Anant Madabhushi Assistant Professor Rutgers University Dept. of Biomedical Engineering 599 Taylor Road Piscataway. N. J. 08854 (732) 445-4500, Ext 6213 FAX - 3753 anantm@rci.rutgers.edu

Computer aided diagnosis, prostate cancer, breat cancer, image analysis, machine learning, prognosis, digital pathology, MRI

The complexity of biological systems and the vast amount of information now available at the level of genes, proteins, metabolites, tissues, and organs, requires developing quantitative computational methods to define relationships between structure and function at multiple biological scales. For instance, while Magnetic resonance imaging (MRI) and MR spectroscopy can probe a variety of physiological (e.g. blood vessel permeability) and metabolic characteristics of cancer, little is known about the changes in gene expression that underlie the spectral and imaging features observed in cancer. The ability to map genomic and proteomic expressions to /in-vivo/ imaging opens the potential for new insights into disease characterization and a better understanding of disease mechanisms.

As Director of the Laboratory for Computational Imaging and Bioinformatics at Rutgers University, Dr. Madabhushi's research goals have centered on developing novel computerized image and spectral analysis, and multi-modal registration tools to facilitate synergistic and correlative analysis of disease signatures across multiple scales and functionalities -- from gene and protein expression to spectroscopy to histopathology and to MRI, for early computerized diagnosis, prognosis, and theragnosis of prostate, breast, and ovarian cancer/.

We seek to develop a quantifiable framework to link and integrate the cross-modality phenotype mappings for cancer across spatial scales, which will provide the ability to map profiles of proteins and enzymes to their corresponding imaging and/or histological image parameters on a pixel-by-pixel basis. The framework will facilitate identification of specific subsets of cross-modal features that relate to the emergence of a specific phenotype. We have also been developing machine learning schemes for combining measurements at multiple scales and modalities into an integrated prediction score that can provide a superior outcome prediction compared to a single predictor.

Selected Publications

Tiwari P, Rosen M, Madabhushi A. (2009) A hierarchical spectral clustering and nonlinear dimensionality reduction scheme for detection of prostate cancer from magnetic resonance spectroscopy (MRS). Med Phys. 36(9):3927-39.

Lexe G, Monaco J, Doyle S, Basavanhally A, Reddy A, Seiler M, Ganesan S, Bhanot G, Madabhushi A. (2009) Towards Improved Cancer Diagnosis and Prognosis using Analysis of Gene Expression Data and Computer Aided Imaging. Exp Biol Med (Maywood). 234(8):860-79.

Tiwari P, Rosen M, Madabhushi A. (2008) Consensus-locally linear embedding (C-LLE): application to prostate cancer detection on magnetic resonance spectroscopy. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv. 11(Pt 2):330-8.

Viswanath S, Bloch BN, Genega E, Rofsky N, Lenkinski R, Chappelow J, Toth R, Madabhushi A. (2008) A comprehensive segmentation, registration, and cancer detection scheme on 3 Tesla in vivo prostate DCE-MRI. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv. 11(Pt 1):662-9.

(2008) Toth R, Chappelow J, Rosen M, Pungavkar S, Kalyanpur A, Madabhushi A. Multi-attribute non-initializing texture reconstruction based active shape model (MANTRA). Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv. 11(Pt 1):653-61.

Lee G, Rodriguez C, Madabhushi A. (2008) Investigating the efficacy of nonlinear dimensionality reduction schemes in classifying gene and protein expression studies.
IEEE/ACM Trans Comput Biol Bioinform. Jul-Sep;5(3):368-84.

Tiwari P, Madabhushi A, Rosen M. (2007) A hierarchical unsupervised spectral clustering scheme for detection of prostate cancer from magnetic resonance spectroscopy (MRS). Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv. 10(Pt 2):278-86.

Doyle S, Rodriguez C, Madabhushi A, Tomaszeweski J, Feldman M. (2006) Detecting prostatic adenocarcinoma from digitized histology using a multi-scale hierarchical classification approach. Conf Proc IEEE Eng Med Biol Soc. 1:4759-62.

Madabhushi A, Yang P, Rosen M, Weinstein S. (2006) Distinguishing lesions from posterior acoustic shadowing in breast ultrasound via non-linear dimensionality reduction. Conf Proc IEEE Eng Med Biol Soc. 1:3070-3.

Souza A, Udupa JK, Madabhushi A. (2008) Image filtering via generalized scale. Med Image Anal. 12(2):87-98. Epub 2007 Aug 9.

Doyle S, Madabhushi A, Feldman M, Tomaszeweski J. (2006)A boosting cascade for automated detection of prostate cancer from digitized histology. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv. 9(Pt 2):504-11.

Madabhushi A, Udupa JK. (2006) New methods of MR image intensity standardization via generalized scale. Med Phys. 33(9):3426-34.

Madabhushi A, Udupa JK, Moonis G. (2006) Comparing MR image intensity standardization against tissue characterizability of magnetization transfer ratio imaging. J Magn Reson Imaging. 24(3):667-75.

Madabhushi A, Shi J, Rosen M, Tomaszeweski JE, Feldman MD. (2005) Graph embedding to improve supervised classification and novel class detection: application to prostate cancer. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv. 8(Pt 1):729-37.

Madabhushi A, Feldman MD, Metaxas DN, Tomaszeweski J, Chute D. (2005) Automated detection of prostatic adenocarcinoma from high-resolution ex vivo MRI. IEEE Trans Med Imaging. 24(12):1611-25.

Madabhushi A, Udupa JK. (2005) Interplay between intensity standardization and inhomogeneity correction in MR image processing. IEEE Trans Med Imaging. 24(5):561-76.

Madabhushi A, Metaxas DN. (2003) Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions. IEEE Trans Med Imaging. 22(2):155-69.