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Three-dimensional visualization of the functional fascicular groups of a long-segment peripheral nerve

Neural Regeneration Research 2018年第8期13卷 1465-1470页

作者： Jian Qi Wei-Ya Wang Ying-Chun Zhong Jia-Ming Zhou Peng Luo Ping Tang Cai-Feng He Shuang Zhu Xiao-Lin Liu Yi Zhang Department of Orthopedics and Microsurgery First Affiliated Hospital of Sun Yat-sen University Department of Plastic Surgery Nanxishan Hospital of Guangxi Zhuang Autonomous Region automation College Guangdong University of Technology Scientific Research Department Sixth Affiliated Hospital of Sun Yat-sen University Department of Orthopedics Shenzhen Nanshan People's Hospital Zhongshan University Medical Equipment Co. Ltd. Department of Joint and Orthopedics Orthopedic Center Zhujiang Hospital of Southern Medical University Department of Plastic Surgery First Affiliated Hospital of Sun Yat-sen University

The three-dimensional（3D） visualization of the functional bundles in the peripheral nerve provides direct and detailed intraneural spatial information. It is useful for selecting suitable surgical methods to repair nerve defects and in optimizing the construction of tissue-engineered nerve grafts. However, there remain major technical hurdles in obtaining, registering and interpreting 2D images, as well as in establishing 3D models. Moreover, the 3D models are plagued by poor accuracy and lack of detail and cannot completely reflect the stereoscopic microstructure inside the nerve. To explore and help resolve these key technical problems of 3D reconstruction, in the present study, we designed a novel method based on re-imaging techniques and computer image layer processing technology. A 20-cm ulnar nerve segment from the upper arm of a fresh adult cadaver was used for acetylcholinesterase（ACh E） staining. Then, 2D panoramic images were obtained before and after ACh E staining under the stereomicroscope. Using layer processing techniques in Photoshop, a space transformation method was used to fulfill automatic registration. The contours were outlined, and the 3D rendering of functional fascicular groups in the long-segment ulnar nerve was performed with Amira 4.1 software. The re-imaging technique based on layer processing in Photoshop produced an image that was detailed and accurate. The merging of images was accurate, and the whole procedure was simple and fast. The least square support vector machine was accurate, with an error rate of only 8.25%. The 3D reconstruction directly revealed changes in the fusion of different nerve functional fascicular groups. In conclusion. The technique is fast with satisfactory visual reconstruction.

关键词： nerve regeneration peripheral nerve ulnar nerve three-dimensional reconstruction functional fascicular group registration segmentation locating spots auto-registration acetylcholinesterase neural regeneration

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Texture classification based on EMD and FFT

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2006年第9期7卷 1516-1521页

作者： XIONG Chang-zhen XU Jun-yi ZOU Jian-cheng QI Dong-xu School of Information Science and Technology Sun Yat-sen University Guangzhou 510275 China Information Science School Guangdong University of Business Studies Guangzhou 510320 China College of Science North China University of Technology Beijing 100041 China

Empirical mode decomposition (EMD) is an adaptive and approximately orthogonal filtering process that reflects human’s visual mechanism of differentiating textures. In this paper, we present a modified 2D EMD algorithm using the FastRBF and an appropriate number of iterations in the shifting process (SP), then apply it to texture classification. Rotation-invariant texture feature vectors are extracted using auto-registration and circular regions of magnitude spectra of 2D fast Fourier transform (FFT). In the experiments, we employ a Bayesion classifier to classify a set of 15 distinct natural textures selected from the Brodatz album. The experimental results, based on different testing datasets for images with different orientations, show the effectiveness of the proposed classification scheme.

关键词： Texture classification Empirical mode decomposition (EMD) Fourier transform auto-registration Rotationinvariant

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