Improvement of Segmentation Efficiency in Mammogram Images Using Dual-ROI Method

2. Literature Survey

Several approaches are recommended by researchers in breast cancer segmentation framework. In this situation, short assessments of some paramount contributions to the accessible articles are conferred.

Jasmine et al. 2009 presented a paper on a new approach for detecting micro-calcification in digital mammograms employing the combination of wavelet analysis of the image by applying artificial neural networks (ANN) for building the classifiers. The micro-calcification corresponds to high frequency components and the detection of micro-calcification is achieved by extracting the micro-calcification features from the wavelet analysis of the image and we use these results as an input of neural network for classification. The neural network contains one input, two hidden and one output .The system is classified normal from abnormal, mass for micro-calcification and abnormal severity (benign or malignant). The experiments demonstrate that their approach can provide true detection rate approximately 87% and 0 false detection per image which is significant. The evaluation of the system is carried on INbreast dataset.