Christian S. Bowman, Ryan Traband, Xuesong Wang, Sara P. Knowles, Sassoum Lo, Zhenyu Jia, Nicholi Vorsa, Ira A. Herniter
{"title":"多叶样品提取系统(MuLES):一个工具,以提高自动化形态测量叶片研究","authors":"Christian S. Bowman, Ryan Traband, Xuesong Wang, Sara P. Knowles, Sassoum Lo, Zhenyu Jia, Nicholi Vorsa, Ira A. Herniter","doi":"10.1002/aps3.11513","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Premise</h3>\n \n <p>The measurement of leaf morphometric parameters from digital images can be time-consuming or restrictive when using digital image analysis softwares. The Multiple Leaf Sample Extraction System (MuLES) is a new tool that enables high-throughput leaf shape analysis with minimal user input or prerequisites, such as coding knowledge or image modification.</p>\n </section>\n \n <section>\n \n <h3> Methods and Results</h3>\n \n <p>MuLES uses contrasting pixel color values to distinguish between leaf objects and their background area, eliminating the need for color threshold–based methods or color correction cards typically required in other software methods. The leaf morphometric parameters measured by this software, especially leaf aspect ratio, were able to distinguish between large populations of different accessions for the same species in a high-throughput manner.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>MuLES provides a simple method for the rapid measurement of leaf morphometric parameters in large plant populations from digital images and demonstrates the ability of leaf aspect ratio to distinguish between closely related plant types.</p>\n </section>\n </div>","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/8d/19/APS3-11-e11513.PMC10083438.pdf","citationCount":"1","resultStr":"{\"title\":\"Multiple Leaf Sample Extraction System (MuLES): A tool to improve automated morphometric leaf studies\",\"authors\":\"Christian S. Bowman, Ryan Traband, Xuesong Wang, Sara P. Knowles, Sassoum Lo, Zhenyu Jia, Nicholi Vorsa, Ira A. Herniter\",\"doi\":\"10.1002/aps3.11513\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Premise</h3>\\n \\n <p>The measurement of leaf morphometric parameters from digital images can be time-consuming or restrictive when using digital image analysis softwares. The Multiple Leaf Sample Extraction System (MuLES) is a new tool that enables high-throughput leaf shape analysis with minimal user input or prerequisites, such as coding knowledge or image modification.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods and Results</h3>\\n \\n <p>MuLES uses contrasting pixel color values to distinguish between leaf objects and their background area, eliminating the need for color threshold–based methods or color correction cards typically required in other software methods. The leaf morphometric parameters measured by this software, especially leaf aspect ratio, were able to distinguish between large populations of different accessions for the same species in a high-throughput manner.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>MuLES provides a simple method for the rapid measurement of leaf morphometric parameters in large plant populations from digital images and demonstrates the ability of leaf aspect ratio to distinguish between closely related plant types.</p>\\n </section>\\n </div>\",\"PeriodicalId\":2,\"journal\":{\"name\":\"ACS Applied Bio Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2023-03-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/8d/19/APS3-11-e11513.PMC10083438.pdf\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Bio Materials\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/aps3.11513\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, BIOMATERIALS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"99","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/aps3.11513","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
Multiple Leaf Sample Extraction System (MuLES): A tool to improve automated morphometric leaf studies
Premise
The measurement of leaf morphometric parameters from digital images can be time-consuming or restrictive when using digital image analysis softwares. The Multiple Leaf Sample Extraction System (MuLES) is a new tool that enables high-throughput leaf shape analysis with minimal user input or prerequisites, such as coding knowledge or image modification.
Methods and Results
MuLES uses contrasting pixel color values to distinguish between leaf objects and their background area, eliminating the need for color threshold–based methods or color correction cards typically required in other software methods. The leaf morphometric parameters measured by this software, especially leaf aspect ratio, were able to distinguish between large populations of different accessions for the same species in a high-throughput manner.
Conclusions
MuLES provides a simple method for the rapid measurement of leaf morphometric parameters in large plant populations from digital images and demonstrates the ability of leaf aspect ratio to distinguish between closely related plant types.