and Machine learning, Receiver operating characteristic.

The concepts of his Optical coherence tomography study are interwoven with issues in Cardiac allograft vasculopathy, Biomedical engineering and Confidence interval. Many of his studies on Computer vision involve topics that are commonly interrelated, such as Intravascular ultrasound. His research integrates issues of Magnetic resonance imaging, Edge detection, Kernel and Calf muscle in his study of Pattern recognition.

Between 2014 and 2022, his most popular works were:

  • Retinal neurodegeneration may precede microvascular changes characteristic of diabetic retinopathy in diabetes mellitus. (218 citations)
  • Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning. (113 citations)
  • Automated 3-D Retinal Layer Segmentation of Macular Optical Coherence Tomography Images With Serous Pigment Epithelial Detachments (91 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Internal medicine
  • Computer vision

Artificial intelligence, Segmentation, Pattern recognition, Optical coherence tomography and Computer vision are his primary areas of study. His biological study spans a wide range of topics, including Algorithm and Graph. His specific area of interest is Segmentation, where Milan Sonka studies Image segmentation.

His Image segmentation research is multidisciplinary, relying on both Radiology and Scintigraphy. His study in Optical coherence tomography is interdisciplinary in nature, drawing from both Lung transplantation, Biomedical engineering and Confidence interval. His Computer vision research includes themes of Abdominal ct and Kidney.

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.

Top Publications

Image Processing: Analysis and Machine Vision

Milan Sonka;Vaclav Hlavac;Roger Boyle.
(1993)

8872 Citations

3D Slicer as an image computing platform for the Quantitative Imaging Network.

Andriy Fedorov;Reinhard Beichel;Jayashree Kalpathy-Cramer;Julien Finet.
Magnetic Resonance Imaging (2012)

2584 Citations

Retinal Imaging and Image Analysis

M D Abràmoff;M K Garvin;M Sonka.
IEEE Reviews in Biomedical Engineering (2010)

1038 Citations

Optimal Surface Segmentation in Volumetric Images-A Graph-Theoretic Approach

Kang Li;Xiaodong Wu;D.Z. Chen;M. Sonka.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2006)

741 Citations

Interstitial lung disease: A quantitative study using the adaptive multiple feature method.

Renuka Uppaluri;Eric A. Hoffman;Milan Sonka;Gary W. Hunninghake.
American Journal of Respiratory and Critical Care Medicine (1999)

698 Citations

Automated 3-D Intraretinal Layer Segmentation of Macular Spectral-Domain Optical Coherence Tomography Images

M.K. Garvin;M.D. Abramoff;Xiaodong Wu;S.R. Russell.
IEEE Transactions on Medical Imaging (2009)

559 Citations

3-D active appearance models: segmentation of cardiac MR and ultrasound images

S.C. Mitchell;J.G. Bosch;B.P.F. Lelieveldt;R.J. van der Geest.
IEEE Transactions on Medical Imaging (2002)

523 Citations

Image processing analysis and machine vision [2nd ed.]

Milan Sonka;Václav Hlaváč;Roger Boyle.
(1999)

492 Citations

Effect of endothelial shear stress on the progression of coronary artery disease, vascular remodeling, and in-stent restenosis in humans: in vivo 6-month follow-up study.

Peter H. Stone;Ahmet U. Coskun;Scott Kinlay;Maureen E. Clark.
Circulation (2003)

463 Citations

Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac MR images

S.C. Mitchell;B.P.F. Lelieveldt;R.J. van der Geest;H.G. Bosch.
IEEE Transactions on Medical Imaging (2001)

441 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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