Multi-scale mass segmentation for mammograms via cascaded random forests

@article{Min2017MultiscaleMS,
  title={Multi-scale mass segmentation for mammograms via cascaded random forests},
  author={Hang Min and Shekhar Chandra and Neeraj Dhungel and Stuart Crozier and Andrew P. Bradley},
  journal={2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)},
  year={2017},
  pages={113-117}
}
Breast mass detection and segmentation are difficult tasks due to the variation in size and shape of breast masses. Constructing classifiers for this problem is also challenging due to the fact that normal tissue regions overwhelmingly outnumber abnormal regions. In this paper, we propose a novel approach for detecting and segmenting breast masses in mammography based on multi-scale morphological filtering and a self-adaptive cascade of random forests (CasRFs). CasRFs can cope with severe class… CONTINUE READING

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SHOWING 1-10 OF 25 REFERENCES

SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2012
VIEW 8 EXCERPTS
HIGHLY INFLUENTIAL

Automated Mass Detection in Mammograms Using Cascaded Deep Learning and Random Forests

  • 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
  • 2015
VIEW 6 EXCERPTS
HIGHLY INFLUENTIAL

Breast mass detection with kernelized supervised hashing

  • 2015 8th International Conference on Biomedical Engineering and Informatics (BMEI)
  • 2015
VIEW 1 EXCERPT

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