{"title":"使用局部七层模式的乳腺密度分类:多分辨率和多拓扑方法","authors":"Andrik Rampun, B. Scotney, P. Morrow, Haibo Wang","doi":"10.1109/CBMS.2019.00133","DOIUrl":null,"url":null,"abstract":"We present an extension of our previous work in [1] by investigating the use of Local Septenary Patterns (LSP) for breast density classification in mammograms. The LSP operator is a variant of Local Binary Patterns (LBP) inspired by Local Ternary Patterns (LTP) and Local Quinary patterns (LQP). The main extensions in our work are i) we investigate the use of a multi-resolution technique when extracting micro texture information, ii) we investigate different neighbourhood topologies as different ways of extracting texture features, and iii) we use an additional dataset called InBreast as well as the most popular dataset in the literature, which is the Mammographic Image Analysis Society (MIAS) to further evaluate the performance of the LSP operator.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Breast Density Classification using Local Septenary Patterns: A Multi-resolution and Multi-topology Approach\",\"authors\":\"Andrik Rampun, B. Scotney, P. Morrow, Haibo Wang\",\"doi\":\"10.1109/CBMS.2019.00133\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present an extension of our previous work in [1] by investigating the use of Local Septenary Patterns (LSP) for breast density classification in mammograms. The LSP operator is a variant of Local Binary Patterns (LBP) inspired by Local Ternary Patterns (LTP) and Local Quinary patterns (LQP). The main extensions in our work are i) we investigate the use of a multi-resolution technique when extracting micro texture information, ii) we investigate different neighbourhood topologies as different ways of extracting texture features, and iii) we use an additional dataset called InBreast as well as the most popular dataset in the literature, which is the Mammographic Image Analysis Society (MIAS) to further evaluate the performance of the LSP operator.\",\"PeriodicalId\":311634,\"journal\":{\"name\":\"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CBMS.2019.00133\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBMS.2019.00133","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Breast Density Classification using Local Septenary Patterns: A Multi-resolution and Multi-topology Approach
We present an extension of our previous work in [1] by investigating the use of Local Septenary Patterns (LSP) for breast density classification in mammograms. The LSP operator is a variant of Local Binary Patterns (LBP) inspired by Local Ternary Patterns (LTP) and Local Quinary patterns (LQP). The main extensions in our work are i) we investigate the use of a multi-resolution technique when extracting micro texture information, ii) we investigate different neighbourhood topologies as different ways of extracting texture features, and iii) we use an additional dataset called InBreast as well as the most popular dataset in the literature, which is the Mammographic Image Analysis Society (MIAS) to further evaluate the performance of the LSP operator.