Multiscale and Multilevel Wavelet Analysis of Mammogram Using Complex Neural Network

Malar, E.; Kandaswamy, A.; Gauthaam, M.

doi:10.1007/978-3-319-03756-1_59

Cited by 3 publications

(1 citation statement)

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“…It explains the preprocessing done with tophat (mathematical morphology), which is important to mention that this step facilitates the application of techniques of segmentation or neural networks. On the other hand, some other studies did not take into consideration this situation for example in [8]- [10]. Therefore, this study shows the behavior of the main algorithms, which provides important information in subsequent processing.…”

Section: Introductionmentioning

confidence: 86%

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

Guardado-Medina

Mendoza-Luna²,

Ortiz-Ramirez³

et al. 2014

OALib

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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.

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Section: Introductionmentioning

confidence: 86%