Dr. Anant Madabhushi is currently an Assistant Professor, Dept. of Biomedical Engineering, Rutgers University. He received his Bachelors Degree in Biomedical Engineering from Mumbai University, India in 1998 and his Masters in Biomedical Engineering from the University of Texas, Austin in 2000. In 2004 he obtained his PhD in Bioengineering from the University of Pennsylvania. Dr. Madabhushi has more than 20 publications and book chapters in leading International journals and his presented his work at a number of prestigious conferences. His research interests are in the area of medical image analysis, computer-aided diagnosis, machine learning, and computer vision.

Research

Our research focuses on developing novel computer-aided diagnostic (CAD) systems that can assist the doctor by automatically detecting suspicious regions in medical images, such as those obtained from the MRI, CT, and ultrasound scanners. CAD can help in early detection of malignant and pre-malignant lesions, in targeted cancer treatment, and in reducing the number of unnecessary biopsies. By combining sophisticated computer vision, medical image processing, and novel classification tools we have been able to develop highly accurate CAD methods for detecting breast and prostate cancer on ultrasound and high-resolution MRI that in some instances are able to out-perform expert radiologists. Work is also currently underway to develop powerful CAD methods for detecting lung cancer on multi-slice CT and prostatic adenocarcinoma from digitized histological sections. Another research thrust is in the use of sophisticated machine learning and biomedical image analysis methods for understanding tumor progression and identifying tissue classes that are intermediate between normal and malignant.

Recent Papers:

1. Madabhushi, A, Udupa, J, Souza, A, Generalized Scale: Theory, Algorithms and Application to Inhomogeneity Correction, Computer Vision and Image Understanding, In Press.
2. Madabhushi, A, Feldman, M, Metaxas, D, Tomasezweski, J, Chute, D, Automated Segmentation of Prostatic Adenocarcinoma from High Resolution MR by Optimally Combining 3D Texture Features, IEEE Transactions on Medical Imaging, In Press.
3. Madabhushi, A, Udupa, J, Interplay of Inhomogeneity Correction and Intensity Standardization in MR Image Analysis, IEEE Transactions on Medical Imaging, vol. 24[5], pp. 561-576, 2005