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Computer-aided identification and diagnosis of mass lesion in mammogram

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  1. Abstract
  2. Introduction
  3. Commercial CAD/CADx
  4. Computer aided mammographic systems
  5. Computer aided detection of mass
  6. Computer aided diagnostic systems
  7. Conclusion
  8. References

In this growing technological world still disease are challenging human life. Especially the breast cancer stands the leading causes of mortality in women. Early diagnosis helps for better treatment and increase survival rate. Mammography is one among the methods to detect the breast cancer at an early stage. Some of the signs of breast cancer are clusters of micro calcifications, mass lesion, architectural distortion or asymmetry of the breast. In this paper, we focus only on mass lesion. Computer aided detection (CAD) and computer aided diagnosis (CADx) are tools to recognize abnormality at an early stage. These tools help radiologists to locate and evaluate mammographic abnormality and serve as a useful second opinion to diagnose the missed malignant cases and reduce unnecessary biopsies. Keywords: Mass, Computer aided detection, Computer aided diagnosis, and Computer aided mammography.

[...] Monika Shinde: Computer aided diagnosis in digital mammography : classification of mass and normal tissue, M. S. Dissertation, University of South Florida Nicholas Jabari Lee: Computer- aided diagnostic systems for digital mammograms, M.S. Dissertation, B.S., Jackson State University, December Giovanni Luca Masala, ?Computer Aided Detection on Mammography?, Proceedings of World Academy of Science, Enginering and Technology, Volume 15, October 2006. Polakowski WE, Cournoyer DA, Rogers SK, DeSimio MP, Ruck DW, Hoffmeister JW, Raines RA , ?Computer-Aided Breast Cancer Detection and Diagnosis of Masses using Difference of Gaussians and Derivative-based Feature Saliency?, IEEE transactions on medical imaging, vol. [...]

[...] Since the shapes of masses are crucial in classification between benignancy and malignancy, four shape features are further generated and joined with the five features previously used in mass detection to be implemented in another PNN for mass classification. Obtained a Az = 0.6432 on average. Ibrahim et al., proposed a system that extracts some features from the breast tissue of digital mammogram image. Then, the discrimination power of these features is tested to avoid using non-classifying features in order to minimize the classification error. [...]

[...] The mass lesion includes circumscribed lesions, which are compact and lobular or circular/oval shaped, and its is easy to detect than spiculated/stellate lesions which consist of a central mass (not always present) with radiating spicules in some or many directions. The most important sign of malignancy is the presence of spiculation. Due to wrong interpretation of the radiologist or because of the limitation of human visualization system certain errors like false negative errors may arise. To overcome such limitation of mammography the researchers developed Computer aided mammography abbreviated as CAM. [...]

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