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A Deep Learning Based Approach to Skin Lesion Border Extraction With a Novel Edge Detector in Dermoscopy Images

机译：一种深入的基于Dermicocy intermopt inded探测器的皮肤病变边界提取方法

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Abder-Rahman Ali; Jingpeng Li; Sally Jane OShea; Guang Yang; Thomas Trappenberg; Xujiong Ye;
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1. Accurate Border Detection of Skin Lesions in Skin Cancer Images using Log Edge Detector [J] . J. H. Jaseema Yasmin, M. Mohamed Sathik International Journal of Applied Engineering Research . 2017,第9aPta1期

机译：使用Log Edge探测器的皮肤癌图像中皮肤病变的准确边框检测
2. Analysis of density based and fuzzy c-means clustering methods on lesion border extraction in dermoscopy images [J] . Sinan Kockara, Mutlu Mete, Bernard Chen, BMC Bioinformatics . 2010,第SUPPLEMENTa6期

机译：基于密度的模糊c均值聚类方法在皮肤镜图像病变边界提取中的应用
3. A basis function feature-based approach for skin lesion discrimination in dermatology dermoscopy images. [J] . Stanley RJ, Stoecker WV, Moss RH, Skin research and technology: official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) . 2008,第4期

机译：一种基于基本功能特征的皮肤病学皮肤镜检查图像中皮肤病变鉴别方法。
4. A Deep Learning Based Approach to Skin Lesion Border Extraction With a Novel Edge Detector in Dermoscopy Images [C] . Abder-Rahman Ali, Jingpeng Li, Sally Jane O’Shea, International Joint Conference on Neural Networks . 2019

机译：基于深度学习的皮肤镜图像中新型边缘检测器的皮肤病变边界提取方法
5. Automatic detection of lesion border and edge-related structures in dermoscopy images. [D] . Chen, Xiaohe. 2007

机译：在皮肤镜检查图像中自动检测病变边界和边缘相关结构。
6. Analysis of density based and fuzzy c-means clustering methods on lesion border extraction in dermoscopy images [O] . Sinan Kockara, Mutlu Mete, Bernard Chen, 2010

机译：基于密度的模糊c均值聚类方法在皮肤镜图像病变边界提取中的应用
7. Figure 3: Proposed approach: (A) The skin lesion image is firstly converted to grayscale, after which the skin lesion is segmented and smoothed, lesion border (edge) detected, and the lesion irregularity measure derived. (B) A CNN is trained on the smoothed segmented image, skin lesion border, and the skin lesion irregularity measure. (C) A Gaussian naive Bayes is trained on the irregularity measure. (D) The generated models are used to predict class probabilities, and a threshold is eventually used to determine the final decision (regular or irregular skin lesion border). [O] . -1

机译：图3：所提出的方法：（a）将皮肤病变图像首先转化为灰度，之后将皮肤病变分段和平滑，检测到病变边界（边缘），并且衍生病变不规则性测量。（b）CNN在平滑的分段图像，皮肤病变边界和皮肤病原不规则测量上培训。（c）高斯天真贝叶斯在不规则措施上培训。（d）所生成的模型用于预测类概率，最终最终用于确定最终决定（常规或不规则的皮肤病变边界）。

A Deep Learning Based Approach to Skin Lesion Border Extraction With a Novel Edge Detector in Dermoscopy Images

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