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利用颜色信息进行皮肤镜图像黑色素瘤的计算机辅助诊断:一项回顾性调查与批判性分析

Incorporating Colour Information for Computer-Aided Diagnosis of Melanoma from Dermoscopy Images: A Retrospective Survey and Critical Analysis.

作者信息

Madooei Ali, Drew Mark S

机构信息

School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.

出版信息

Int J Biomed Imaging. 2016;2016:4868305. doi: 10.1155/2016/4868305. Epub 2016 Dec 19.

Abstract

Cutaneous melanoma is the most life-threatening form of skin cancer. Although advanced melanoma is often considered as incurable, if detected and excised early, the prognosis is promising. Today, clinicians use computer vision in an increasing number of applications to aid early detection of melanoma through dermatological image analysis (dermoscopy images, in particular). Colour assessment is essential for the clinical diagnosis of skin cancers. Due to this diagnostic importance, many studies have either focused on or employed colour features as a constituent part of their skin lesion analysis systems. These studies range from using low-level colour features, such as simple statistical measures of colours occurring in the lesion, to availing themselves of high-level semantic features such as the presence of blue-white veil, globules, or colour variegation in the lesion. This paper provides a retrospective survey and critical analysis of contributions in this research direction.

摘要

皮肤黑色素瘤是最具生命威胁的皮肤癌形式。尽管晚期黑色素瘤通常被认为无法治愈,但如果早期发现并切除,预后很有希望。如今,临床医生在越来越多的应用中使用计算机视觉,通过皮肤病图像分析(特别是皮肤镜图像)来辅助黑色素瘤的早期检测。颜色评估对于皮肤癌的临床诊断至关重要。由于这种诊断重要性,许多研究要么专注于颜色特征,要么将其作为皮肤病变分析系统的组成部分加以应用。这些研究范围从使用低级颜色特征,如病变中出现颜色的简单统计测量,到利用高级语义特征,如病变中蓝白色面纱、小球或颜色斑驳的存在。本文对这一研究方向的贡献进行了回顾性调查和批判性分析。

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本文引用的文献

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Fuzzy logic color detection: Blue areas in melanoma dermoscopy images.模糊逻辑颜色检测:黑色素瘤皮肤镜图像中的蓝色区域。
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