Volume 42 Issue 4
Jul.  2021
Turn off MathJax
Article Contents
Yu-Hang Liao, Chao-Wei Zhou, Wei-Zhen Liu, Jing-Yi Jin, Dong-Ye Li, Fei Liu, Ding-Ding Fan, Yu Zou, Zen-Bo Mu, Jian Shen, Chun-Na Liu, Shi-Jun Xiao, Xiao-Hui Yuan, Hai-Ping Liu. 3DPhenoFish: Application for two- and three-dimensional fish morphological phenotype extraction from point cloud analysis. Zoological Research, 2021, 42(4): 492-502. doi: 10.24272/j.issn.2095-8137.2021.141
Citation: Yu-Hang Liao, Chao-Wei Zhou, Wei-Zhen Liu, Jing-Yi Jin, Dong-Ye Li, Fei Liu, Ding-Ding Fan, Yu Zou, Zen-Bo Mu, Jian Shen, Chun-Na Liu, Shi-Jun Xiao, Xiao-Hui Yuan, Hai-Ping Liu. 3DPhenoFish: Application for two- and three-dimensional fish morphological phenotype extraction from point cloud analysis. Zoological Research, 2021, 42(4): 492-502. doi: 10.24272/j.issn.2095-8137.2021.141

3DPhenoFish: Application for two- and three-dimensional fish morphological phenotype extraction from point cloud analysis

doi: 10.24272/j.issn.2095-8137.2021.141
#Authors contributed equally to this work
Funds:  This work was supported by the National Natural Science Foundation of China (32072980) and Key Research and Development Projects in Tibet (XZ202001ZY0016N, XZ201902NB02, XZNKY-2019-C-053)
More Information
  • Fish morphological phenotypes are important resources in artificial breeding, functional gene mapping, and population-based studies in aquaculture and ecology. Traditional morphological measurement of phenotypes is rather expensive in terms of time and labor. More importantly, manual measurement is highly dependent on operational experience, which can lead to subjective phenotyping results. Here, we developed 3DPhenoFish software to extract fish morphological phenotypes from three-dimensional (3D) point cloud data. Algorithms for background elimination, coordinate normalization, image segmentation, key point recognition, and phenotype extraction were developed and integrated into an intuitive user interface. Furthermore, 18 key points and traditional 2D morphological traits, along with 3D phenotypes, including area and volume, can be automatically obtained in a visualized manner. Intuitive fine-tuning of key points and customized definitions of phenotypes are also allowed in the software. Using 3DPhenoFish, we performed high-throughput phenotyping for four endemic Schizothoracinae species, including Schizopygopsis younghusbandi, Oxygymnocypris stewartii, Ptychobarbus dipogon, and Schizothorax oconnori. Results indicated that the morphological phenotypes from 3DPhenoFish exhibited high linear correlation (>0.94) with manual measurements and offered informative traits to discriminate samples of different species and even for different populations of the same species. In summary, we developed an efficient, accurate, and customizable tool, 3DPhenoFish, to extract morphological phenotypes from point cloud data, which should help overcome traditional challenges in manual measurements. 3DPhenoFish can be used for research on morphological phenotypes in fish, including functional gene mapping, artificial selection, and conservation studies. 3DPhenoFish is an open-source software and can be downloaded for free at https://github.com/lyh24k/3DPhenoFish/tree/master.
  • #Authors contributed equally to this work
  • loading
  • [1]
    Aliyu I, Gana KJ, Musa AA, Agajo J, Orire AM, Abiodun FT, et al. 2017. A proposed fish counting algorithm using digital image processing technique. ATBU, Journal of Science, Technology & Education, 5(1): 1−11.
    Balakrishnama S, Ganapathiraju A. 1998. Linear discriminant analysis-a brief tutorial. Institute for Signal and information Processing, 18(1998): 1−8.
    Balta H, Velagic J, Bosschaerts W, De Cubber G, Siciliano B. 2018. Fast statistical outlier removal based method for large 3D point clouds of outdoor environments. IFAC-PapersOnLine, 51(22): 348−353. doi: 10.1016/j.ifacol.2018.11.566
    Bär T, Reuter JF, Zöllner JM. 2012. Driver head pose and gaze estimation based on multi-template ICP 3-D point cloud alignment. In: Proceedings of 2012 15th International IEEE Conference on Intelligent Transportation Systems. Anchorage: IEEE, 1797–1802.
    Batanov S D, Starostina O S, Baranova I A. 2019. Non-contact methods of cattle conformation assessment using mobile measuring systems. IOP Conference Series: Earth and Environmental Science, 315(3): 032006.
    Besl PJ, McKay ND. 1992. A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2): 239−256. doi: 10.1109/34.121791
    Bradski G, Kaehler A. 2008. Learning OpenCV: Computer Vision with the OpenCV library. O'Reilly Media, Inc.
    Chen XW, Zhu YL, Wu T, Wang ZQ. 2017. The point cloud registration technology based on SAC-IA and improved ICP. J Xi’an Polytech Univ, 31(3): 395−401. (in Chinese)
    Comba L, Biglia A, Aimonino DR, Gay P. 2018. Unsupervised detection of vineyards by 3D point-cloud UAV photogrammetry for precision agriculture. Computers and Electronics in Agriculture, 155: 84−95. doi: 10.1016/j.compag.2018.10.005
    Fernandes AFA, Turra EM, de Alvarenga ÉR, Passafaro TL, Lopes FB, Alves GFO, et al. 2020. Deep Learning image segmentation for extraction of fish body measurements and prediction of body weight and carcass traits in Nile tilapia. Computers and Electronics in Agriculture, 170: 105274. doi: 10.1016/j.compag.2020.105274
    Gongal A, Karkee M, Amatya S. 2018. Apple fruit size estimation using a 3D machine vision system. Information Processing in Agriculture, 5(4): 498−503. doi: 10.1016/j.inpa.2018.06.002
    Hao M M, Yu H L, Li D L. 2015. The measurement of fish size by machine vision-a review. In: Proceedings of 9th IFIP WG 5.14 International Conference on Computer and Computing Technologies in Agriculture. Cham: Springer, 15–32.
    Hartmann A, Czauderna T, Hoffmann R, Stein N, Schreiber F. 2011. HTPheno: an image analysis pipeline for high-throughput plant phenotyping. BMC Bioinformatics, 12(1): 148. doi: 10.1186/1471-2105-12-148
    Huang LW, Li SQ, Zhu AQ, Fan XY, Zhang CY, Wang HY. 2018. Non-contact body measurement for qinchuan cattle with LiDAR sensor. Sensors, 18(9): 3014. doi: 10.3390/s18093014
    Le Cozler Y, Allain C, Caillot A, Delouard JM, Delattre L, Luginbuhl T, et al. 2019. High-precision scanning system for complete 3D cow body shape imaging and analysis of morphological traits. Computers and Electronics in Agriculture, 157: 447−453. doi: 10.1016/j.compag.2019.01.019
    Li ML, Sun CM. 2018. Refinement of LiDAR point clouds using a super voxel based approach. ISPRS Journal of Photogrammetry and Remote Sensing, 143: 213−221. doi: 10.1016/j.isprsjprs.2018.03.010
    Lin GC, Tang YC, Zou XJ, Xiong JT, Li JH. 2019. Guava detection and pose estimation using a low-cost RGB-D sensor in the field. Sensors, 19(2): 428. doi: 10.3390/s19020428
    López-Fanjul C, Toro M Á. 2007. Fundamentos de la Mejora Genética en Acuicultura. Madrid: Genética y Genómica en Acuicultura, 155–182.
    Navarro A, Lee-Montero I, Santana D, Henríquez P, Ferrer MA, Morales A, et al. 2016. IMAFISH_ML: a fully-automated image analysis software for assessing fish morphometric traits on gilthead seabream (Sparus aurata L.), meagre (Argyrosomus regius) and red porgy (Pagrus pagrus). Computers and Electronics in Agriculture, 121: 66−73. doi: 10.1016/j.compag.2015.11.015
    Oliphant TE. 2007. Python for scientific computing. Computing in Science & Engineering, 9(3): 10−20.
    Orts-Escolano S, Morell V, García-Rodríguez J, Cazorla M. 2013. Point cloud data filtering and downsampling using growing neural gas. In: Proceedings of the 2013 International Joint Conference on Neural Networks. Dallas: IEEE, 1–8.
    Pérez-Ruiz M, Tarrat-Martín D, Sánchez-Guerrero MJ, Valera M. 2020. Advances in horse morphometric measurements using LiDAR. Computers and Electronics in Agriculture, 174: 105510. doi: 10.1016/j.compag.2020.105510
    Pezzuolo A, Guarino M, Sartori L, Marinello F. 2018. A feasibility study on the use of a structured light depth-camera for three-dimensional body measurements of dairy cows in free-stall barns. Sensors, 18(2): 673.
    Rusu RB, Cousins S. 2011. 3D is here: point cloud library (PCL). In: Proceedings of 2011 IEEE International Conference on Robotics and Automation. Shanghai: IEEE, 1–4.
    Schnabel R, Wahl R, Klein R. 2007. Efficient RANSAC for point‐cloud shape detection. Computer Graphics Forum, 26(2): 214−226. doi: 10.1111/j.1467-8659.2007.01016.x
    Schroeder WJ, Avila LS, Hoffman W. 2000. Visualizing with VTK: a tutorial. IEEE Computer Graphics and Applications, 20(5): 20−27. doi: 10.1109/38.865875
    Shah SZH, Rauf HT, IkramUllah M, Khalid MS, Farooq M, Fatima M, et al. 2019. Fish-pak: fish species dataset from pakistan for visual features based classification. Data in Brief, 27: 104565. doi: 10.1016/j.dib.2019.104565
    Spampinato C, Chen-Burger YH, Nadarajan G, Fisher RB. 2008. Detecting, tracking and counting fish in low quality unconstrained underwater videos. In: Proceedings of the 3rd International Conference on Computer Vision Theory and Applications. Madeira: DBLP, 514–519.
    Spampinato C, Giordano D, Di Salvo R, Chen-Burger YHJ, Fisher RB, Nadarajan G. 2010. Automatic fish classification for underwater species behavior understanding. In: Proceedings of the 1st ACM International Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams. Firenze: ACM, 45–50.
    Vo AV, Truong-Hong L, Laefer DF, Bertolotto M. 2015. Octree-based region growing for point cloud segmentation. ISPRS Journal of Photogrammetry and Remote Sensing, 104: 88−100. doi: 10.1016/j.isprsjprs.2015.01.011
    Wang G A, Hwang JN, Wallace F, Rose C. 2019. Multi-scale fish segmentation refinement and missing shape recovery. IEEE Access, 7: 52836−52845. doi: 10.1109/ACCESS.2019.2912612
    Wang ZL, Walsh KB, Verma B. 2017. On-tree mango fruit size estimation using RGB-D images. Sensors, 17(12): 2738.
    Wu YX, Li F, Liu FF, Cheng LN, Guo LL. 2016. Point cloud segmentation using Euclidean cluster extraction algorithm with the Smoothness. Meas Control Technol, 35(3): 36−38. (in Chinese)
    Zermas D, Morellas V, Mulla D, Papanikolopoulos N. 2020. 3D model processing for high throughput phenotype extraction–the case of corn. Computers and Electronics in Agriculture, 172: 105047. doi: 10.1016/j.compag.2019.105047
  • ZR-2021-141 Supplementary Tables and Figures.pdf
  • 加载中


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索


    Article Metrics

    Article views (777) PDF downloads(186) Cited by()
    Proportional views


    DownLoad:  Full-Size Img  PowerPoint