The use of a priori information in the detection of mammographic microcalcifications to improve their classification.
In this work, we present a calcification-detection scheme that automatically localizes calcifications in a previously detected cluster in order to generate the input for a cluster-classification scheme developed in the past. The calcification-detection scheme makes use of three pieces of a priori information: the location of the center of the cluster, the size of the cluster, and the approximate number of calcifications in the cluster. This information can be obtained either automatically from a cluster-detection scheme or manually by a radiologist. It is used to analyze only the portion of the mammogram that contains a cluster and to identify the individual calcifications more accurately, after enhancing them by means of a "Difference of Gaussians" filter. Classification performances (patient-based Az=0.92; cluster-based Az=0.72) comparable to those obtained by using manually-identified calcifications (patient-based Az=0.92; cluster-based Az=0.82) can be achieved.
Salfity
María F
MF
Instituto de Física Rosario, CONICET-UNR, Bv. 27 de Febrero 210 bis, 2000 Rosario, Argentina. m.f.salfity@lboro.ac.uk
Nishikawa
Robert M
RM
Jiang
Yulei
Y
Papaioannou
John
J
D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000517:prevention & control; D002114:Calcinosis / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011230:Precancerous Conditions / Q000000981:diagnostic imaging*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique