[Faculty] Fwd: [CSRC-SDSU COLLOQUIUM]: Automated Histopathological Image Analysis to Predict Breast Cancer Disease survival and Recurrency

[Jose Castillo]

*DATE:*  Friday, October 17th, 2014

*TITLE:*  Automated Histopathological Image Analysis to Predict Breast
Cancer Disease survival and Recurrency

*TIME:*  3:30 PM

*LOCATION:*  GMCS 214

*SPEAKER:*  Dr. Baidya Nath Saha. University of Calgary, Canada

*ABSTRACT:*  Immunohistochemical staining of the proliferation antigen
Ki-67, tumor epithelial marker Pan Cytokeratin and nuclear stain DAPI are
routinely used in the diagnostic and prognostic assessment of breast
cancer. In clinical practice, these histological slides are interpreted by
a pathologist which are not only prone to inter-observer variation, but are
also tedious and time-consuming. To overcome these, an automated image
analysis is proposed which can be decomposed into three steps:  (a)
segmentation, (b) feature extraction and (c) classification. At first, a
novel level set based energy functional is proposed to segment the cell
boundaries from Ki-67, Pan Cytokeratin and DAPI images. The novel energy
functional allows an individual energy functional for each cell and the
individual energy functional provide repulsive forces among the neighboring
cells that enable to separate the overlapping cells. Then a fast ellipse
fitting algorithm is applied along each cell contour and we compute the
histogram of the deviation of the cell boundaries from the best fitted
ellipse to capture the inherent morphological complexity of epithelial
architecture. It is assumed that the shapes of the normal cells are
elliptical in nature and the abnormality of the epithelial cell
architecture is also exploited for disease prognosis (distant recurrence /
non-recurrence breast cancer, five year-survival or less).  This histogram
information is fed into a novel K-Nearest Neighbor (kNN) regularized SVM
classifier. We propose a new k-Nearest Neighbor (kNN) based regularization
term into SVM optimization framework which improves the classification
accuracy of SVM by leveraging the strengths of both SVM and kNN. The
intuition is that data points that are mostly surrounded by the data points
belong to its own class are given more priority during training to increase
generalization ability. Results of these proposed methods are demonstrated
superiority over state-of-the-art techniques. Improving the performance of
the proposed method by exploiting a pool of shape, gradient and intensity
based features to capture the complex epithelial architecture for disease
diagnosis and prognosis is the future work of this research.

*HOST:*  Dr. Mauro Tambasco

For future events, please visit our website at:

http://www.csrc.sdsu.edu/colloquium.html

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Jose E. Castillo  Ph.D.

Director / Professor

Computational Science Research Center

5500 Campanile Dr

San Diego State University

San Diego CA 92182-1245

619 5947205/3430, Fax 619-594-2459

 http://www.csrc.sdsu.edu/mimetic-book/

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