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Breast cancer is reported to be the second deadliest cancer among cancerous woman. Statistics show that the case of breast cancer in the world is increasing every year. By analyzing a mammogram, pathologists could detect the presence of micro calcification in ones breast. However, micro calcification could be classified into benign and malignant. The later indicates the presence of cancer. Computer-Aided...
Clustered micro calcifications (MCs) are one of the early signs of breast cancer. In this paper, we propose a new computer aided diagnosis (CAD) system for automatic detection of MCs in two steps. First, pixels corresponding to potential micro calcifications are found using a multilayer feed-forward neural network. The input of this network consists of 4 wavelet and 2 gray-level features. The output...
Identifying relevant, representative and more important, discriminant image features for analysis and proper image classification purpose is one of the main tasks in image processing and pattern recognition field. In this paper, Gabor wavelets based features are extracted from medical mammogram images representing normal tissues, or benign and malign tumors. Once features are detected, Principal Component...
In this paper an approach is proposed to develop a computer-aided diagnosis (CAD) system that can be very helpful for radiologist in diagnosing microcalcifications' patterns in digitized mammograms earlier and faster than typical screening programs and showed the efficiency of feature selection on the CAD system. The proposed method has been implemented in four stages: (a) the region of interest (ROI)...
Clusters of microcalcifications in mammograms are an important early sign of breast cancer in women. In this paper an approach is proposed to develop a computer-aided diagnosis (CAD) system that can be very helpful for radiologist in diagnosing microcalcifications' patterns in digitized mammograms earlier and faster than typical screening programs. The proposed method has been implemented in three...
In this paper, we investigate wavelet-based feature extraction from mammogram images and efficient dimensionality reduction techniques. The aim is to propose a new computerized feature extraction technique to identify abnormalities in breast mammogram images. In this work, dimensionality reduction is carried out using the minimal-redundancy-maximal-relevance criterion (mRMR). The classification accuracy...
Female breast cancer is the major cause of death in occidental countries. Efforts in computer vision have been made in order to help improving the diagnostic accuracy by radiologists. We propose a methodology to distinguish Mass and Non-Mass tissues on mammograms. It is based on the computation of geostatistical measures (Moran's Index and Geary's Coefficient) over a multiresolution image representation...
The clustered microcalcification on X-ray mammogram provides an important cue for early detection of breast cancer. Texture analysis methods can be applied to detect clustered micro calcifications in digitized mammograms. In this paper a novel two stage method for mammogram segmentation is implemented to facilitate automatic segmentation of micro calcification. The first stage is the Modified combined...
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