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.
Dave I. Sims and 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 (Presented at SPIE Medical Imaging: February 15, 2017; Published: 13 March 2017); https://doi.org/10.1117/12.2247085.
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