-
[show abstract]
[hide abstract]
ABSTRACT: We present an active contour framework for segmenting neuronal axons on 3D confocal microscopy data. Our work is motivated by the need to conduct high throughput experiments involving microfluidic devices and femtosecond lasers to study the genetic mechanisms behind nerve regeneration and repair. While most of the applications for active contours have focused on segmenting closed regions in 2D medical and natural images, there haven't been many applications that have focused on segmenting open-ended curvilinear structures in 2D or higher dimensions. The active contour framework we present here ties together a well known 2D active contour model [5] along with the physics of projection imaging geometry to yield a segmented axon in 3D. Qualitative results illustrate the promise of our approach for segmenting neruonal axons on 3D confocal microscopy data.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2012; 2012:4006-9.
-
[show abstract]
[hide abstract]
ABSTRACT: Quantitative measures of breast morphology can help a breast cancer survivor to understand outcomes of reconstructive surgeries. One bottleneck of quantifying breast morphology is that there are only a few reliable automation algorithms for detecting the breast contour. This study proposes a novel approach for detecting the breast contour, which is based on a parametric active contour model. In addition to employing the traditional parametric active contour model, the proposed approach enforces a mathematical shape constraint based on the catenary curve, which has been previously shown to capture the overall shape of the breast contour reliably [1]. The mathematical shape constraint regulates the evolution of the active contour and helps the contour evolve towards the breast, while minimizing the undesired effects of other structures such as, the nipple/areola and scars. The efficacy of the proposed approach was evaluated on anterior posterior photographs of women who underwent or were scheduled for breast reconstruction surgery including autologous tissue reconstruction. The proposed algorithm shows promising results for detecting the breast contour.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2012; 2012:4450-3.
-
IEEE Signal Process. Lett. 01/2012; 19:75-78.
-
[show abstract]
[hide abstract]
ABSTRACT: Accurate segmentation of magnetic resonance (MR) images of the brain to differentiate features such as soft tissue, tumor, edema and necrosis is critical for both diagnosis and treatment purposes. Region-based formulations of geometric active contour models are popular choices for segmentation of MR and other medical images. Most of the traditional region-based formulations model local region intensity by assuming a piecewise constant approximation. However, the piecewise constant approximation rarely holds true for medical images such as MR images due to the presence of noise and bias field, which invariably results in a poor segmentation of the image. To overcome this problem, we have developed a probabilistic region-based active contour model for automatic segmentation of MR images of the brain. In our approach, a mixture of Gaussian distributions is used to accurately model the arbitrarily shaped local region intensity distribution. Prior spatial information derived from probabilistic atlases is also integrated into the level set evolution framework for guiding the segmentation process. Our experiments with a series of publicly available brain MR images show that the proposed active contour model gives stable and accurate segmentation results when compared to the traditional region based formulations.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2011; 2011:2821-4.
-
Gautam S. Muralidhar MS,
Alan C. Bovik PhD,
Mehul P. Sampat PhD,
Gary J. Whitman MD,
MD Tamara Miner Haygood PhD,
Tanya W. Stephens MD,
Mia K. Markey PhD, Gautam S. Muralidhar,
Alan C. Bovik,
Mehul P. Sampat,
Gary J. Whitman,
Tamara Miner Haygood,
Tanya W. Stephens,
Mia K. Markey
[show abstract]
[hide abstract]
ABSTRACT: In this paper, we review the role played by breast magnetic resonance imaging in the detection and diagnosis of breast cancer. This is followed by a discussion of clinical decision support systems in medicine and their contributions in breast magnetic resonance imaging interpretation. We conclude by discussing the future of computer-aided diagnosis in breast magnetic resonance imaging. Mt Sinai J Med 78:280–290, 2011. © 2011 Mount Sinai School of Medicine
Mount Sinai Journal of Medicine A Journal of Translational and Personalized Medicine 02/2011; 78(2):280 - 290. · 2.00 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: We have developed a novel, model-based active contour algorithm, termed "snakules", for the annotation of spicules on mammography. At each suspect spiculated mass location that has been identified by either a radiologist or a computer-aided detection (CADe) algorithm, we deploy snakules that are converging open-ended active contours also known as snakes. The set of convergent snakules have the ability to deform, grow and adapt to the true spicules in the image, by an attractive process of curve evolution and motion that optimizes the local matching energy. Starting from a natural set of automatically detected candidate points, snakules are deployed in the region around a suspect spiculated mass location. Statistics of prior physical measurements of spiculated masses on mammography are used in the process of detecting the set of candidate points. Observer studies with experienced radiologists to evaluate the performance of snakules demonstrate the potential of the algorithm as an image analysis technique to improve the specificity of CADe algorithms and as a CADe prompting tool.
IEEE transactions on medical imaging. 10/2010; 29(10):1768-80.
-
[show abstract]
[hide abstract]
ABSTRACT: In this paper, we describe a novel approach for the automatic classification of candidate spiculated mass locations on mammography. Our approach is based on “Snakules” — an evidence-based active contour algorithm that we have recently developed for the annotation of spicules on mammography. We use snakules to extract features characteristic of spicules and spiculated masses, and use these features to classify whether a region of a mammogram contains a spiculated mass or not. The results from our initial classification experiment demonstrate the strong potential of snakules as an image analysis technique to extract features specific to spicules and spiculated masses, which can subsequently be used to distinguish true spiculated mass locations from non-lesion locations on a mammogram and improve the specificity of computer-aided detection (CADe) algorithms.
Image Analysis & Interpretation (SSIAI), 2010 IEEE Southwest Symposium on; 06/2010
-
Proceedings of the International Conference on Image Processing, ICIP 2010, September 26-29, Hong Kong, China; 01/2010
-
IEEE Trans. Med. Imaging. 01/2010; 29:1768-1780.
-
J. Digital Imaging. 01/2010; 23:701-705.
-
[show abstract]
[hide abstract]
ABSTRACT: Automated analysis of fluorescence microscopy images of endothelial cells labeled for actin is important for quantifying changes in the actin cytoskeleton. The current manual approach is laborious and inefficient. The goal of our work is to develop automated image analysis methods, thereby increasing cell analysis throughput. In this study, we present preliminary results on comparing different algorithms for cell segmentation and image denoising.
AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium 02/2008;
-
[show abstract]
[hide abstract]
ABSTRACT: The use of computer-aided detection (CAD) systems in mammography has been the subject of intense research for many years. These systems have been developed with the aim of helping radiologists to detect signs of breast cancer. However, the effectiveness of CAD systems in practice has sparked recent debate. In this commentary, we argue that computer-aided detection will become an increasingly important tool for radiologists in the early detection of breast cancer, but there are some important issues that need to be given greater focus in designing CAD systems if they are to reach their full potential.
Breast cancer 01/2008; 2:5-9.
