Magnetic resonance imaging, computed tomography, mammography, ultrasound and other digital imaging techniques are invaluable tools for medical applications today. These techniques have been used to map the anatomy of individuals, thus increasing our knowledge of disease, and hence are used for diagnosis and treatment. Image segmentation algorithms are used for the automatic delineation of anatomical structures or other regions of interest in a digital image. There is no single acceptable segmentation algorithm for all applications. General methods exist which can be applied to a variety of data. However, using prior knowledge about the data for which the application is intended can enhance performance.

The focus of this work was to develop a scheme to automatically detect lesions in T1-weighted MR images of patients with a history of stroke. Manual segmentation is the most accurate technique to map the lesions in the brain and is considered to be the gold standard. Thus, an automated segmentation algorithm was developed in this project. This approach leads to huge savings in the time required to map the lesions compared to the manual tracing method. The performance was compared with results obtained by manual segmentation and we observed improvements in accuracy and speed compared with the most current region-growing algorithm.

Here is an example of (a) an image containing a lesion, along with (b) the results of manual segmentation and (c) automated segmentation:

This work was a collaborative effort with Prof. Pélagie Beeson in the Dept. of Speech & Hearing Sciences.