DeepEar: A Deep Convolutional Network without Deformation for Ear Segmentation

Q3 Computer Science

中国图象图形学报 Pub Date : 2023-09-01 DOI:10.18178/joig.11.3.242-247

Yuhan Chen, Wende Ke, Qingfeng Li, Dongxin Lu, Yani Bai, Zhen Wang

{"title":"DeepEar: A Deep Convolutional Network without Deformation for Ear Segmentation","authors":"Yuhan Chen, Wende Ke, Qingfeng Li, Dongxin Lu, Yani Bai, Zhen Wang","doi":"10.18178/joig.11.3.242-247","DOIUrl":null,"url":null,"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.","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"中国图象图形学报","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.18178/joig.11.3.242-247","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Computer Science","Score":null,"Total":0}

引用次数: 0

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.

查看原文本刊更多论文

DeepEar:一种无变形的深度卷积耳分割网络

随着机器人技术在各个领域的交叉应用，机器视觉逐渐受到重视。图像分割作为机器视觉的重要组成部分，在生物医学图像分割中得到了广泛的应用，近年来提出了许多用于图像分割的算法。如今，中医逐渐受到重视，耳科诊断在中医中占有重要地位，耳科诊断自动化的需求也逐渐强烈。本文提出了一种深度卷积耳分割网络(DeepEar)，该网络将空间金字塔块与编码器-解码器结构相结合，并在整个网络中应用了非均匀卷积层。值得注意的是，DeepEar输出的耳朵图像与输入图像具有相同的大小。实验表明，本文提出的DeepEar算法具有较强的耳区分割能力，能够得到完整的耳区，多余区域较少。该网络的分割结果精度为0.9915，精度为0.9762,Recal为9.9723，谐波测度为0.9738，特异性为0.9955，在定量评价方面明显优于其他基于卷积神经网络(CNN)的方法。此外，本文所提出的网络基本完成了耳甲分割，进一步验证了所提出网络的能力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

中国图象图形学报 Computer Science-Computer Graphics and Computer-Aided Design

CiteScore

1.20

自引率

0.00%

发文量

6776

期刊介绍： Journal of Image and Graphics (ISSN 1006-8961, CN 11-3758/TB, CODEN ZTTXFZ) is an authoritative academic journal supervised by the Chinese Academy of Sciences and co-sponsored by the Institute of Space and Astronautical Information Innovation of the Chinese Academy of Sciences (ISIAS), the Chinese Society of Image and Graphics (CSIG), and the Beijing Institute of Applied Physics and Computational Mathematics (BIAPM). The journal integrates high-tech theories, technical methods and industrialisation of applied research results in computer image graphics, and mainly publishes innovative and high-level scientific research papers on basic and applied research in image graphics science and its closely related fields. The form of papers includes reviews, technical reports, project progress, academic news, new technology reviews, new product introduction and industrialisation research. The content covers a wide range of fields such as image analysis and recognition, image understanding and computer vision, computer graphics, virtual reality and augmented reality, system simulation, animation, etc., and theme columns are opened according to the research hotspots and cutting-edge topics. Journal of Image and Graphics reaches a wide range of readers, including scientific and technical personnel, enterprise supervisors, and postgraduates and college students of colleges and universities engaged in the fields of national defence, military, aviation, aerospace, communications, electronics, automotive, agriculture, meteorology, environmental protection, remote sensing, mapping, oil field, construction, transportation, finance, telecommunications, education, medical care, film and television, and art. Journal of Image and Graphics is included in many important domestic and international scientific literature database systems, including EBSCO database in the United States, JST database in Japan, Scopus database in the Netherlands, China Science and Technology Thesis Statistics and Analysis (Annual Research Report), China Science Citation Database (CSCD), China Academic Journal Network Publishing Database (CAJD), and China Academic Journal Network Publishing Database (CAJD). China Science Citation Database (CSCD), China Academic Journals Network Publishing Database (CAJD), China Academic Journal Abstracts, Chinese Science Abstracts (Series A), China Electronic Science Abstracts, Chinese Core Journals Abstracts, Chinese Academic Journals on CD-ROM, and China Academic Journals Comprehensive Evaluation Database.