Analysis of Preprocessing Algorithms for Space Frequency and Mathematical Morphology on Mammograms

Ramón O. Guardado-Medina¹, Luis E. Mendoza-Luna², Jorge A. Ortiz-Ramirez², Humberto Bracamontes del Toro², Ruben Ruelas Lepe^1,2, Gustavo Ochoa Mata²

¹Information Technologies Department, University of Guadalajara, México.
²Electronics Engineering Department, Technological Institute of Ciudad Guzmán, México.

DOI: 10.4236/oalib.1100924                

Abstract

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.

Keywords

Preprocessing Images, Mathematical Morphology, Fourier Transform, Frequency

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	International Agency for Research Cancer (2013) http://www.iarc.fr/
[2]	Instituto Nacional de Cancerología de México, Sistema de información de Cáncer (2013) http://www.infocancer.org.mx/contenidos.php?idcontenido=1
[3]	Instituto Nacional de Estadística y Geografía (2014) http://www.inegi.org.mx/
[4]	Instituto Nacional de Cáncer de los Institutos Nacionales de la Salud en EE.UU (2013) http://www.cancer.gov/espanol/recursos/hojas-informativas/deteccion-diagnostico/mamografias.
[5]	Quintanilla-Dominguez, J., Cortina-Januchs, M.G., Ojeda-Magana, B., Jevti, A., Vega-Corona, A. and Andina, D. (2010) Microcalcification Detection Applying Artificial Neural Networks and Mathematical morphology in Digital Mammograms. World Automation Congress.
[6]	Guardado-Medina, R.O, Ojeda-Magaña, B., Quintanilla-Domínguez, J., Ruelas, R. and Andina, D. (2013) Quality of Microcalcification Segmentation in Mammograms by Clustering Algorithms. SOCO13, Salamanca, Spain.
[7]	Ojeda-Magaña, B., Quintanilla-Dominguez, J., Ruelas, R. and Andina, D. (2009) Images Subsegmentation with the PFCM Clustering Algorithm. 7th IEEE International Conference on Industrial Informatics, 23-26 June 2009, Cardiff, 499-505.
[8]	Malar, E., Kandaswamy, A. and Gauthaam, M. (2013) Multiscale and Multilevel Wavelet Analysis of Mammogram Using Complex Neural Network. Springer International Publishing, Switzerland, 658-668.
[9]	Chen, Z., Strange, H., Denton, E. and Zwiggelaar, R. (2014) Analysis of Mammographic Microcalcification Clusters Using Topological Features. Springer International Publishing, Switzerland, 620-627.
[10]	Paradkar, S. and Spande, S. (2011) Intelligent Detection of Microcalcification from Digitized Mammograms. Sadhana, 36, 125-139.
[11]	Gonzalez, R.C., Woods, R.E. and Eddins, S.L. (2004) Digital Images Processing Using Matlab. Pearson Education.
[12]	Castleman, K.R. (1999) Digital Images Processing. Prentice Hall, Upper Saddle River.
[13]	Gonzalez, R. C. and Woods, R.E. (1996) Tratamiento Digital de Imagines. Editorial Díaz de Santos, S.A.
[14]	Rose, C., Turi, D., Williams, A., Wolstencroft, K. and Taylor, C., IWDM (2006) The Digital Database for Screening Mammography, University of South Florida. http://marathon.csee.usf.edu/Mammography/Database.html