Classification of Mammographic ROI for Microcalcification Detection Using Multifractal Approach.
Microcalcifications (MCs) are the main signs of precancerous cells. The development of aided-system for their detection has become a challenge for researchers in this field. In this paper, we propose a system for MCs detection based on the multifractal approach that classifies mammographic ROIs into...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 6; pp. 1544 - 1560 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160503254&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160503254 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2022 vid: 35 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 160503254 158058324 160503254 160503254 10.1007/s10278-022-00677-w 160503254 ppf: 1544 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Classification of Mammographic ROI for Microcalcification Detection Using Multifractal Approach. aug: au: Kermouni Serradj, Nadia Messadi, Mahammed Lazzouni, Sihem affil: Biomedical Engineering Laboratory, Faculty of Technology, Abou Bekr Belkaid University, 13000, Tlemcen, Algeria sug: subj: Mammography Classification Calcinosis Radiography Human Breast Neoplasms Symptoms Precancerous Conditions Symptoms Breast Tissue Density Algorithms Contrast Media Image Enhancement Female Sensitivity and Specificity Descriptive Statistics Female ab: Microcalcifications (MCs) are the main signs of precancerous cells. The development of aided-system for their detection has become a challenge for researchers in this field. In this paper, we propose a system for MCs detection based on the multifractal approach that classifies mammographic ROIs into normal (healthy) or abnormal ROIs containing MCs. The proposed method is divided into four main steps: a mammogram pre-processing step based on breast selection, breast density reduction using haze removal algorithm and contrast enhancement using multifractal measures. The second step consists of extracting the normal and abnormal ROIs and calculating the multifractal spectrum of each ROI. The next step represents the extraction of the multifractal features from the multifractal spectrum and the GLCM characteristics of each ROI. The last step is the classification of ROIs where three classifiers are tested (KNN, DT, and SVM). The system is evaluated on images from the INbreast database (308 images) with a total of 2688 extracted ROIs (1344 normal, 1344 with MC) from different BI-RADS classes. In this study, the SVM classifier gave the best classification results with a sensitivity, specificity, and precision of 98.66%, 97.77%, and 98.20% respectively. These results are very satisfactory and remarkable compared to the literature. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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