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Segmentation of breast cancer lesion in digitized mammogram images
Faculty
Engineering
Year:
2014
Type of Publication:
ZU Hosted
Pages:
Authors:
Shaymaa Ahmed Hassan Ahmmd
Staff Zu Site
Abstract In Staff Site
Journal:
2014 Cairo International Biomedical Engineering Conference (CIBEC) IEE
Volume:
Keywords :
Segmentation , breast cancer lesion , digitized mammogram
Abstract:
Segmentation or abnormality detection is an essential step in mammographic computer-aided diagnosis (CAD) systems. This paper presents a novel computerized method to automatically detect mass lesions (i.e. detect suspicious locations containing abnormalities inside the breast area) on digitized mammogram images. In particular, we implement an enhanced version of the region growing algorithm for segmentation of mass lesions that can be implemented in a complete CAD system. The proposed algorithm uses region growing technique with a novel automatic threshold estimation method to detect and segment mass lesions. The proposed algorithm detects masses by analyzing a single view of the breast (i. e. Medio-Lateral oblique (MLO) view or Cranio-Caudal (CC) view). The performance of the proposed algorithm was evaluated using two mammogram databases from two different hospitals. The matching percentage of the segmented regions obtained by the proposed algorithm is 83% with respect to the ground truth (i.e. reference determined by an expert radiologist). The proposed algorithm showed promising performance when compared with other commonly used segmentation techniques.
Author Related Publications
Shaymaa Ahmed Hassan Ahmmd, "Detection of breast cancer mass using MSER detector and features matching", Springer, 2019
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Shaymaa Ahmed Hassan Ahmmd, "Breast cancer masses classification using deep convolutional neural networks and transfer learning", Springer Nature, 2020
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Shaymaa Ahmed Hassan Ahmmd, "Pectoral muscle identification in mammograms for Computer Aided Diagnosis of breast cancer", IEEE, 2012
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