A Self Organizing Map approach to breast cancer detection.
Detection and characterization of cancer tumors in mammograms is vital in daily clinical practice. The problem of detecting possible cancer areas is very complex due, on one hand, to the diversity in shape of the ill tissue and on the other hand to the poorly defined border between the healthy and the cancerous zone. Even though it has been studied for many years, there are still remaining challenges and directions for future research such as developing better enhancement and segmentation algorithms. The performance of the Self Organizing Map (SOM) in detecting the cancer suspicious regions in digitized mammograms is revealed in this study. In order to achieve the best results we firstly apply the preprocessing algorithms proposed in section II of the study.
Oprea
Alina E
AE
Applied Electronics and Information Engineering Departement, Politehnica University of Bucharest, Bucharest, Romania. aoprea@alpha.imag.pub.ro
Strungaru
Rodica
R
Ungureanu
G Mihaela
GM
D000465:Algorithms; D001940:Breast / Q000033:anatomy & histology*; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D016000:Cluster Analysis; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008327:Mammography / Q000379:methods / Q000592:standards*; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011856:Radiographic Image Enhancement / Q000379:methods*; D012984:Software