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.

has been cited by the following article:

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

  AUTHORS: Ramón O. Guardado-Medina, Luis E. Mendoza-Luna, Jorge A. Ortiz-Ramirez, Humberto Bracamontes del Toro, Ruben Ruelas Lepe, Gustavo Ochoa Mata

  KEYWORDS: Preprocessing Images, Mathematical Morphology, Fourier Transform, Frequency

  JOURNAL NAME: Open Access Library Journal, Vol.1 No.7, October 29, 2014

  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.