Pattern Recognition
The nature of statistical learning theory
The nature of statistical learning theory
Risk Classification of Mammograms Using Anatomical Linear Structure and Density Information
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Mammographic risk assessment based on anatomical linear structures
IWDM'06 Proceedings of the 8th international conference on Digital Mammography
A Novel Breast Tissue Density Classification Methodology
IEEE Transactions on Information Technology in Biomedicine
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Mammographic risk assessment is concerned with the probability of a woman developing breast cancer. Recently, it has been suggested that the density of linear structures is related to risk. Two independent sets of mammographic images were annotated according to BIRADS risk classes by expert radiologists. Linear structure information was extracted from each image using the line operator method, and density segmentation was performed using a method based on minimum error thresholding.Linear discriminant analysis and a Support Vector Machine classifer were used to classify the images in to BIRADS classes. The classification was performed three times for each dataset --- once using density information only, once using linear structure information only, and once using both density and linear structure information. The results of classification showed a marked improvement when both density and linear structure information were used, suggesting that linear structure information is valuable in mammographic risk classification.