Comput Methods Programs Biomed - Breast mass contour segmentation algorithm in digital mammograms.

Tópicos

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Resumo

Many computer aided diagnosis (CAD) systems help radiologist on difficult task of mass detection in a breast mammogram and, besides, they also provide interpretation about detected mass. One of the most crucial information of a mass is its shape and contour, since it provides valuable information about spread ability of a mass. However, accuracy of shape recognition of a mass highly related with the precision of detected mass contours. In this work, we introduce a new segmentation algorithm, breast mass contour segmentation, based on classical seed region growing algorithm to enhance contour of a mass from a given region of interest with ability to adjust threshold value adaptively. The new approach is evaluated over a dataset with 260 masses whose contours are manually annotated by expert radiologists. The performance of the method is evaluated with respect to a set of different evaluation metrics, such as specificity, sensitivity, balanced accuracy, Yassnoff and Hausdorrf error distances. The results obtained from experimentations shows that our method outperforms the other compared methods. All the findings and details of approach are presented in detail.

Resumo Limpo

mani comput aid diagnosi cad system help radiologist difficult task mass detect breast mammogram besid also provid interpret detect mass one crucial inform mass shape contour sinc provid valuabl inform spread abil mass howev accuraci shape recognit mass high relat precis detect mass contour work introduc new segment algorithm breast mass contour segment base classic seed region grow algorithm enhanc contour mass given region interest abil adjust threshold valu adapt new approach evalu dataset mass whose contour manual annot expert radiologist perform method evalu respect set differ evalu metric specif sensit balanc accuraci yassnoff hausdorrf error distanc result obtain experiment show method outperform compar method find detail approach present detail

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