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Automatic Sulcal Curve Extraction on the Human Cortical Surface

1Computer Science, University of North Carolina, Chapel Hill, NC, USA.
2Psychiatry, University of North Carolina, Chapel Hill, NC, USA.
Publication Date:
Proc SPIE Int Soc Opt Eng
Volume Number:
Proc SPIE Int Soc Opt Eng. 2015 Mar;9413.
PubMed ID:
Appears in Collections:
P30 HD003110/HD/NICHD NIH HHS/United States
R01 HD053000/HD/NICHD NIH HHS/United States
R01 HD055741/HD/NICHD NIH HHS/United States
R01 MH060352/MH/NIMH NIH HHS/United States
R01 MH091645/MH/NIMH NIH HHS/United States
R42 NS059095/NS/NINDS NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Lyu I., Kim S.H., Styner M. Automatic Sulcal Curve Extraction on the Human Cortical Surface. Proc SPIE Int Soc Opt Eng. 2015 Mar;9413. PMID: 26028801. PMCID: PMC4449147.
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The recognition of sulcal regions on the cortical surface is an important task to shape analysis and landmark detection. However, it is challenging especially in a complex, rough human cortex. In this paper, we focus on the extraction of sulcal curves from the human cortical surface. The previous sulcal extraction methods are time-consuming in practice and often have a difficulty to delineate curves correctly along the sulcal regions in the presence of significant noise. Our pipeline is summarized in two main steps: 1) We extract candidate sulcal points spread over the sulcal regions. We further reduce the size of the candidate points by applying a line simplification method. 2) Since the candidate points are potentially located away from the exact valley regions, we propose a novel approach to connect candidate sulcal points so as to obtain a set of complete curves (line segments). We have shown in experiment that our method achieves high computational efficiency, improved robustness to noise, and high reliability in a test-retest situation as compared to a well-known existing method.

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