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Proceedings Paper

Classification of nodules in mammogram image by using wavelet transform
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Paper Abstract

This work presents a classifier for mammographic masses using the wavelet transform as characteristics generator. It considers the BI-RADS classification, dividing mass according to their shapes: circulate, nodular and speculate. We developed procedures with two steps: the first involves a model applying one wavelet technique performing the contours analysis with simulated mass images. This procedure was used to choose the best wavelet that could generate the desired characteristics. The second procedure had the objective of applying the chosen wavelet to masses from segmented images. Both methods have as answers three classes of shape. A root-mean-square function is applied to obtain the energy measure for each level of wavelet decomposition. Thus the shape feature vectors are formed with the coefficients of the details and coefficients of approximation extracted by the energy of wavelet decomposition levels. Linear Discriminan Analysis (LDA) by using Fischer Discriminant was used to reduce the number of characteristics for the feature vector. The Mahalanobis distance was used by the classifier to verify aimed the pertinence of the images for each one the previously given classes. To test actual images, the leave-one-out method was used to the classifier training. The classifier has registered good results, compared to others reports in the corresponding literature.

Paper Details

Date Published: 15 May 2003
PDF: 11 pages
Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.483552
Show Author Affiliations
Cesar H. M. Santaella, Univ. de Sao Paulo (Brazil)
Homero Schiabel, Univ. de Sao Paulo (Brazil)
Ana Claudia Patrocinio, Univ. de Sao Paulo (Brazil)
Fatima de Lourdes dos Santos Nunes, Fundacao de Ensino Euripides Soares da Rocha (Brazil)
Roseli A. F. Romero, Univ. de Sao Paulo (Brazil)


Published in SPIE Proceedings Vol. 5032:
Medical Imaging 2003: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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