Shape analysis of microcalcifications using Radon transform.
Microcalcifications are one of the early signs of breast cancer, and they are of great importance for an early diagnosis. Moreover, the spatial distribution and the shape of the microcalcifications have a significant impact in medical practice to evaluate the probability of malignancy of the tumor. In this work a method, performing computer-aided classification of the shape of calcifications accordingly to the classification scheme proposed by Le Gal, is presented. In the first stage, in order to remove mammographic background, the image is preprocessed with a matched filter, designed by modeling the microcalcifications as Gaussian spots and the image as a Fractional Brownian Motion. Afterwards, morphology of spots has been evaluated using two different sets of parameters. The first set utilizes the moments of inertia of the second and third order to compute a set of features, which are invariant to rotations and translations of the image. The second set of parameters is derived from the evaluation of the Radon transform, as computed along eight axes. The results of the Radon transform are used to associate to each lesion a set of features, which are invariant to rotation and scaling of the image. In the final stage, a multilayer neural network has been used to assign each microcalcification to the classes introduced by Le Gal. The topology of the neural network is the same for both sets of descriptors, in order to allow comparison of the discriminative power of the two feature sets. Experimental results obtained with the proposed method from a set of digitized mammograms are reported and discussed.
Bocchi
L
L
Department of Electronics and Telecommunications, University of Florence, Florence, Italy. leonardo.bocchi@unifi.it
Nori
J
J
D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; 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