COMPUTER AIDED DIAGNOSIS SYSTEM FOR ANALYING BREAST MAMMOGRAPHY
DOI:
https://doi.org/10.24867/15BE01RadonjicKeywords:
image processing, machine learningAbstract
This paper proposes a CAD tool based upon traditional computer vision algorithms that facilitates radiological analyses during the interpretation of mammographic images.
References
[1] Rafael Gonzalez: Digital Image Processing
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[4] Y. Kim, Contrast enhancement using brightness preserving bi-histogram equalization
[5] Y. Wan, Q. Chen, and B. Zhang, Image enhancement based on equal area dualistic sub-image histogram equalization method
[6] K. S. Sim, C. P. Tso, and Y. Tan, Recursive sub-image histogram equalization applied to gray-scale images
[7] M. Kim and M. G. Chung, Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement
[2] Adaptive Histogram Equalization – Wikipedia
[3] Efficient Contrast Enhancement using Adaptive Gamma Correction and Cumulative Intensity Distribution
[4] Y. Kim, Contrast enhancement using brightness preserving bi-histogram equalization
[5] Y. Wan, Q. Chen, and B. Zhang, Image enhancement based on equal area dualistic sub-image histogram equalization method
[6] K. S. Sim, C. P. Tso, and Y. Tan, Recursive sub-image histogram equalization applied to gray-scale images
[7] M. Kim and M. G. Chung, Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement
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Published
2021-11-06
Issue
Section
Electrotechnical and Computer Engineering
