A New CAD System for Breast Microcalcifications Diagnosis

Abstract

In this paper a new approach of breast microcalcifications diagnosis on digital mammograms is introduced. The proposed approach begins with a preprocessing procedure aiming artifacts and pectoral muscle removal based on morphologic operators and contrast enhancement based on galactophorous tree interpolation.The second step of the proposed CAD system consists on segmenting microcalcifications clusters, using Generalized Gaussian Density (GGD) estimation and a Bayesian backpropagation neural network.The last step is microcalcifications characterization using morphologic features which are used to feed a neuro-fuzzy system to classify the detected breast microcalcifications into benign and malignant classes.

A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis

“…Many methods have been proposed to achieve a robust mammography-based CAD system for microcalcification diagnosis [13][14][15][16][17][18], in some cases well performing in dealing with specific abnormalities. Nevertheless, the automatic and accurate classification of microcalcification clusters, especially in differentiating the benign from the malignant ones, remains still complicated due to their nature.…”

“…For what concerns the extraction of a representative set of features from the ROIs, different models have been proposed in literature ranging from those describing shapes of the clusters and classical statistical measurements [10,19] to the ones exploiting morphological features [5,16]. In some works, the texture analysis model uses a set of local statistical properties of pixel intensity.…”

Pre-processing of Digital Mammogram – A Review

“…The method proposed by H.Boulehmi et al [16],has showed near to accuracy result achieved at earlier stages during evaluating malignancy. In the citation by B.Barathi et al [13], the suggested method claimed to perform well in terms of speed, robustness and accuracy, on the contrary the citation by R.K Khare et al [12], said that his method has proved to be far more accurate in comparison to several other works.…”

“…The key characteristics to highlight in his work is the enhanced image generated from this technique of global enhancement, can interpret and compare features, thus a speeding up the task of radiologists. In the citation by H. Boulehmi et al [16] enhancement method has improved the quality of mammograms to facilitate the detection procedure. Among the three primary different enhancement techniques of conventional techniques, regions-based techniques and features-based techniques.…”

“…The citation by H. Boulehmi et al [16] enhancement method the feature based method processed mammographic image characteristics and include morphological operations, wavelet transform and fractal approach, among the various other important methods that were suggested by him. In the citation by B. Senthilkumar et al [21], work well.…”