Vessel detection by mean shift based ray propagation

Tek, Hüseyin; Comanicìu, Dorin; Williams, Jim

doi:10.1109/mmbia.2001.991738

Cited by 32 publications

(25 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The mean-shift algorithms give a fast solution for object tracking in video sequences (e.g., vehicle tracking, closed loop video) [19], [20], but usually do not give object contours. Different methods can be combined such as mixing the mean-shift and active contour approaches [21]. In contrast to segmentation-based methods, this second group of methods does not require a between-frames object-pairing stage in the processing of temporal sequences.…”

Section: B Tracking By Segmentation and Model Trackingmentioning

confidence: 99%

Tracking of migrating cells under phase-contrast video microscopy with combined mean-shift processes

Debeir

Ham

Kiss

et al. 2005

IEEE Trans. Med. Imaging

261

179

View full text Add to dashboard Cite

Abstract-In this paper, we propose a combination of mean-shift-based tracking processes to establish migrating cell trajectories through in vitro phase-contrast video microscopy. After a recapitulation on how the mean-shift algorithm permits efficient object tracking we describe the proposed extension and apply it to the in vitro cell tracking problem. In this application, the cells are unmarked (i.e., no fluorescent probe is used) and are observed under classical phase-contrast microscopy. By introducing an adaptive combination of several kernels, we address several problems such as variations in size and shape of the tracked objects (e.g., those occurring in the case of cell membrane extensions), the presence of incomplete (or noncontrasted) object boundaries, partially overlapping objects and object splitting (in the case of cell divisions or mitoses). Comparing the tracking results automatically obtained to those generated manually by a human expert, we tested the stability of the different algorithm parameters and their effects on the tracking results. We also show how the method is resistant to a decrease in image resolution and accidental defocusing (which may occur during long experiments, e.g., dozens of hours). Finally, we applied our methodology on cancer cell tracking and showed that cytochalasin-D significantly inhibits cell motility.

show abstract

Section: B Tracking By Segmentation and Model Trackingmentioning

confidence: 99%

Tracking of migrating cells under phase-contrast video microscopy with combined mean-shift processes

Debeir

Ham

Kiss

et al. 2005

IEEE Trans. Med. Imaging

261

179

View full text Add to dashboard Cite

show abstract

“…Vessel enhancement filters based on eigenvalue analysis of the Hessian matrix have been proposed, e.g., by Sato et al [13] and Frangi et al [5]. Tek et al [14] presented an approach which focuses on the segmentation of vessel cross sections. A single click inside the vessel on a slice initiates mean shift-based ray propagation to detect the boundary of the vessel.…”

Section: Actionmentioning

confidence: 99%

Contextual picking of volumetric structures

Kohlmann

Brückner

Kanitsar

et al. 2009

2009 IEEE Pacific Visualization Symposium

View full text Add to dashboard Cite

This paper presents a novel method for the interactive identification of contextual interest points within volumetric data by picking on a direct volume rendered image. In clinical diagnostics the points of interest are often located in the center of anatomical structures. In order to derive the volumetric position which allows a convenient examination of the intended structure, the system automatically extracts contextual meta information from the DICOM (Digital Imaging and Communications in Medicine) images and the setup of the medical workstation. Along a viewing ray for a volumetric picking, the ray profile is analyzed for structures which are similar to predefined templates from a knowledge base. We demonstrate with our results that the obtained position in 3D can be utilized to highlight a structure in 2D slice views, to interactively calculate centerlines of tubular objects, or to place labels at contextually-defined volumetric positions.

show abstract

“…In order to get the final and correct anatomical epicardium surface, one has to detect the border within the image by robust image processing algorithms. For this purpose a combination of both, the Mahalanobis Distance [20] and the Mean-Shift technique is applied [22]. Furthermore, we introduce an expected grey value at the sample position which has been extracted from the shape-learning procedure which leads to the following weighted expression:…”

Section: Appendixmentioning

confidence: 99%