High Accuracy Microcalcifications Detection of Breast Cancer Using Wiener LTI Tophat Model

In order to avoid cancer, it is imperative that microcalcification in the breast be found. It is sufficiently small to be difficult to discern with the unassisted eye. Computer-based detection output is modest and tends to stay concealed from the radiologist doing the examination, which might help t...

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Published in:	IEEE access Vol. 12; pp. 153316 - 153329
Main Authors:	Jamil, Razia, Dong, Min, Rashid, Javed, Orken, Mamyrbayev, Sholpan Pernebaykyzy, Zhumagulova, Kymbat Ragytovna, Momynzhanova
Format:	Journal Article
Language:	English
Published:	IEEE 2024
Subjects:	Accuracy Biomedical image processing Breast cancer Calcium computer-aided design Design automation Mammography microcalcifications Microwave imaging Radiology wiener filter Wiener filters
ISSN:	2169-3536, 2169-3536
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Abstract	In order to avoid cancer, it is imperative that microcalcification in the breast be found. It is sufficiently small to be difficult to discern with the unassisted eye. Computer-based detection output is modest and tends to stay concealed from the radiologist doing the examination, which might help the radiologist increase diagnostic accuracy. According to this study, the best Wiener Linear Time Invariant Filter method with Tophat Transformation (LFWT) can identify microcalcification in the breast with an accuracy rate of 99.5%. In this work, we focused on the identification of microcalcifications in images, an essential initial step towards precisely identifying all the indicators in a mammography-based early breast cancer diagnosis. To make the cancer region visible and prominent, the Wiener and CLAHE filters are used. Tophat morphological operators were applied to mask detection, and edges were extracted. The analytical performance of the proposed model for microcalcification identification in mammograms was evaluated and compared with other approaches using Mammographic Image Analysis Society (MIAS) and Mini-Mammographic imaging datasets. Additionally, three techniques- The Local Contrast Method (LCM), the Local Relative Contrast Measure Method (LRCMM), and the High-Boost-Based Multiscale Local Contrast Measure (HBBMLCM) are used to identify microcalcification linked to cancer on mammography images. Performance Evaluation of the Proposed Model: the LFWT methodology had the best level of efficacy in detecting microcalcification linked to breast cancer. The suggested LFWT technique finds each and every tiny point on the MIAS dataset's mammography.
AbstractList	In order to avoid cancer, it is imperative that microcalcification in the breast be found. It is sufficiently small to be difficult to discern with the unassisted eye. Computer-based detection output is modest and tends to stay concealed from the radiologist doing the examination, which might help the radiologist increase diagnostic accuracy. According to this study, the best Wiener Linear Time Invariant Filter method with Tophat Transformation (LFWT) can identify microcalcification in the breast with an accuracy rate of 99.5%. In this work, we focused on the identification of microcalcifications in images, an essential initial step towards precisely identifying all the indicators in a mammography-based early breast cancer diagnosis. To make the cancer region visible and prominent, the Wiener and CLAHE filters are used. Tophat morphological operators were applied to mask detection, and edges were extracted. The analytical performance of the proposed model for microcalcification identification in mammograms was evaluated and compared with other approaches using Mammographic Image Analysis Society (MIAS) and Mini-Mammographic imaging datasets. Additionally, three techniques- The Local Contrast Method (LCM), the Local Relative Contrast Measure Method (LRCMM), and the High-Boost-Based Multiscale Local Contrast Measure (HBBMLCM) are used to identify microcalcification linked to cancer on mammography images. Performance Evaluation of the Proposed Model: the LFWT methodology had the best level of efficacy in detecting microcalcification linked to breast cancer. The suggested LFWT technique finds each and every tiny point on the MIAS dataset's mammography.
Author	Sholpan Pernebaykyzy, Zhumagulova Orken, Mamyrbayev Jamil, Razia Kymbat Ragytovna, Momynzhanova Rashid, Javed Dong, Min
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References	ref13 ref35 ref12 Saravanan (ref22) 2017; 5 ref34 ref15 ref37 ref14 ref36 ref31 ref30 ref11 ref33 ref10 ref32 ref2 ref17 ref39 ref16 ref38 ref19 ref18 Gülsün (ref6) 2004; 28 ref24 ref26 ref25 ref20 ref42 ref41 ref21 ref28 ref27 ref29 ref8 ref7 Suckling (ref43) 2015 ref9 ref3 (ref1) 2024 ref5 Leborgne (ref4) 1951; 65 ref40 Gaikwad (ref23) 2015; 10
References_xml	– volume: 28 start-page: 153 issue: 2 volume-title: Clin. Imag. year: 2004 ident: ref6 article-title: Evaluation of breast microcalcifications according to breast imaging reporting and data system criteria and le Gal’s classification – ident: ref35 doi: 10.1007/s11045-020-00756-7 – ident: ref28 doi: 10.1155/2019/9360941 – ident: ref34 doi: 10.3390/s21144854 – ident: ref31 doi: 10.1002/cncr.31551 – volume: 10 start-page: 19 issue: 1 year: 2015 ident: ref23 article-title: Detection of breast cancer in mammogram using support vector machine publication-title: Int. J. Sci. Eng. Res. – ident: ref14 doi: 10.1109/TCBB.2018.2806438 – ident: ref19 doi: 10.1016/j.ipm.2018.10.014 – ident: ref3 doi: 10.3390/cancers13235916 – ident: ref16 doi: 10.1007/978-981-13-3765-9_8 – ident: ref18 doi: 10.1007/s40846-018-0415-9 – ident: ref29 doi: 10.1155/2019/2717454 – volume: 5 start-page: 173 year: 2017 ident: ref22 article-title: Images segmentation using k-means clustering based thresholding algorithm publication-title: Int. J. Adv. Technol. Eng. Sci. – volume: 65 start-page: 1 issue: 1 year: 1951 ident: ref4 article-title: Diagnosis of tumors of the breast by simple roentgenography; calcifications in carcinomas publication-title: Amer. J. Roentgenol. Radium Ther. – ident: ref9 doi: 10.1016/j.diii.2013.12.011 – ident: ref36 doi: 10.1371/journal.pone.0256500 – ident: ref27 doi: 10.1016/j.ejmp.2019.05.022 – ident: ref32 doi: 10.1109/EIT48999.2020.9208290 – ident: ref41 doi: 10.1109/TBME.2021.3059869 – ident: ref33 doi: 10.3390/jimaging5030037 – ident: ref15 doi: 10.1016/j.jksuci.2019.10.014 – ident: ref11 doi: 10.3892/mco.2022.2514 – ident: ref13 doi: 10.1109/BHI.2019.8834517 – ident: ref20 doi: 10.1109/ACCESS.2019.2892795 – ident: ref38 doi: 10.1016/j.bspc.2022.104360 – year: 2015 ident: ref43 article-title: Mammographic image analysis society (MIAS) database v1.21 – volume-title: Breast Cancer. Updated 12 July 2023 year: 2024 ident: ref1 – volume-title: Breast Cancer Screening Guidelines. American Cancer Society Recommendations for the Early Detection of Breast Cancer ident: ref2 – ident: ref39 doi: 10.4258/hir.2021.27.3.222 – ident: ref5 doi: 10.13053/cys-22-1-2560 – ident: ref8 doi: 10.5812/iranjradiol-120758 – ident: ref42 doi: 10.1109/TMI.2023.3345008 – ident: ref17 doi: 10.1007/978-3-030-18058-4_18 – ident: ref7 doi: 10.1055/s-0032-1313102 – ident: ref21 doi: 10.1016/j.ijrobp.2018.08.032 – ident: ref40 doi: 10.1109/TAP.2020.3016407 – ident: ref10 doi: 10.1007/s10911-015-9349-9 – ident: ref25 doi: 10.1007/978-3-319-95921-4_24 – ident: ref30 doi: 10.7567/1882-0786/ab265d – ident: ref24 doi: 10.1049/iet-ipr.2017.0536 – ident: ref26 doi: 10.1007/s10278-019-00192-5 – ident: ref12 doi: 10.1186/s41747-023-00384-3 – ident: ref37 doi: 10.1007/s11760-021-01882-w
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SubjectTerms	Accuracy Biomedical image processing Breast cancer Calcium computer-aided design Design automation Mammography microcalcifications Microwave imaging Radiology wiener filter Wiener filters
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Title	High Accuracy Microcalcifications Detection of Breast Cancer Using Wiener LTI Tophat Model
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