• Title/Summary/Keyword: voxel

Search Result 416, Processing Time 0.031 seconds

Effects of Various Intracranial Volume Measurements on Hippocampal Volumetry and Modulated Voxel-based Morphometry (두개강의 용적측정법이 해마의 용적측정술과 화소기반 형태계측술에 미치는 영향)

  • Tae, Woo-Suk;Kim, Sam-Soo;Lee, Kang-Uk;Nam, Eui-Cheol
    • Investigative Magnetic Resonance Imaging
    • /
    • v.13 no.1
    • /
    • pp.63-73
    • /
    • 2009

  • Purpose : To investigate the effects of various intracranial volume (ICV) measurement methods on the sensitivity of hippocampal volumetry and modulated voxel-based morphometry (mVBM) in female patients with major depressive disorder (MDD). Materials and Methods : T1 magnetic resonance imaging (MRI) data for 41 female subjects (21 MDD patients, 20 normal subjects) were analyzed. Hippocampal volumes were measured manually, and ICV was measured manually and automatically using the FreeSurfer package. Gray and white matter volumes were measured separately. Results : Manual ICV normalization provided the greatest sensitivity in hippocampal volumetry and mVBM, followed by FreeSurfer ICV, GWMV, and GMV. Manual and FreeSurfer ICVs were similar in normal subjects (p = 0.696), but distinct in MDD patients (p = 0.000002). Manual ICV-corrected total gray matter volume (p = 0.0015) and Manual ICV-corrected bilateral hippocampal volumes (right, p = 0.014; left, p = 0.004) were decreased significantly in MDD patients, but the differences of hippocampal volumes corrected by FreeSurfer ICV, GWMV, or GMV were not significant between two groups (p > 0.05). Only manual ICV-corrected mVBM analysis was significant after correction for multiple comparisons. Conclusion : The method of ICV measurement greatly affects the sensitivity of hippocampal volumetry and mVBM. Manual ICV normalization showed the ability to detect differences between women with and without MDD for both methods.

  • PDF

Reconfiguration of Physical Structure of Vegetation by Voxelization Based on 3D Point Clouds (3차원 포인트 클라우드 기반 복셀화에 의한 식생의 물리적 구조 재구현)

  • Ahn, Myeonghui;Jang, Eun-kyung;Bae, Inhyeok;Ji, Un
    • KSCE Journal of Civil and Environmental Engineering Research
    • /
    • v.40 no.6
    • /
    • pp.571-581
    • /
    • 2020

  • Vegetation affects water level change and flow resistance in rivers and impacts waterway ecosystems as a whole. Therefore, it is important to have accurate information about the species, shape, and size of any river vegetation. However, it is not easy to collect full vegetation data on-site, so recent studies have attempted to obtain large amounts of vegetation data using terrestrial laser scanning (TLS). Also, due to the complex shape of vegetation, it is not easy to obtain accurate information about the canopy area, and there are limitations due to a complex range of variables. Therefore, the physical structure of vegetation was analyzed in this study by reconfiguring high-resolution point cloud data collected through 3-dimensional terrestrial laser scanning (3D TLS) in a voxel. Each physical structure was analyzed under three different conditions: a simple vegetation formation without leaves, a complete formation with leaves, and a patch-scale vegetation formation. In the raw data, the outlier and unnecessary data were filtered and removed by Statistical Outlier Removal (SOR), resulting in 17%, 26%, and 25% of data being removed, respectively. Also, vegetation volume by voxel size was reconfigured from post-processed point clouds and compared with vegetation volume; the analysis showed that the margin of error was 8%, 25%, and 63% for each condition, respectively. The larger the size of the target sample, the larger the error. The vegetation surface looked visually similar when resizing the voxel; however, the volume of the entire vegetation was susceptible to error.

  • https://doi.org/10.12652/Ksce.2020.40.6.0571 인용 PDF KSCI

Attention based Feature-Fusion Network for 3D Object Detection (3차원 객체 탐지를 위한 어텐션 기반 특징 융합 네트워크)

  • Sang-Hyun Ryoo;Dae-Yeol Kang;Seung-Jun Hwang;Sung-Jun Park;Joong-Hwan Baek
    • Journal of Advanced Navigation Technology
    • /
    • v.27 no.2
    • /
    • pp.190-196
    • /
    • 2023

  • Recently, following the development of LIDAR technology which can detect distance from the object, the interest for LIDAR based 3D object detection network is getting higher. Previous networks generate inaccurate localization results due to spatial information loss during voxelization and downsampling. In this study, we propose an attention-based convergence method and a camera-LIDAR convergence system to acquire high-level features and high positional accuracy. First, by introducing the attention method into the Voxel-RCNN structure, which is a grid-based 3D object detection network, the multi-scale sparse 3D convolution feature is effectively fused to improve the performance of 3D object detection. Additionally, we propose the late-fusion mechanism for fusing outcomes in 3D object detection network and 2D object detection network to delete false positive. Comparative experiments with existing algorithms are performed using the KITTI data set, which is widely used in the field of autonomous driving. The proposed method showed performance improvement in both 2D object detection on BEV and 3D object detection. In particular, the precision was improved by about 0.54% for the car moderate class compared to Voxel-RCNN.

  • https://doi.org/10.12673/jant.2023.27.2.190 인용 PDF HTML