Ms. Shannon C. Agner Profile

Shannon C. Agner

at Rutgers State Univ of New Jersey

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Publications (3)

Proceedings Article | 21 March 2014 Paper

Spectral embedding-based registration (SERg) for multimodal fusion of prostate histology and MRI

Eileen Hwuang, Mirabela Rusu, Sudha Karthigeyan, Shannon Agner, Rachel Sparks, Natalie Shih, John Tomaszewski, Mark Rosen, Michael Feldman, Anant Madabhushi

Proceedings Volume 9034, 90343P (2014) https://doi.org/10.1117/12.2044317

KEYWORDS: Image registration, Magnetic resonance imaging, Prostate, Independent component analysis, Chromium, Feature extraction, Image restoration, Image segmentation, Brain, Neuroimaging

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Multi-modal image registration is needed to align medical images collected from different protocols or imaging sources, thereby allowing the mapping of complementary information between images. One challenge of multimodal image registration is that typical similarity measures rely on statistical correlations between image intensities to determine anatomical alignment. The use of alternate image representations could allow for mapping of intensities into a space or representation such that the multimodal images appear more similar, thus facilitating their co-registration. In this work, we present a spectral embedding based registration (SERg) method that uses non-linearly embedded representations obtained from independent components of statistical texture maps of the original images to facilitate multimodal image registration. Our methodology comprises the following main steps: 1) image-derived textural representation of the original images, 2) dimensionality reduction using independent component analysis (ICA), 3) spectral embedding to generate the alternate representations, and 4) image registration. The rationale behind our approach is that SERg yields embedded representations that can allow for very different looking images to appear more similar, thereby facilitating improved co-registration. Statistical texture features are derived from the image intensities and then reduced to a smaller set by using independent component analysis to remove redundant information. Spectral embedding generates a new representation by eigendecomposition from which only the most important eigenvectors are selected. This helps to accentuate areas of salience based on modality-invariant structural information and therefore better identifies corresponding regions in both the template and target images. The spirit behind SERg is that image registration driven by these areas of salience and correspondence should improve alignment accuracy. In this work, SERg is implemented using Demons to allow the algorithm to more effectively register multimodal images. SERg is also tested within the free-form deformation framework driven by mutual information. Nine pairs of synthetic T1-weighted to T2-weighted brain MRI were registered under the following conditions: five levels of noise (0%, 1%, 3%, 5%, and 7%) and two levels of bias field (20% and 40%) each with and without noise. We demonstrate that across all of these conditions, SERg yields a mean squared error that is 81.51% lower than that of Demons driven by MRI intensity alone. We also spatially align twenty-six ex vivo histology sections and in vivo prostate MRI in order to map the spatial extent of prostate cancer onto corresponding radiologic imaging. SERg performs better than intensity registration by decreasing the root mean squared distance of annotated landmarks in the prostate gland via both Demons algorithm and mutual information-driven free-form deformation. In both synthetic and clinical experiments, the observed improvement in alignment of the template and target images suggest the utility of parametric eigenvector representations and hence SERg for multimodal image registration.

Proceedings Article | 16 March 2011 Paper

Spectral embedding based active contour (SEAC): application to breast lesion segmentation on DCE-MRI

Shannon Agner, Jun Xu, Mark Rosen, Sudha Karthigeyan, Sarah Englander, Anant Madabhushi

Proceedings Volume 7963, 796305 (2011) https://doi.org/10.1117/12.878218

KEYWORDS: Image segmentation, Breast, Magnetic resonance imaging, Feature extraction, Medical imaging, Image filtering, Data modeling, Yield improvement, Tumor growth modeling, Fuzzy logic

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Proceedings Article | 17 March 2008 Paper

Novel kinetic texture features for breast lesion classification on dynamic contrast enhanced (DCE) MRI

Shannon Agner, Salil Soman, Edward Libfeld, Margie McDonald, Mark Rosen, Mitchell Schnall, Deanna Chin, John Nosher, Anant Madabhushi

Proceedings Volume 6915, 69152C (2008) https://doi.org/10.1117/12.770920

KEYWORDS: Magnetic resonance imaging, Breast, Image classification, Breast cancer, Principal component analysis, Computer aided diagnosis and therapy, Mammography, Cancer, Image segmentation, Tissues

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Dynamic contrast enhanced (DCE) MRI has emerged as a promising new imaging modality for breast cancer screening. Currently, radiologists evaluate breast lesions based on qualitative description of lesion morphology and contrast uptake profiles. However, the subjectivity associated with qualitative description of breast lesions on DCE-MRI introduces a high degree of inter-observer variability. In addition, the high sensitivity of MRI results in poor specificity and thus a high rate of biopsies on benign lesions. Computer aided diagnosis (CAD) methods have been previously proposed for breast MRI, but research in the field is far from comprehensive. Most previous work has focused on either quantifying morphological attributes used by radiologists, characterizing lesion intensity profiles which reflect uptake of contrast dye, or characterizing lesion texture. While there has been much debate on the relative importance of the different classes of features (e.g., morphological, textural, and kinetic), comprehensive quantitative comparisons between the different lesion attributes have been rare. In addition, although kinetic signal enhancement curves may give insight into the underlying physiology of the lesion, signal intensity is susceptible to MRI acquisition artifacts such as bias field and intensity non-standardness. In this paper, we introduce a novel lesion feature that we call the kinetic texture feature, which we demonstrate to be superior compared to the lesion intensity profile dynamics. Our hypothesis is that since lesion intensity is susceptible to artifacts, lesion texture changes better reflect lesion class (benign or malignant). In this paper, we quantitatively demonstrate the superiority of kinetic texture features for lesion classification on 18 breast DCE-MRI studies compared to over 500 different morphological, kinetic intensity, and lesion texture features. In conjunction with linear and non-linear dimensionality reduction methods, a support vector machine (SVM) classifier yielded classification accuracy and positive predictive values of 78% and 86% with kinetic texture features compared to 78% and 73% with morphological features and 72% and 83% with textural features, respectively.

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