基于深度可分离卷积网络的皮肤镜图像病灶分割方法

崔文成; 张鹏霞; 邵虹

doi:10.11959/j.issn.2096-6652.202041

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基于深度可分离卷积网络的皮肤镜图像病灶分割方法

专刊：深度强化学习 | 更新时间：2024-06-05

- 基于深度可分离卷积网络的皮肤镜图像病灶分割方法
- Dermoscopic image lesion segmentation method based on deep separable convolutional network
- 智能科学与技术学报 2020年2卷第4期页码：385-393
- 作者机构：
- 作者简介：
  
  [ "崔文成（1973- ），男，沈阳工业大学信息科学与工程学院副教授，主要研究方向为智能信息处理。" ]
  [ "张鹏霞（1996- ），女，沈阳工业大学信息科学与工程学院硕士生，主要研究方向为智能信息处理。" ]
  [ "邵虹（1974- ），女，博士，沈阳工业大学信息科学与工程学院教授，主要研究方向为图像处理与模式识别、智能信息处理。" ]
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202041
  中图分类号： TP391.4
- 网络出版日期：2020-12，
  
  纸质出版日期：2020-12-15
- 稿件说明：
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崔文成, 张鹏霞, 邵虹. 基于深度可分离卷积网络的皮肤镜图像病灶分割方法[J]. 智能科学与技术学报, 2020,2(4):385-393.

Wencheng CUI, Pengxia ZHANG, Hong SHAO. Dermoscopic image lesion segmentation method based on deep separable convolutional network[J]. Chinese journal of intelligent science and technology, 2020, 2(4): 385-393.
崔文成, 张鹏霞, 邵虹. 基于深度可分离卷积网络的皮肤镜图像病灶分割方法[J]. 智能科学与技术学报, 2020,2(4):385-393. DOI： 10.11959/j.issn.2096-6652.202041.

Wencheng CUI, Pengxia ZHANG, Hong SHAO. Dermoscopic image lesion segmentation method based on deep separable convolutional network[J]. Chinese journal of intelligent science and technology, 2020, 2(4): 385-393. DOI： 10.11959/j.issn.2096-6652.202041.

摘要

针对皮肤镜图像病灶难定位、病灶精准分割难以实现的问题，提出一种基于深度可分离卷积网络的皮肤镜图像病灶分割方法。首先对皮肤镜图像进行黑框移除和毛发移除处理，将图像中有碍确定病灶位置的人工噪声、天然噪声移除；然后在降噪处理的基础上，对图像进行形变、旋转，以扩充数据集；最后构建基于深度可分离卷积、空洞卷积的编解码分割模型，编码部分对图像进行特征提取，解码部分融合特征图，并对图像细节特征进行恢复。实验结果表明，该方法针对皮肤镜图像病灶分割问题可取得较好的分割效果，分割病灶的准确率达到95.24%，与分割模型U-Net相比，准确度提高了6.17%。

Abstract

Aiming at the problem of the difficulty in locating the lesions in dermoscopic images and achieving precise segmentation of the lesions

a method of lesion segmentation in dermatological images based on deep separable convolutional network was proposed.Firstly

perform the black frame removal and hair removal processing on the dermoscopic image to remove the artificial and natural noise that hinders the location of the lesion in the image.Then the image after the noise reduction process was deformed and rotated to expand the data set.Finally

a encoder-decoder segmentation model based on depth separable convolution and hole convolution was constructed.The coding part extracts the features of the image

and the decoding part fuses the feature maps and restores the image details.Experimental results show that this method can achieve better segmentation results for the problem of skin disease image lesion segmentation.The accuracy of segmenting lesions reaches 95.24%.Compared with the segmentation model U-Net

the accuracy is improved by 6.17%.

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references

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