Characterisation of Breast Cancer Lesions using Image Processing Based TechniqueCorrespondence Address :
Dr. Yousif Mohmed Abdallah,
Asscoiate Professor, Department of Radiological Science and Medical Imaging, College of Applied Medical Science, Majmaah University, Majmaah-11952, Saudi Arabia.
Introduction: Characterisation of mammographs deliberates as influential approaches in cataloguing of breast tissues and tumour. In breast, unravelling of nearby tissues in the mammographs is one of the tough processing procedures. The existence of speckle noise in these mammographs boundaries makes the pathology analysis more difficult.
Aim: To characterise breast cancer lesions using different image processing algorithms in order to improve the mammographs and increase their diagnostic value.
Materials and Methods: This retrospective study aims to locate structures and lesions in breast images. The algorithms use the noise speckles deletion, augmentation and subdivision of the breast tissue and the background in mammographs. More precisely, it aims to ascribe a label to pixels within the mammographs that have the same graphic characteristics. The segmented images were associated with the binary image mask to the original mammograph. The Root-Mean-Square Error (RMSE) and Peak Signal-to-Noise Ratio (PSNR) were studied in images database. Both percentage match between ground truth and segmentation results were calculated.
Results: Percentage match measure of watershed algorithm was 96.60 (p<0.05) and Corresponding Ratio (CR) was 0.019 (p<0.05). The edge detection gave good and clear visualisation of the processed images that increased the diagnostic value of them.
Conclusion: The edge detection and water-marker technique are able to identify the breast lesions precisely and improves radiological analysis and diagnosis.
Image enhancement, Image processing, Mammography, Segmentation
Yousif Mohmed Abdallah, Nouf Hussain Abuhadi, Dalia Bilal. CHARACTERISATION OF BREAST CANCER LESIONS USING IMAGE PROCESSING BASED TECHNIQUE. Journal of Clinical and Diagnostic Research [serial online] 2019 August [cited: 2019 Aug 20 ]; 13:TC06-TC09. Available from
Date of Submission: Mar 06, 2019
Date of Peer Review: Apr 08, 2019
Date of Acceptance: Jun 25, 2019
Date of Publishing: Aug 01, 2019
FINANCIAL OR OTHER COMPETING INTERESTS: As declared above.
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