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Automatic detection of boundaries of brain tumor
This paper characterize the brain lesions in CT images, and describes a knowledge-guided boundary detection algorithm that is both data- and goal-driven.
Abstract
An important computational step in computer-aided neurosurgery is the extraction of boundaries of lesions in a series of images. Currently in many clinical applications, the boundaries of lesions are traced manually. Manual methods are not only tedious but also subjective, leading to substantial inter- and intraobserver variability. Furthermore, recent studies show that human observation of a lesion is not sufficient to guarantee accurate localization. With clinical images, possible confusion between lesions and coexisting normal structures (like blood vessels) is a serious constraint on an observer's performance. Automatic detection of lesions is a non-trivial problem. Typically the boundaries of lesions in CT images are of single-pixel width, and the gradient at the lesion boundary varies considerably. As many studies show, these characteristics of lesions within CT images, in conjunction with the generally low signal-to-ratio of CT images, render simple boundary detection techniques ineffective. In this paper we characterize the brain lesions in CT images, and describe a knowledge-guided boundary detection algorithm. The algorithm is both data- and goal-driven.