#### Filter Results:

- Full text PDF available (25)

#### Publication Year

2008

2017

- This year (5)
- Last 5 years (31)
- Last 10 years (46)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Data Set Used

#### Key Phrases

#### Organism

Learn More

Tracking multiple targets with uncertain target dynamics is a difficult problem, especially with nonlinear state and/or measurement equations. With multiple targets, representing the full posterior distribution over target states is not practical. The problem becomes even more complicated when the number of targets varies, in which case the dimensionality… (More)

- Ismail Ben Ayed, Hua-mei Chen, Kumaradevan Punithakumar, Ian G. Ross, Shuo Li
- 2010 IEEE Computer Society Conference on Computer…
- 2010

This study investigates an efficient algorithm for image segmentation with a global constraint based on the Bhattacharyya measure. The problem consists of finding a region consistent with an image distribution learned a priori. We derive an original upper bound of the Bhattacharyya measure by introducing an auxiliary labeling. From this upper bound, we… (More)

This study investigates novel object-interaction priors for graph cut image segmentation with application to intervertebral disc delineation in magnetic resonance (MR) lumbar spine images. The algorithm optimizes an original cost function which constrains the solution with learned prior knowledge about the geometric interactions between different objects in… (More)

- Kumaradevan Punithakumar, Jing Yuan, Ismail Ben Ayed, Shuo Li, Yuri Boykov
- SIAM J. Imaging Sciences
- 2012

This study investigates a convex relaxation approach to figure-ground separation with a global distribution matching prior evaluated by the Bhattacharyya measure. The problem amounts to finding a region that most closely matches a known model distribution. It has been previously addressed by curve evolution, which leads to suboptimal and computationally… (More)

- Mariam Afshin, Ismail Ben Ayed, +5 authors Shuo Li
- IEEE Trans. Med. Imaging
- 2014

Automating the detection and localization of segmental (regional) left ventricle (LV) abnormalities in magnetic resonance imaging (MRI) has recently sparked an impressive research effort, with promising performances and a breadth of techniques. However, despite such an effort, the problem is still acknowledged to be challenging, with much room for… (More)

- Cyrus M. S. Nambakhsh, Jing Yuan, +4 authors Ismail Ben Ayed
- Medical Image Analysis
- 2013

A fundamental step in the diagnosis of cardiovascular diseases, automatic left ventricle (LV) segmentation in cardiac magnetic resonance images (MRIs) is still acknowledged to be a difficult problem. Most of the existing algorithms require either extensive training or intensive user inputs. This study investigates fast detection of the left ventricle (LV)… (More)

- Ismail Ben Ayed, Hua-mei Chen, Kumaradevan Punithakumar, Ian G. Ross, Shuo Li
- Medical Image Analysis
- 2012

This study investigates fast detection of the left ventricle (LV) endo- and epicardium boundaries in a cardiac magnetic resonance (MR) sequence following the optimization of two original discrete cost functions, each containing global intensity and geometry constraints based on the Bhattacharyya similarity. The cost functions and the corresponding max-flow… (More)

- Amin Zia, Thia Kirubarajan, James P. Reilly, Derek Yee, Kumaradevan Punithakumar, Shahram Shirani
- IEEE Transactions on Signal Processing
- 2008

In most solutions to state estimation problems, e.g., target tracking, it is generally assumed that the state transition and measurement models are known a priori. However, there are situations where the model parameters or the model structure itself are not known a priori or are known only partially. In these scenarios, standard estimation algorithms like… (More)

We state vertebral body (VB) segmentation in MRI as a distribution-matching problem, and propose a convex-relaxation solution which is amenable to parallel computations. The proposed algorithm does not require a complex learning from a large manually-built training set, as is the case of the existing methods. From a very simple user input, which amounts to… (More)

- N. Nadarajah, T. Kirubarajan, T. Lang, M. Mcdonald, Kumaradevan Punithakumar
- IEEE Trans. Aerospace and Electronic Systems
- 2011