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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...
Breast cancer is the most common cancer among women. To assist the ultrasound (US) diagnosis of solid breast tumors, the lobulated contour feature quantified by boundary-based corner counts is studied to classify breast tumors as malignant or benign. The corner points in this research was detected based on wavelet transform (WT), and the classification selected through comparison is support vector...
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...
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...
De-noising the MRS data is a key processing in analysis of spectroscopy MRS data. This paper presents an effective method based on wavelet-transform and pattern recognition technologies. Upon the characteristics of MRS data, a new wavelet basis function was designed, and a de-noising method of free induction decay (FID) data using wavelet threshold to obtain better MRS spectrums was conduced; hence,...
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