Mr. Amer Abufadel Profile

Amer Abufadel

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Proceedings Article | 27 January 2010 Paper

A regions of confidence based approach to enhance segmentation with shape priors

Vikram Appia, Balaji Ganapathy, Amer Abufadel, Anthony Yezzi, Tracy Faber

Proceedings Volume 7533, 753302 (2010) https://doi.org/10.1117/12.850888

KEYWORDS: Image segmentation, Data modeling, Principal component analysis, Optical alignment, Shape analysis, Binary data, Cardiovascular magnetic resonance imaging, Ruthenium, Image quality, Image processing algorithms and systems

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We propose an improved region based segmentation model with shape priors that uses labels of confidence/interest to exclude the influence of certain regions in the image that may not provide useful information for segmentation. These could be regions in the image which are expected to have weak, missing or corrupt edges or they could be regions in the image which the user is not interested in segmenting, but are part of the object being segmented. In the training datasets, along with the manual segmentations we also generate an auxiliary map indicating these regions of low confidence/interest. Since, all the training images are acquired under similar conditions, we can train our algorithm to estimate these regions as well. Based on this training we will generate a map which indicates the regions in the image that are likely to contain no useful information for segmentation. We then use a parametric model to represent the segmenting curve as a combination of shape priors obtained by representing the training data as a collection of signed distance functions. We evolve an objective energy functional to evolve the global parameters that are used to represent the curve. We vary the influence each pixel has on the evolution of these parameters based on the confidence/interest label. When we use these labels to indicate the regions with low confidence; the regions containing accurate edges will have a dominant role in the evolution of the curve and the segmentation in the low confidence regions will be approximated based on the training data. Since our model evolves global parameters, it improves the segmentation even in the regions with accurate edges. This is because we eliminate the influence of the low confidence regions which may mislead the final segmentation. Similarly when we use the labels to indicate the regions which are not of importance, we will get a better segmentation of the object in the regions we are interested in.

Proceedings Article | 28 February 2007 Paper

4D segmentation of cardiac data using active surfaces with spatiotemporal shape priors

Amer Abufadel, Tony Yezzi, Ronald Schafer

Proceedings Volume 6498, 64980D (2007) https://doi.org/10.1117/12.715098

KEYWORDS: Image segmentation, Principal component analysis, Heart, Binary data, 3D metrology, Data modeling, 3D modeling, Image resolution, Data acquisition, Shape analysis

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We present a 4D spatiotemporal segmentation algorithm based on the Mumford-Shah functional coupled with shape priors. When used in a clinical setting, our algorithm could greatly alleviate the time that clinicians must spend working with the acquired data to manually retrieve diagnostically meaningful measurements. The advantage of the 4D algorithm is that segmentation occurs in both space and time simultaneously, improving accuracy and robustness over existing 2D and 3D methods. The segmentation contour or hyper-surface is a zero level set function in 4D space that exploits the coherence within continuous regions not only between spatial slices, but between consecutive time samples as well. Shape priors are incorporated into the segmentation to limit the result to a known shape. Variations in shape are computed using principal component analysis (PCA), of a signed distance representation of the training data derived from manual segmentation of 18 carefully selected data sets. The automatic segmentation occurs by manipulating the parameters of this signed distance representation to minimize a predetermined energy functional. Several tests are presented to show the consistency and accuracy of the novel automatic 4D segmentation process.

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