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- 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)

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

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

We consider linear systems whose state parameters are separable into linear and nonlinear sets, and evolve according to some known transition distribution, and whose measurement noise is distributed according to a mixture of Gaussians. In doing so, we propose a novel particle filter that addresses the optimal state estimation problem for the aforementioned… (More)

- K. PUNITHAKUMAR
- 2008

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, Kumaradevan Punithakumar, Shuo Li, Ali Islam, Jaron Chong
- MICCAI
- 2009

We present a discrete kernel density matching energy for segmenting the left ventricle cavity in cardiac magnetic resonance sequences. The energy and its graph cut optimization based on an original first-order approximation of the Bhattacharyya measure have not been proposed previously, and yield competitive results in nearly real-time. The algorithm seeks… (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)

- 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)

- 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)

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)