{"title":"基于多尺度分解的皮肤损伤分析改进方法","authors":"Y. Filali, M. A. Sabri, A. Aarab","doi":"10.1109/EITECH.2017.8255250","DOIUrl":null,"url":null,"abstract":"Skin cancer is one of the most common types of cancer in the white populations and the incidence of skin cancer has reached epidemic proportions. This paper proposes a new approach for automatic segmentation and classification for skin lesion. The segmentation is based on a pre-processing using the color structure texture image decomposition. Geometrical component is used in the lesion segmentation and the texture component is used to extract the lesion texture features. Feature classification is performed using the Support Vector Machine (SVM) classifier. The efficiency and the performance of the proposed approach are evaluated in comparison with recent and robust dermoscopic approaches from literature.","PeriodicalId":447139,"journal":{"name":"2017 International Conference on Electrical and Information Technologies (ICEIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"An improved approach for skin lesion analysis based on multiscale decomposition\",\"authors\":\"Y. Filali, M. A. Sabri, A. Aarab\",\"doi\":\"10.1109/EITECH.2017.8255250\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Skin cancer is one of the most common types of cancer in the white populations and the incidence of skin cancer has reached epidemic proportions. This paper proposes a new approach for automatic segmentation and classification for skin lesion. The segmentation is based on a pre-processing using the color structure texture image decomposition. Geometrical component is used in the lesion segmentation and the texture component is used to extract the lesion texture features. Feature classification is performed using the Support Vector Machine (SVM) classifier. The efficiency and the performance of the proposed approach are evaluated in comparison with recent and robust dermoscopic approaches from literature.\",\"PeriodicalId\":447139,\"journal\":{\"name\":\"2017 International Conference on Electrical and Information Technologies (ICEIT)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Conference on Electrical and Information Technologies (ICEIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EITECH.2017.8255250\",\"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 Electrical and Information Technologies (ICEIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EITECH.2017.8255250","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An improved approach for skin lesion analysis based on multiscale decomposition
Skin cancer is one of the most common types of cancer in the white populations and the incidence of skin cancer has reached epidemic proportions. This paper proposes a new approach for automatic segmentation and classification for skin lesion. The segmentation is based on a pre-processing using the color structure texture image decomposition. Geometrical component is used in the lesion segmentation and the texture component is used to extract the lesion texture features. Feature classification is performed using the Support Vector Machine (SVM) classifier. The efficiency and the performance of the proposed approach are evaluated in comparison with recent and robust dermoscopic approaches from literature.