III: Small: Collaborative Research: Modeling, Detection, and Analysis of Branching Structures in Medical Imaging

Supported by the National Science Foundation (NSF) -

Investigators

Vasileios Megalooikonomou, PhD
Department of Computer and Information Sciences

Temple University
314 Wachman Hall

1805 N. Broad Street

Philadelphia PA 19122

215-204-5774, 215-204-5774, 215-204-5082 (fax)

vasilis AT cis DOT temple DOT edu
http://www.cis.temple.edu/~vasilis

Haibin Ling, PhD
Department of Computer and Information Sciences

Temple University
324 Wachman Hall

1805 N. Broad Street

Philadelphia PA 19122

215-204-5697, 215-204-5082 (fax)
hbling AT temple DOT edu
http://www.ist.temple.edu/~hbling

Predrag Bakic, PhD
Department of Radiology

Hospital of the University of Pennsylvania
1 Silverstein Building

3400 Spruce Street

Philadelphia, PA 19104

215-746-8758, 215-746-8764 (fax)
Predrag.Bakic AT uphs DOT upenn DOT edu
http://www.med.upenn.edu/apps/faculty/index.php/g334/p203846

List of Personnel

Vasileios Megalooikonomou, PhD, Associate Professor of Computer Science, Temple Univ.
Haibin Ling, PhD, Assistant Professor of Computer Science, Temple Univ.
Predrag R. Bakic, PhD, Research Assistant Professor of Radiology, Hospital of the University of Pennsylvania.
Despina Kontos, PhD, Research Assistant Professor of Radiology, Hospital of the University of Pennsylvania.
Andrew Maidment, PhD, Associate Professor of Radiology, Chief of the Physics Section of Radiology, Hospital of the University of Pennsylvania.
Tatyana Nuzhnaya, Ph.D. student, Temple Univ.
Erkang Cheng, Ph.D. student, Temple Univ.

Keywords

Data mining

breast images

branching structures

texture analysis

databases
imaging biomarkers

modeling

Project Summary

Detection and analysis of branching structures and/or texture is very challenging; it arises in many areas of science and engineering (e.g., medical images, chemical compounds, etc). The objective of this project is to develop novel approaches to model, detect, and analyze branching structures obtained from multimodality data. Such representation and analysis tools are expected to make many complex problems more tractable. Examples include identifying and recognizing a large number of structure classes; discovering new relationships among structure, texture, and function or pathology; evaluating hypotheses; developing modeling tools; assisting with surgical design; and managing medical image data efficiently. Specifically, the investigators plan to explore three research topics under this project: (1) To develop descriptors of branching structures and texture, and knowledge discovery tools that will enable hypotheses generation and evaluation and improve modeling of branching structures; (2) To design automated algorithms and a flexible framework to detect branching structures. The investigators are especially interested in addressing challenges of occlusion and topology change; (3) To demonstrate the applicability of the proposed tools to breast imaging by building a prototype database of images from various modalities and associated clinical data that will provide advanced analysis and visualization capabilities. Though the investigators use breast imaging as the driving application, the proposed project is expected to provide software and data resources that can assist clinical tasks and scientific discoveries in general. Developing automated tools to effectively characterize, detect, and classify tree-like structures in images would provide great insight into the relationship between the branching topology and function or pathology. The investigators plan to further contribute to the medical/scientific community by disseminating the related software and annotated data sets. The educational goals include incorporating research findings to graduate courses at Temple (data mining course, computer vision course and medical image analysis seminar) and at the University of Pennsylvania (medical image analysis course).

Publications and Products

T. Nuzhnaya, M. Barnathan, H. Ling, V. Megalooikonomou, P. Bakic, and A. Maidment, " Probabilistic Branching Node Detection using Adaboost and Hybrid Local Features ", Proceedings of the 7th IEEE International Symposium on Biomedical Imaging (ISBI), Rotterdam, The Netherlands, 2010.

E. Cheng, N. Xie, H. Ling, P. Bakic, A. Maidment, and V. Megalooikonomou, " Mammographic Image Classification Using Histogram Intersection ", Proceedings of the 7th IEEE International Symposium on Biomedical Imaging (ISBI), Rotterdam, The Netherlands, 2010.

H. Ling, L. Bai, E. Blasch, and X. Mei, " Robust Infrared Vehicle Tracking across Target Pose Change using L1 Regularization ", Proceedings of the International Conference on Information Fusion (FUSION), Edinburgh, UK, 2010.

Q. Wei, X. Zhang, W. Hu, and H. Ling, " Compact Visual Codebook for Action Recognition ", Proceedings of IEEE International Conference on Image Processing (ICIP), Hong Kong, China, 2010.

N. Xie, H. Ling, W. Hu, and X. Zhang, " Use Bin-Ratio Information for Category and Scene Classification ", Proceedings of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), San Francisco, 2010.

C. Lu, N. Adluru, H. Ling, G. Zhu, and L. J. Latecki, " Contour Based Object Detection Using Part-Bundles ", Computer Vision and Image Understanding (CVIU), in press.

V. Megalooikonomou, M. Barnathan, D. Kontos, P. R. Bakic, A. D.A. Maidment, " A Representation and Classification Scheme for Tree-like Structures in Medical Images: Analyzing the Branching Pattern of Ductal Trees in X-ray Galactograms ", IEEE Transactions on Medical Imaging, Vol. 28, Issue 4, pp. 487-493, 2009.

Other specific products

Coming soon...

Area References

Huo Z., Giger M.L., Wolverton DE, Zhong W., Cumming S., Olopade O.I., Computerized analysis of mammographic parenchymal patterns for breast cancer risk assessment: feature selection, Medical Physics, 27:4-12, (2000).

E. A. Hoffman, J. M. Reinhardt, M. Sonka, B.A. Simon, J. Guo, O. Saba, D. Chon, S. Samrah, H. Shikata, J. Tschirren, K. Palagyi, K. C. Beck, and G. McLennan, Characterization of the Interstitial Lung Diseases via Density-Based and Texture-Based Analysis of Computed Tomography Images of Lung Structure and Function, Academic Radiology, 10 (10), 1104-1118, (2003).

CS Atwood, RC Hovey, JP Glover, G Chepko, E Ginsburg, WG Robison, and BK Vonderhaar, Progesterone induces side-branching of the ductal epithelium in the mammary glands of peripubertal mice. Journal of Endocrinology,167(1):39-52, (2000).

M. Barnathan, J. Zhang, D. Kontos, P. Bakic, A. Maidment, V. Megalooikonomou, .Analyzing Tree-Like Structures in Biomedical Images Based on Texture and Branching: An Application to Breast Imaging., Proceedings of the International Workshop on Digital Mammography (IWDM), Tucson, AZ, Digital Mammography, Lecture Notes in Computer Science, Vol. 5116, pp. 25-32, (2008).

Ling H, Zhou SK, Zheng Y, Georgescu B, Suehling M, Comaniciu D. Hierarchical, Learning-based Automatic Liver Segmentation. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA, June 2008.

M.A. Fischler and R.A. Elschlager. The representation and matching of pictorial structures. IEEE Transactions on Computer, 22(1):67-92, January 1973.

Kirbas, C. and Quek, F. 2004. A review of vessel extraction techniques and algorithms. ACM Comput. Surv. 36, 2 (Jun. 2004), 81-121.

M. Barnathan, J. Zhang, E. Miranda, V. Megalooikonomou, S. Faro, H. Hensley, L. D. Valle, K. Khalili, J. Gordon, F. B. Mohamed, "A Texture-Based Methodology For Identifying Tissue Type in Magnetic Resonance Images", Proceedings of the 5th IEEE International Symposium on Biomedical Imaging (ISBI), Paris, France, pp. 464-467, 2008.

V. Megalooikonomou, M. Barnathan, D. Kontos, P. R. Bakic, A. D.A. Maidment, " A Representation and Classification Scheme for Tree-like Structures in Medical Images: Analyzing the Branching Pattern of Ductal Trees in X-ray Galactograms," IEEE Transactions on Medical Imaging, Vol. 28, No, 4, pp. 487-493, April 2009.

David Lesage, Elsa D. Angelini, Isabelle Bloch, Gareth Funka-Lea, "A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes." Medical Image Analysis, Volume 13, Issue 6, December 2009, Pages 819-845

Project Websites

http://knight.cis.temple.edu/~vasilis/research/branchingstructures.html

This is the main website for our project.