Fatima Ghazi, A. Benkuider, F. Ayoub, Khalil Ibrahimi
{"title":"Selection of the Discriming Feature Using the BEMD’s BIMF for Classification of Breast Cancer Mammography Image","authors":"Fatima Ghazi, A. Benkuider, F. Ayoub, Khalil Ibrahimi","doi":"10.3390/biomedinformatics4020066","DOIUrl":null,"url":null,"abstract":"Mammogram exam images are useful in identifying diseases, such as breast cancer, which is one of the deadliest cancers, affecting adult women around the world. Computational image analysis and machine learning techniques can help experts identify abnormalities in these images. In this work we present a new system to help diagnose and analyze breast mammogram images. To do this, the system a method the Selection of the Most Discriminant Attributes of the images preprocessed by BEMD “SMDA-BEMD”, this entails picking the most pertinent traits from the collection of variables that characterize the state under study. A reduction of attribute based on a transformation of the data also called an extraction of characteristics by extracting the Haralick attributes from the Co-occurrence Matrices Methods “GLCM” this reduction which consists of replacing the initial set of data by a new reduced set, constructed at from the initial set of features extracted by images decomposed using Bidimensional Empirical Multimodal Decomposition “BEMD”, for discrimination of breast mammogram images (healthy and pathology) using BEMD. This decomposition makes it possible to decompose an image into several Bidimensional Intrinsic Mode Functions “BIMFs” modes and a residue. The results obtained show that mammographic images can be represented in a relatively short space by selecting the most discriminating features based on a supervised method where they can be differentiated with high reliability between healthy mammographic images and pathologies, However, certain aspects and findings demonstrate how successful the suggested strategy is to detect the tumor. A BEMD technique is used as preprocessing on mammographic images. This suggested methodology makes it possible to obtain consistent results and establishes the discrimination threshold for mammography images (healthy and pathological), the classification rate is improved (98.6%) compared to existing cutting-edge techniques in the field. This approach is tested and validated on mammographic medical images from the Kenitra-Morocco reproductive health reference center (CRSRKM) which contains breast mammographic images of normal and pathological cases.","PeriodicalId":72394,"journal":{"name":"BioMedInformatics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BioMedInformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/biomedinformatics4020066","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Mammogram exam images are useful in identifying diseases, such as breast cancer, which is one of the deadliest cancers, affecting adult women around the world. Computational image analysis and machine learning techniques can help experts identify abnormalities in these images. In this work we present a new system to help diagnose and analyze breast mammogram images. To do this, the system a method the Selection of the Most Discriminant Attributes of the images preprocessed by BEMD “SMDA-BEMD”, this entails picking the most pertinent traits from the collection of variables that characterize the state under study. A reduction of attribute based on a transformation of the data also called an extraction of characteristics by extracting the Haralick attributes from the Co-occurrence Matrices Methods “GLCM” this reduction which consists of replacing the initial set of data by a new reduced set, constructed at from the initial set of features extracted by images decomposed using Bidimensional Empirical Multimodal Decomposition “BEMD”, for discrimination of breast mammogram images (healthy and pathology) using BEMD. This decomposition makes it possible to decompose an image into several Bidimensional Intrinsic Mode Functions “BIMFs” modes and a residue. The results obtained show that mammographic images can be represented in a relatively short space by selecting the most discriminating features based on a supervised method where they can be differentiated with high reliability between healthy mammographic images and pathologies, However, certain aspects and findings demonstrate how successful the suggested strategy is to detect the tumor. A BEMD technique is used as preprocessing on mammographic images. This suggested methodology makes it possible to obtain consistent results and establishes the discrimination threshold for mammography images (healthy and pathological), the classification rate is improved (98.6%) compared to existing cutting-edge techniques in the field. This approach is tested and validated on mammographic medical images from the Kenitra-Morocco reproductive health reference center (CRSRKM) which contains breast mammographic images of normal and pathological cases.
乳房 X 光检查图像有助于识别疾病,如乳腺癌,这是影响全球成年女性的最致命癌症之一。计算图像分析和机器学习技术可以帮助专家识别这些图像中的异常。在这项工作中,我们提出了一个帮助诊断和分析乳房 X 光图像的新系统。为此,该系统采用了一种方法,即从经过 BEMD "SMDA-BEMD "预处理的图像中选择最具鉴别力的属性,这就需要从描述所研究状态的变量集合中挑选出最相关的特征。通过共现矩阵方法(GLCM)提取 Haralick 属性,这种基于数据转换的属性还原也称为特征提取,这种还原包括将初始数据集替换为新的还原集,新还原集由使用双维经验多模态分解法(BEMD)对图像进行分解后提取的初始特征集构建而成,用于使用双维经验多模态分解法(BEMD)对乳腺 X 光图像(健康和病理)进行判别。这种分解方法可将图像分解为多个双维本征模式函数(Bidimensional Intrinsic Mode Functions "BIMFs")模式和一个残差。研究结果表明,乳腺图像可以在相对较短的空间内通过选择最具辨别力的特征来表示,这种基于监督的方法可以在健康乳腺图像和病理图像之间进行高可靠性的区分。BEMD 技术用于乳腺 X 射线图像的预处理。与该领域现有的尖端技术相比,该方法提高了分类率(98.6%)。该方法在来自凯尼特拉-摩洛哥生殖健康参考中心(CRSRKM)的乳腺 X 射线医学影像上进行了测试和验证,其中包含正常和病理病例的乳腺 X 射线图像。