Microcalcification enhancement and classification on mammograms using the wavelet transform

This paper presents a method to enhance microcalcifications and classify their borders by applying the wavelet transform. Decomposing an image and removing its low frequency sub-band the microcalcifications are enhanced. Analyzing the effects of perturbations on high frequency sub-band itpsilas possible to classify its borders as smooth, rugged or undefined. Results show a false positive reduction of 69.27% using a region growing algorithm.

[1]  C.D. Maciel,et al.  A Study on the Best Wavelet for Audio Compression , 2006, 2006 Fortieth Asilomar Conference on Signals, Systems and Computers.

[2]  Aledir Silveira Pereira,et al.  Processamento de imagens médicas utilizando a transformada de Hough , 1995 .

[3]  Johann Drexl,et al.  Integrated wavelets for enhancement of microcalcifications in digital mammography , 2003, IEEE Transactions on Medical Imaging.

[4]  Gholamali Rezai-Rad,et al.  Detecting microcalcification clusters in digital mammograms using combination of wavelet and neural network , 2005, International Conference on Computer Graphics, Imaging and Visualization (CGIV'05).

[5]  Díbio Leandro Borges,et al.  Automated mammogram classification using a multiresolution pattern recognition approach , 2001, Proceedings XIV Brazilian Symposium on Computer Graphics and Image Processing.

[6]  Alan F. Murray,et al.  International Joint Conference on Neural Networks , 1993 .

[7]  Paul S. Addison,et al.  The Illustrated Wavelet Transform Handbook , 2002 .

[8]  Jacob Scharcanski,et al.  Denoising and enhancing digital mammographic images for visual screening , 2006, Comput. Medical Imaging Graph..

[9]  David A. Yuen,et al.  Detection of clustered microcalcifications in small field digital mammography , 2006, Comput. Methods Programs Biomed..

[10]  Nicolaos B. Karayiannis,et al.  Detection of microcalcifications in digital mammograms using wavelets , 1998, IEEE Transactions on Medical Imaging.