D
Dorin Comaniciu
Researcher at Princeton University
Publications - 632
Citations - 43059
Dorin Comaniciu is an academic researcher from Princeton University. The author has contributed to research in topics: Segmentation & Object detection. The author has an hindex of 74, co-authored 622 publications receiving 40541 citations. Previous affiliations of Dorin Comaniciu include Siemens & Rutgers University.
Papers
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Patent
Method and system for guidewire tracking in fluoroscopic image sequences
TL;DR: In this article, a method and system for tracking a guidewire in a fluoroscopic image sequence is described, where the guidewires are detected in each frame of the image sequence by rigidly tracking from a previous frame to the current frame.
Book ChapterDOI
Image-based device tracking for the co-registration of angiography and intravascular ultrasound images
TL;DR: This is the first reported system able to automatically establish a robust correspondence between the angiography and IVUS images, thus providing clinicians with a comprehensive view of the coronaries.
Proceedings ArticleDOI
Multi-stage osteolytic spinal bone lesion detection from CT data with internal sensitivity control
Michael Wels,B. M. Kelm,Alexey Tsymbal,Matthias Hammon,Grzegorz Soza,Michael Sühling,Alexander Cavallaro,Dorin Comaniciu +7 more
TL;DR: This paper presents a method for fully-automatic osteolytic spinal bone lesion detection from 3D CT data using a multi-stage approach subsequently applying multiple discriminative models, i.e., multiple random forests, for lesion candidate detection and rejection to an input volume.
Journal ArticleDOI
A Probabilistic Model for Automatic Segmentation of the Esophagus in 3-D CT Scans
Johannes Feulner,Shaohua Kevin Zhou,Matthias Hammon,Sascha Seifert,Martin Huber,Dorin Comaniciu,Joachim Hornegger,Alexander Cavallaro +7 more
TL;DR: The method is compared to an alternative approach that uses a particle filter instead of a Markov chain to infer the approximate esophagus shape, to the performance of a human observer and also to state of the art methods, which are all semiautomatic.
Patent
Multi-layer aggregation for object detection
Hien M. Nguyen,Vivek Kumar Singh,Yefeng Zheng,Bogdan Georgescu,Dorin Comaniciu,Shaohua Kevin Zhou +5 more
TL;DR: In this article, a deep or multiple layer network (72-80) is used to learn features for detecting (58) the object in the image, and multiple features from different layers are aggregated to train a classifier for the object.