MRI images have been used for a wide variety of medical applications for a long time because they are safe and highly sensitive at detecting of tissue abnormalities that indicate cancer. MRI is generated by measuring the response of tissue components to a magnetic field. Also, based on the published statistics, brain tumor is one of the most common causes of death and early detection and monitoring is crucial for treatment. Literature and market review suggests that although extensive research exists on brain tumor detection using MRI images, MRI?based systems designed for brain tumor detection that have ultimate clinical value and use are lacking. Accordingly, we propose a new software technology that effectively detect, segment, classify and monitor brain tumor in MRI images.
There have been extensive efforts on brain tumor detection in MR images because early detection has been always crucial for success of treatment. For ongoing work in our parent RO1 grant we are developing new machine learning and image analysis methods that for automatic detection and segmentation of brain tumor in MRI images, which is expected to help patients to have a much better chance of successful treatment. If successful, the proposed software technology in this project may potentially be the first that may be commercialized for brain tumor segmentation and classification on MRI images.
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