DeepEar: A Deep Convolutional Network without Deformation for Ear Segmentation

Abstract

With the cross-application of robotics in various fields, machine vision has gradually received attention. As an important part in machine vision, image segmentation has been widely applied especially in biomedical image segmentation, and many algorithms in image segmentation have been proposed in recent years. Nowadays, traditional Chinese medicine gradually received attention and ear diagnosis plays an important role in traditional Chinese medicine, the demand for automation in ear diagnosis becomes gradually intense. This paper proposed a deep convolution network for ear segmentation (DeepEar), which combined spatial pyramid block and the encoder-decoder architecture, besides, atrous convolutional layers are applied throughout the network. Noteworthy, the output ear image from DeepEar has the same size as input images. Experiments shows that this paper proposed DeepEar has great capability in ear segmentation and obtained complete ear with less excess region. Segmentation results from the proposed network obtained Accuracy = 0.9915, Precision = 0.9762, Recal l= 9.9723, Harmonic measure = 0.9738 and Specificity = 0.9955, which performed much better than other Convolution Neural Network (CNN)- based methods in quantitative evaluation. Besides, this paper proposed network basically completed ear-armor segmentation, further validated the capability of the proposed network.