基于多尺度密集块网络的皮肤病变图像分割算法

Skin Lesion Image Segmentation Algorithm Based on Multi-Scale DenseNet

针对皮肤病变图像分割问题,提出了一种基于多尺度密集块网络(DenseNet)的皮肤病变图像分割算法。首先依次采用形态学闭操作和非锐化滤波器对原始皮肤病变图像进行预处理,得到不含皮肤毛发和血管伪影的细化图像。然后将预处理后的图像输入到分割网络中。该网络基于编码-解码(Encoder-Decoder)架构,运用并行多分支结构和金字塔池化模型两种多尺度特征融合方法,可实现不同感受野下的特征提取。同时,将DenseNet结构融合到编码器中,实现图像特征的复用,利用目标损失与内容损失相结合的LTotal损失函数,进一步提升了图像分割的精度。最后,通过SoftMax分类器得到分割结果并计算相关评估指数。ISBI 2016皮肤病变图像数据集上的实验结果显示,所提算法的逐像素分割精度为95.48%,Dice系数为96.37%,Jaccard指数为93.41%,灵敏度为92.93%,特异性为96.49%,总体性能优于现有算法。所提算法可精确分割皮肤病变区域,能够应用于黑色素瘤计算机辅助诊断系统。

Aiming at the problem of skin lesion image segmentation,a skin lesion image segmentation is proposed based on multi-scale DenseNet.First,the morphological closing operation and the un-sharp filter are used to preprocess the original skin lesion image and to obtain a refinement image without hairs and blood-vessel artifacts.Then,the pre-processed image is input into a segmentation network.This network is based on an encoder-decoder architecture and uses two multi-scale feature fusion methods of parallel multi-branch structure and pyramid pooling block model to achieve feature extraction under different receptive fields.Furthermore,the DenseNet structure is integrated into the encoder to realize the reuse of image features,and the LTotal loss function which combines target loss and content loss is adopted to further improve the accuracy of image segmentation.Finally,the segmentation results are obtained through the SoftMax classifier and the related evaluation indicators are calculated.The experimental results on the ISBI 2016 skin lesion image dataset show that the accuracy,Dice coefficient,Jaccard index,sensitivity,and specificity are 95.48%,96.37%,93.41%,92.93%,and 96.49%,respectively,and the whole performance here is better than those of the existing algorithms.The proposed algorithm can accurately segment skin lesions and thus it can be applied to the melanoma computer-aided diagnosis systems.