{"title":"提取神经突结构用于高通量成像筛选基于神经元的检测","authors":"Yong Zhang, Xiaobo Zhou, Stephen T. C. Wong","doi":"10.1109/LSSA.2006.250391","DOIUrl":null,"url":null,"abstract":"Neuron image analysis has recently emerged as a critical component for enabling quantitative systems neurobiology and high throughput drug screening. In this paper, we present a new algorithm for fast and automatic extraction of neurite structures in microscopy neuron images. The algorithm is based on novel methods for soma segmentation, seed point detection, recursive center line detection, and 2D curve smoothing. The algorithm is fully automatic without any human interaction while robust enough for processing images of poor quality, e.g., low contrast or low signal-to-noise ratio. It can be used to extract accurately highly complex neurite structures. All these advantages make the proposed algorithm suitable for increasingly demanding and complex image analysis tasks in systems biology and drug screening","PeriodicalId":360097,"journal":{"name":"2006 IEEE/NLM Life Science Systems and Applications Workshop","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Extraction of Neurite Structures for High Throughput Imaging Screening of Neuron Based Assays\",\"authors\":\"Yong Zhang, Xiaobo Zhou, Stephen T. C. Wong\",\"doi\":\"10.1109/LSSA.2006.250391\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Neuron image analysis has recently emerged as a critical component for enabling quantitative systems neurobiology and high throughput drug screening. In this paper, we present a new algorithm for fast and automatic extraction of neurite structures in microscopy neuron images. The algorithm is based on novel methods for soma segmentation, seed point detection, recursive center line detection, and 2D curve smoothing. The algorithm is fully automatic without any human interaction while robust enough for processing images of poor quality, e.g., low contrast or low signal-to-noise ratio. It can be used to extract accurately highly complex neurite structures. All these advantages make the proposed algorithm suitable for increasingly demanding and complex image analysis tasks in systems biology and drug screening\",\"PeriodicalId\":360097,\"journal\":{\"name\":\"2006 IEEE/NLM Life Science Systems and Applications Workshop\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 IEEE/NLM Life Science Systems and Applications Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/LSSA.2006.250391\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE/NLM Life Science Systems and Applications Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/LSSA.2006.250391","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Extraction of Neurite Structures for High Throughput Imaging Screening of Neuron Based Assays
Neuron image analysis has recently emerged as a critical component for enabling quantitative systems neurobiology and high throughput drug screening. In this paper, we present a new algorithm for fast and automatic extraction of neurite structures in microscopy neuron images. The algorithm is based on novel methods for soma segmentation, seed point detection, recursive center line detection, and 2D curve smoothing. The algorithm is fully automatic without any human interaction while robust enough for processing images of poor quality, e.g., low contrast or low signal-to-noise ratio. It can be used to extract accurately highly complex neurite structures. All these advantages make the proposed algorithm suitable for increasingly demanding and complex image analysis tasks in systems biology and drug screening