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1860796884995538944
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INTELEK Repository
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Online Access
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https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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| date |
2024-09-24 16:02:58
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| eventvenue |
Bosnia and Herzegovina, Virtual
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Restricted Document
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| id |
10553
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UniSZA
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4591-01-FH03-FSK-21-55109.pdf
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Adobe Acrobat Pro DC 20.6.20042
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| recordtype |
oai_dc
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| resourceurl |
https://intelek.unisza.edu.my/intelek/pages/view.php?ref=10553
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| spelling |
10553 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=10553 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Conference Conference Paper application/pdf 5 1.6 Adobe Acrobat Pro DC 20 Paper Capture Plug-in Adobe Acrobat Pro DC 20.6.20042 2024-09-24 16:02:58 4591-01-FH03-FSK-21-55109.pdf UniSZA Private Access Breast lesions detection using FADHECAL and Multilevel Otsu Thresholding Segmentation in digital mammograms Breast cancer is the most common cause of mortality among women. Early detection plays an important role to improve survival rates. Digital mammograms can be used to detect breast lesions within the breast tissue. However, digital mammograms have a limitation of low contrast images due to the low exposure factors used. As a result, the extraction of breast lesions using the region of interest (ROI) tool will be difficult and, thus, lead to misclassification. This paper presents a novel technique to detect breast lesions in digital mammograms, known as Fuzzy Anisotropic Diffusion Histogram Equalization Contrast Adaptive Limited (FADHECAL) incorporated with Multilevel Otsu Thresholding Segmentation. FADHECAL will enhance the breast lesions by reducing the image noise while preserving the details. Multilevel Otsu Thresholding Segmentation detects the breast lesions using the ROI tool at different intensity levels. The performance of FADHECAL incorporated with Multilevel Otsu Thresholding Segmentation has been tested on 115 digital mammograms from the Mammographic Image Analysis Society (MIAS) database with the abnormal conditions. The efficiency of the proposed technique is 94.8%, and the error rate is 5.2%. In conclusion, FADHECAL incorporated with the Multilevel Otsu Thresholding Segmentation has provided sufficient detection of breast lesions with the appropriate quality of the digital mammograms. International Conference on Medical and Biological Engineering Bosnia and Herzegovina, Virtual
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| spellingShingle |
Breast lesions detection using FADHECAL and Multilevel Otsu Thresholding Segmentation in digital mammograms
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| summary |
Breast cancer is the most common cause of mortality among women. Early detection plays an important role to improve survival rates. Digital mammograms can be used to detect breast lesions within the breast tissue. However, digital mammograms have a limitation of low contrast images due to the low exposure factors used. As a result, the extraction of breast lesions using the region of interest (ROI) tool will be difficult and, thus, lead to misclassification. This paper presents a novel technique to detect breast lesions in digital mammograms, known as Fuzzy Anisotropic Diffusion Histogram Equalization Contrast Adaptive Limited (FADHECAL) incorporated with Multilevel Otsu Thresholding Segmentation. FADHECAL will enhance the breast lesions by reducing the image noise while preserving the details. Multilevel Otsu Thresholding Segmentation detects the breast lesions using the ROI tool at different intensity levels. The performance of FADHECAL incorporated with Multilevel Otsu Thresholding Segmentation has been tested on 115 digital mammograms from the Mammographic Image Analysis Society (MIAS) database with the abnormal conditions. The efficiency of the proposed technique is 94.8%, and the error rate is 5.2%. In conclusion, FADHECAL incorporated with the Multilevel Otsu Thresholding Segmentation has provided sufficient detection of breast lesions with the appropriate quality of the digital mammograms.
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| title |
Breast lesions detection using FADHECAL and Multilevel Otsu Thresholding Segmentation in digital mammograms
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| title_full |
Breast lesions detection using FADHECAL and Multilevel Otsu Thresholding Segmentation in digital mammograms
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| title_fullStr |
Breast lesions detection using FADHECAL and Multilevel Otsu Thresholding Segmentation in digital mammograms
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| title_full_unstemmed |
Breast lesions detection using FADHECAL and Multilevel Otsu Thresholding Segmentation in digital mammograms
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| title_short |
Breast lesions detection using FADHECAL and Multilevel Otsu Thresholding Segmentation in digital mammograms
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| title_sort |
breast lesions detection using fadhecal and multilevel otsu thresholding segmentation in digital mammograms
|