Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Objective Evaluation of Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation evaluation: A survey of unsupervised methods
Computer Vision and Image Understanding
Development of tolerant features for characterization of masses in mammograms
Computers in Biology and Medicine
Automated assessment of breast tissue density in digital mammograms
Computer Vision and Image Understanding
Segmentation of regions of interest in mammograms in a topographic approach
IEEE Transactions on Information Technology in Biomedicine
State-of-the-Art of computer-aided detection/diagnosis (CAD)
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
Mammogram retrieval on similar mass lesions
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
MARGA: Multispectral Adaptive Region Growing Algorithm for brain extraction on axial MRI
Computer Methods and Programs in Biomedicine
Teeth segmentation of dental periapical radiographs based on local singularity analysis
Computer Methods and Programs in Biomedicine
A marker-based watershed method for X-ray image segmentation
Computer Methods and Programs in Biomedicine
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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.