13 March 2017 Frequency-based similarity detection of structures in human brain
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Abstract
Advancements in 3D scanning and volumetric imaging methods have motivated researchers to tackle new challenges related to storing, retrieving and comparing 3D models, especially in medical domain. Comparing natural rigid shapes and detecting subtle changes in 3D models of brain structures is of great importance. Precision in capturing surface details and insensitivity to shape orientation are highly desirable properties of good shape descriptors. In this paper, we propose a new method, Spherical Harmonics Distance (SHD), which leverages the power of spherical harmonics to provide more accurate representation of surface details. At the same time, the proposed method incorporates the features of a shape distribution method (D2) and inherits its insensitivity to shape orientation. Comparing SHD to a spherical harmonics based method (SPHARM) shows that the performance of the proposed method is less sensitive to rotation. Also, comparing SHD to D2 shows that the proposed method is more accurate in detecting subtle changes. The performance of the proposed method is verified by calculating the Fisher measure (FM) of extracted feature vectors. The FM of the vectors generated by SHD on average shows 27 times higher values than that of D2. Our preliminary results show that SHD successfully combines desired features from two different methods and paves the way towards better detection of subtle dissimilarities among natural rigid shapes (e.g. structures of interest in human brain). Detecting these subtle changes can be instrumental in more accurate diagnosis, prognosis and treatment planning.
Conference Presentation
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Dave I. Sims, Mohammad-Reza Siadat, "Frequency-based similarity detection of structures in human brain", Proc. SPIE 10138, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications, 1013804 (13 March 2017); doi: 10.1117/12.2247085; https://doi.org/10.1117/12.2247085
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