An automatic method is presented to extract edema around spontaneous intracerebral hemorrhage (SICH). A new way to cluster edema based on region growing is proposed, with seeds derived from expectation-maximization algorithm, local grayscale mean derived from adaptive local thresholding with varied window sizes, and growing rules that combines local grayscale mean and grayscale information in the form of two-dimensional entropy. The algorithm has been validated on 36 patient datasets to achieve a Dice coefficient of 0.79 in less than 3 minutes. It may provide a potential tool for neurosurgeons to quantify edema and guide therapy of patients with SICH.
Chen, Mingyang; Hu, Qingmao; Liu, Zhenchuan; Zhou, Shoujun; and Li, Xiaodong
"Segmentation of Cerebral Edema Around Spontaneous Intracerebral Hemorrhage,"
Applied Mathematics & Information Sciences: Vol. 07:
2, Article 19.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol07/iss2/19