This
paper presents the performance analysis of preprocessing algorithms for
enhancement features on mammograms with the objective of improving future
clustering processing investigations, space frequency filtering and morphologic
space filtering, which are analyzed with their different techniques in regions
of interest using the DDSM database. Displaying microcalcifications, which is
achieved with the Gaussian high-pass filter in the space frequency and diamond
filter, in morphologic space, supported by spectral images (spectrograms) as well as the efficiency, is
measured over massive preprocessing images. However, when an improved
visualization is presented in
processing times, it is observed that the variance in time analyzes a
large number of images. Also, although
the frequency is faster than morphological space, the morphological filters
(Ball filtering) shows a significant result than frequency filters.
Cite this paper
Guardado-Medina, R. O. , Mendoza-Luna, L. E. , Ortiz-Ramirez, J. A. , Toro, H. B. D. , Lepe, R. R. and Mata, G. O. (2014). Analysis of Preprocessing Algorithms for Space Frequency and Mathematical Morphology on Mammograms. Open Access Library Journal, 1, e924. doi: http://dx.doi.org/10.4236/oalib.1100924.
Quintanilla-Dominguez, J., Cortina-Januchs,
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Quintanilla-Dominguez, J., Ruelas, R. and Andina, D. (2009) Images Subsegmentation with the PFCM Clustering Algorithm.
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