Rainbow-tinted scan of a ribcage.

Medical Imaging

Malone researchers develop novel medical imaging techniques and analysis to improve surgical procedures, radiation therapy, and the diagnosis and treatment of neurological disorders such as epilepsy and autism spectrum disorders.


Muyinatu A. Lediju Bell

John C. Malone Associate Professor of Electrical and Computer Engineering

Emad Boctor

Director, Medical UltraSound Imaging and Intervention Collaboration

Philippe Burlina

Principal Scientist, Intelligent Systems Center, Johns Hopkins Applied Physics Laboratory

Nicholas Durr

Associate Professor of Biomedical Engineering

Israel Gannot

Adjunct Professor of Biomedical Engineering

Ferdinand Hui

Co-Director, Radiology Artificial Intelligence Lab (RAIL)

Todd McNutt

Director Of Clinical Informatics, Department of Radiation Oncology and Molecular Radiation Sciences

Haris Sair

Co-Director, Radiology Artificial Intelligence Lab (RAIL)

Jeffrey H. Siewerdsen

John C. Malone Professor of Biomedical Engineering

Russell H. Taylor

John C. Malone Professor of Computer Science

Paul Yi

Adjunct Assistant Research Scientist, Malone Center for Engineering in Healthcare

Related Projects

Noninvasive Localization of Epileptic Seizures

Epilepsy affects nearly 3.5 million people in the United States and is linked to a five-fold increase in mortality. While epilepsy is often controlled with medication, 20-40% of patients are medically refractory and continue to experience seizures in spite of drug therapies. The alternative for...

Photoacoustic-Guided Hysterectomy Performed with a da Vinci Surgical Robot

Approximately 600,000 hysterectomies are performed each year in the US to remove the uterus. This surgery requires cauterization and clipping of the uterine arteries, located close to and overlapping the ureter, which can cause accidental injury to the ureter. We are developing...


The overall goal of the SpineCloud project is to gain understanding and predictive power of the factors underlying spine surgery outcomes – particularly the unacceptably broad variability associated with spinal fusion. Such capability will yield evidence-based insight and decision support to improve patient selection, enable more optimal surgical planning, identify...

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