Unsupervised connectivity-based thresholding segmentation of midsagittal brain MR images.
In this paper, we propose an algorithm for automated segmentation of midsagittal brain MR images. First, we apply thresholding to obtain binary images. From the binary images, we locate some landmarks. Based on the landmarks and anatomical information, we preprocess the binary images, which substantially simplifies the subsequent operations. To separate regions what are incorrectly merged after this initial segmentation, a new connectivity-based threshold algorithm is proposed. Assuming that some prior information about the general shape and location of objects is available, the algorithm finds a boundary between two regions using the path connection algorithm and changing the threshold adaptively. In order to test the robustness of the proposed algorithm we applied the algorithm to 120 midsagittal brain images and obtained satisfactory results.
Division of Electrical Engineering, Yonsei University, Seoul, South Korea.
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Lee
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Huh
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Ketter
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Unser
D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement; D007091:Image Processing, Computer-Assisted / Q000145:classification / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D016548:Prosencephalon / Q000033:anatomy & histology; D015203:Reproducibility of Results