基于神经网络的新疆高发病哈萨克族食管癌医学图像分型研究

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英文篇名：Image Classification of Kazak Esophageal Carcinoma in Xinjiang Based on Neural Network 作者：排孜丽耶·尤山塔依 ; 严传波 ; 木拉提·哈米提 ; 姚娟 ; 阿布都艾尼·库吐鲁克 英文作者：Pazilya Yusantay;Yan Chuanbo;Murat Hamit;Yao Juan;Abdugheni Kutluk;College of Medical Engineering Technology;College of Basic Medicine;Department of Radiology, The First Affiliated Hospital, Xinjiang Medical University; 关键词：灰度共生矩阵 ; 形状不变距法 ; BP神经网络算法 ; 特征提取 ; 图像分类 英文关键词：gray level co-occurrence matrix;;shape invariant distance method;;BP neural network algorithm;;feature extraction;;image classification 中文刊名：KJTB 英文刊名：Bulletin of Science and Technology 机构：新疆医科大学基础医学院;新疆医科大学医学工程技术学院;新疆医科大学第一附属医院影像中心; 出版日期：2019-01-30 出版单位：科技通报 年：2019 期：v.35;No.245 基金：国家自然科学基金(81460281,81560294) 语种：中文; 页：KJTB201901020 页数：7 CN：01 ISSN：33-1079/N 分类号：79-84+89

摘要

目的:探讨BP神经网络算法在新疆高发病哈萨克族食管癌X射线图像纹理特征和形状特征分型中的应用。方法:选取哈萨克族正常食管图像、溃疡型食管癌图像和髓质型食管癌图像各100张,利用灰度共生矩阵算法和形状不变距算法分别提取图像的纹理和形状特征。然后,使用BP神经网络算法构造一个分类器,对正常食管和两种中晚期食管癌图像进行分类研究。结果:共提取了14维哈萨克族食管癌X射线图像纹理和形状特征向量,应用BP神经网络算法进行哈萨克族食管癌X射线图像分类实验,基于灰度共生矩阵算法的纹理特征分类准确率为85.333%,基于Hu不变距算法的形状特征分类准确率为65.333%,而纹理和形状综合特征的分类准确率达到了97.667%。结论:本研究提取基于灰度共生矩阵算法和Hu不变距算法的食管癌图像纹理和形状特征,通过构造BP神经网络分类器对食管癌医学图像进行分型研究。结果表明BP神经网络对综合特征的分类准确率较高,为临床医生诊断食管癌提供了参考,也为后期研发食管癌医学图像计算机辅助诊断系统奠定了基础。
        Object:This paper explore the application of BP neural network algorithm combined with image texture and shape feature extraction in X ray image classification of Kazak esophageal cancer in Xinjiang. Methods:The images of normal esophagus, ulcerative carcinoma of esophagus and medullary esophagus cancer were selected, each of which was 100, then the image texture and shape features are extracted by gray co-occurrence matrix method and shape invariant distance method respectively. After that,using BP neural network algorithm to construct a classifier was used to classify the normal esophagus and two kinds of advanced esophageal carcinoma. Results:A total of 14 dimensional features are extracted, using a single feature algorithm for classification, gray level co-occurrence matrix classification accuracy is85.333%, Hu invariant distance classification accuracy is 65.333%. The comprehensive feature classification accuracy was 97.667%,which is more suitable for classification of normal esophagus and middle and advanced esophageal carcinoma. Conclusion:In this study, the image texture and shape feature extraction algorithm is combined with neural network algorithm to analyze characteristics and typing of normal esophagus, ulcerative and medullary esophageal carcinoma. This method has higher classification accuracy, which lays a foundation for the development of computer aided diagnosis system for esophageal cancer medical images.

引文

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