Breast Cancer Detection in Mammographic Image

Taber, Dania Awad Hussien; Elfadil, Rahma Abdulmoniem Hassan; Onsa, Shaima Mahmoud Mustafa

dc.contributor.author	Taber, Dania Awad Hussien
dc.contributor.author	Elfadil, Rahma Abdulmoniem Hassan
dc.contributor.author	Onsa, Shaima Mahmoud Mustafa
dc.date.accessioned	2014-12-25T12:00:54Z
dc.date.available	2014-12-25T12:00:54Z
dc.date.issued	2014-08-10
dc.identifier.citation	Taber,Dania Awad Hussien.Breast Cancer Detection in Mammographic Image/Dania AwadHussien Taber,Rahma Abdulmoniem Hassan Elfadil,Shaima Mahmoud Mustafa Onsa;Alnazeir Osman Mohamed.-Khartuom:Sudan University of Science and Technology, College of Engineering,2014.-50p.:ill;28cm.-Bachelors’Search	en_US
dc.identifier.uri	http://repository.sustech.edu/handle/123456789/9262
dc.description	Bachelors’Search	en_US
dc.description.abstract	Breast cancer is one of the major causes of death in women when compared to all other cancers. This research was developed methods that can be used to detect abnormal breast tissues. Matlab programs were devolved to detect abnormal tissue in mammograms, to assist radiologist in their diagnosis as a second opinion. In this study100 image consist 100 normal ROIs, 100 abnormal ROIs (contained 61 benign ROIs and 39 ROIs malignant ROIs) was extracted from digital mammogram which obtained from mini-MAIS database. Five First order statistical features were calculated for each ROI. Fourteen Haralick features according to GLCM matrix was calculate for each ROI. The best features were selected by their ability to distinguish between normal and abnormal tissue to use it in program to detect breast cancer	en_US
dc.description.sponsorship	Sudan University of Science and Technology, College of Engineering	en_US
dc.language.iso	en	en_US
dc.publisher	Sudan University of Science and Technology	en_US
dc.subject	Biomedical Engineering	en_US
dc.subject	Breast Cancer	en_US
dc.subject	Mammographic	en_US
dc.title	Breast Cancer Detection in Mammographic Image	en_US
dc.type	Thesis	en_US