Digital hair segmentation using hybrid convolutional and recurrent neural networks architecture
作者: Mohamed AttiaMohammed HossnyHailing ZhouSaeid NahavandiHamed AsadiAnousha Yazdabadi
作者单位: 1Institute for Intelligent Systems Research and Innovation, Deakin University, Australia
2School of Medicine, Melbourne University, Australia
3School of Medicine, Deakin University, Australia
刊名: Computer Methods and Programs in Biomedicine, 2019, Vol.177 , pp.17-30
来源数据库: Elsevier Journal
DOI: 10.1016/j.cmpb.2019.05.010
关键词: DermatologyHair detectionHair segmentationDeep learning
原始语种摘要: Abstract(#br)Background and Objective(#br)Skin melanoma is one of the major health problems in many countries. Dermatologists usually diagnose melanoma by visual inspection of moles. Digital hair removal can provide a non-invasive way to remove hair and hair-like regions as a pre-processing step for skin lesion images. Hair removal has two main steps: hair segmentation and hair gaps inpainting. However, hair segmentation is a challenging task which requires manual tuning of thresholding parameters. Hard-coded threshold leads to over-segmentation (false positives) which in return changes the textural integrity of lesions and or under-segmentation (false negatives) which leaves hair traces and artefacts which affect subsequent diagnosis. Additionally, dermal hair exhibits different...
全文获取路径: Elsevier  (合作)
影响因子:1.555 (2012)

  • segmentation 分段
  • convolutional 卷积
  • architecture 构造
  • negatives 负数
  • neural 神经系统的
  • thresholding 阈值
  • texture 结构
  • encode 编成密码
  • pattern 模型
  • features 特征