Detection of single and clustered microcalcifications in mammograms using fractals models and neural networks.
Microcalcifications (microCas) are often early signs of breast cancer. However, detecting them is a difficult visual task and recognizing malignant lesions is a complex diagnostic problem. In recent years, several research groups have been working to develop computer-aided diagnosis (CAD) systems for X-ray mammography. In this paper, we propose a method to detect and classify microcalcifications. In order to discover the presence of microCas clusters, particular attention is paid to the analysis of the spatial arrangement of detected lesions. A fractal model has been used to describe the mammographic image, thus, allowing the use of a matched filtering stage to enhance microcalcifications against the background. A region growing algorithm, coupled with a neural classifier, detects existing lesions. Subsequently, a second fractal model is used to analyze their spatial arrangement so that the presence of microcalcification clusters can be detected and classified. Reported results indicate that fractal models provide an adequate framework for medical image processing; consequently high correct classification rates are achieved.
Bocchi
L
L
Department of Electronics and Telecommunications, University of Florence, Via S. Marta, 3, 50139 Florence, Italy. leo@asp.det.unifi.it
Coppini
G
G
Nori
J
J
Valli
G
G
D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D002114:Calcinosis / Q000000981:diagnostic imaging* / Q000473:pathology; D016000:Cluster Analysis; D003198:Computer Simulation; D005103:Expert Systems; D017709:Fractals; D006801:Humans; D008327:Mammography / Q000379:methods*; D008954:Models, Biological; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D011230:Precancerous Conditions / Q000000981:diagnostic imaging* / Q000473:pathology; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity