Dr. Bülent Yener Profile

Dr. Bülent Yener

Professor at Rensselaer Polytechnic Institute

SPIE Involvement:

Conference Program Committee | Author

Proceedings Article | 1 March 2017 Paper

A computational study on convolutional feature combination strategies for grade classification in colon cancer using fluorescence microscopy data

Aritra Chowdhury, Christopher Sevinsky, Alberto Santamaria-Pang, Bülent Yener

Proceedings Volume 10140, 101400Q (2017) https://doi.org/10.1117/12.2255687

KEYWORDS: Colorectal cancer, Feature extraction, Microscopy, Neural networks, Tissues, Pathology, Machine learning, Cancer, Diagnostics, Convolutional neural networks, Proteins, Matrices, Databases

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Proceedings Article | 23 March 2016 Paper

A machine learning approach to quantifying noise in medical images

Aritra Chowdhury, Christopher Sevinsky, Bülent Yener, Kareem Aggour, Steven Gustafson

Proceedings Volume 9791, 97910U (2016) https://doi.org/10.1117/12.2217702

KEYWORDS: Image quality, Medical imaging, Image analysis, Image processing, Machine learning, Tissues, Image classification, Colorectal cancer, Denoising, Biomedical optics, Image restoration, Image filtering, Data modeling

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Proceedings Article | 23 March 2016 Paper

Model coupling for predicting a developmental patterning process

Nimit Dhulekar, Basak Oztan, Bülent Yener

Proceedings Volume 9791, 979104 (2016) https://doi.org/10.1117/12.2217528

KEYWORDS: Performance modeling, Image segmentation, Interfaces

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Physics-based-theoretical models have been used to predict developmental patterning processes such as branching morphogenesis for over half a century. While such techniques are quite successful in understanding the patterning processes in organs such as the lung and the kidney, they are unable to accurately model the processes in other organs such as the submandibular salivary gland. One possible reason is the detachment of these models from data that describe the underlying biological process. This hypothesis coupled with the increasing availability of high quality data has made discrete, data-driven models attractive alternatives. These models are based on extracting features from data to describe the patterns and their time evolving multivariate statistics. These discrete models have low computational complexity and comparable or better accuracy than the continuous models. This paper presents a case study for coupling continuous-physics-based and discrete-empirical-models to address the prediction of cleft formation during the early stages of branching morphogenesis in mouse submandibular salivary glands (SMG). Given a time-lapse movie of a growing SMG, first we build a descriptive model that captures the underlying biological process and quantifies this ground truth. Tissue-scale (global) morphological features are used to characterize the biological ground truth. Second, we formulate a predictive model using the level-set method that simulates branching morphogenesis. This model successfully predicts the topological evolution, however, it is blind to the cellular organization, and cell-to-cell interactions occurring inside a gland; information that is available in the image data. Our primary objective via this study is to couple the continuous level set model with a discrete graph theory model that captures the cellular organization but ignores the forces that determine the evolution of the gland surface, i.e. formation of clefts and buds. We compared the prediction accuracy of our model to an on-lattice Monte-Carlo simulation model which has been used extensively for modeling morphogenesis and organogenesis. The results demonstrate that the coupled model yields comparable simulations of gland growth to that of the Monte-Carlo simulation model with a significantly lower computational complexity.

Proceedings Article | 23 February 2012 Paper

Follicular lymphoma grading using cell-graphs and multi-scale feature analysis

Basak Oztan, Hui Kong, Metin Gürcan, Bülent Yener

Proceedings Volume 8315, 831516 (2012) https://doi.org/10.1117/12.911360

KEYWORDS: Feature extraction, Lymphoma, Tissues, Image segmentation, Feature selection, Cancer, Machine learning, Image analysis, Computing systems, Model-based design

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