Classifying Image Features in the Last Screening Mammograms Prior to Detection of a Malignant Mass

Survival from breast cancer is directly related to the stage at diagnosis. The earlier the detection, the higher chances of successful treatment [1]. In an attempt to improve early detection, a study has been undertaken to analyze the screening mammograms of breast cancer patients taken prior to cancer detection.

[1]  B Palcic,et al.  Nuclear texture measurements in image cytometry. , 1995, Pathologica.

[2]  W F Bischof,et al.  Automated detection and classification of breast tumors. , 1992, Computers and biomedical research, an international journal.

[3]  R. Bird,et al.  Analysis of cancers missed at screening mammography. , 1992, Radiology.

[4]  N. Boyd,et al.  Automated analysis of mammographic densities. , 1996, Physics in medicine and biology.

[5]  Milorad Neskovic,et al.  Approach to automated screening of mammograms , 1993, Electronic Imaging.

[6]  M. Giger,et al.  Computer vision and artificial intelligence in mammography. , 1994, AJR. American journal of roentgenology.

[7]  W. Philip Kegelmeyer.  Evaluation of stellate lesion detection in a standard mammogram data set , 1993, Electronic Imaging.

[8]  Berkman Sahiner,et al.  An adaptive density-weighted contrast enhancement filter for mammographic breast mass detection , 1996, IEEE Trans. Medical Imaging.

[9]  Rabab K. Ward,et al.  Restoration of mammographic images in the presence of signal-dependent noise , 1993, Electronic Imaging.