H. A. Nugroho, Rezty Amalia Aras, T. Lestari, I. Ardiyanto
{"title":"基于Frangi滤波和形态重建的视网膜血管分割","authors":"H. A. Nugroho, Rezty Amalia Aras, T. Lestari, I. Ardiyanto","doi":"10.1109/ICCEREC.2017.8226686","DOIUrl":null,"url":null,"abstract":"The analysis of structural changes in retinal vessels is the most important part for diagnosing and detecting retinal related diseases such as diabetic retinopathy, hypertension, age-related macular degeneration (AMD) and arteriosclerotic. This paper presents a method for segmenting retinal vessels in retinal fundus image based on Frangi filter and morphological reconstruction. The proposed method is evaluated using colour fundus images from DRIVE and STARE datasets. In DRIVE dataset, the performance of proposed method achieves an average of sensitivity, specificity and accuracy at 72.13%, 96.65 % and 94.50%, respectively. Meanwhile, in STARE dataset, it achieves an average sensitivity of 75.50%, specificity of 90.38% and accuracy of 88.76%. These results indicate that the proposed method successfully segments retinal vessels in fundus images.","PeriodicalId":328054,"journal":{"name":"2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Retinal vessel segmentation based on Frangi filter and morphological reconstruction\",\"authors\":\"H. A. Nugroho, Rezty Amalia Aras, T. Lestari, I. Ardiyanto\",\"doi\":\"10.1109/ICCEREC.2017.8226686\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The analysis of structural changes in retinal vessels is the most important part for diagnosing and detecting retinal related diseases such as diabetic retinopathy, hypertension, age-related macular degeneration (AMD) and arteriosclerotic. This paper presents a method for segmenting retinal vessels in retinal fundus image based on Frangi filter and morphological reconstruction. The proposed method is evaluated using colour fundus images from DRIVE and STARE datasets. In DRIVE dataset, the performance of proposed method achieves an average of sensitivity, specificity and accuracy at 72.13%, 96.65 % and 94.50%, respectively. Meanwhile, in STARE dataset, it achieves an average sensitivity of 75.50%, specificity of 90.38% and accuracy of 88.76%. These results indicate that the proposed method successfully segments retinal vessels in fundus images.\",\"PeriodicalId\":328054,\"journal\":{\"name\":\"2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC)\",\"volume\":\"32 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCEREC.2017.8226686\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCEREC.2017.8226686","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Retinal vessel segmentation based on Frangi filter and morphological reconstruction
The analysis of structural changes in retinal vessels is the most important part for diagnosing and detecting retinal related diseases such as diabetic retinopathy, hypertension, age-related macular degeneration (AMD) and arteriosclerotic. This paper presents a method for segmenting retinal vessels in retinal fundus image based on Frangi filter and morphological reconstruction. The proposed method is evaluated using colour fundus images from DRIVE and STARE datasets. In DRIVE dataset, the performance of proposed method achieves an average of sensitivity, specificity and accuracy at 72.13%, 96.65 % and 94.50%, respectively. Meanwhile, in STARE dataset, it achieves an average sensitivity of 75.50%, specificity of 90.38% and accuracy of 88.76%. These results indicate that the proposed method successfully segments retinal vessels in fundus images.