Anant Madabhushi and team awarded patents

Anant Madabhushi, professor of biomedical engineering and director of the Center for Computational Imaging and Personalized Diagnostics (CCIPD), and his team were issued three patents in digital pathology and computer-assisted disease prognosis.
 
US Patent 9,177,105, titled “Quantitatively Characterizing Disease Morphology with Co-Occurring Gland Tensors in Localized Subgraphs,” describes a novel methodology for quantitatively describing disease morphology via gland directional entropy in medical images. The algorithm involves the use of second-order statistics to describe local disorder in gland orientations via co-occurring gland tensors. This technology is being used for predicting disease outcomes in prostate cancer histopathology and on high-resolution MRI.

Co-inventors include George Lee, research assistant professor at Case Western Reserve University, Sahirzeeshan Ali, an electrical engineering PhD student, and Rachel Sparks, postdoctoral researcher at the University College London.
 
U.S. patent 9,177,014, titled “Discriminatively Weighed Multi-Scale Local Binary Patterns,” presents a learning approach that guarantees finding the salient local binary pattern scale without multi-radii sampling. By adopting the approach presented in the patent, prostate cancer detection on T2 Weighted MR (Magnetic Fields) with a higher accuracy and a higher speed becomes feasible.
 
The co-inventor of this patent is Haibo Wang, research staff at Philips Research North America and a former research associate at CCIPD.
 
U.S. patent 9,183,350 entitled "Quantitatively Characterizing Disease Morphology with Cell Orientation Entropy,” relates to the apparatus, methods and other embodiments associated with objectively predicting biochemical recurrence (BCR) with cell orientation entropy (COrE). The technology describes the associate directional disorder of cells with risk of biochemical recurrence in a tissue. The invention could pave the way for developing computerized image based predictors of disease aggressiveness and outcome from digitized tissue pathology alone.
 
Co-inventors include Lee, Ali and Rachel Sparks, a research associate at University College London.