Preprocessing fast filters and mass segmentation for mammography images

Yuliana Jiménez Gaona; M. J. Rodríguez-Álvarez; Jimmy Freire; Darwin Castillo; Vasudevan Lakshminarayanan

doi:10.1117/12.2593939

1 August 2021 Preprocessing fast filters and mass segmentation for mammography images

Yuliana Jiménez Gaona, M. J. Rodríguez-Álvarez, Jimmy Freire, Darwin Castillo, Vasudevan Lakshminarayanan

Proceedings Volume 11842, Applications of Digital Image Processing XLIV; 1184213 (2021) https://doi.org/10.1117/12.2593939
Event: SPIE Optical Engineering + Applications, 2021, San Diego, California, United States

Abstract

Digital mammography is a valuable technique for breast cancer detection, because it is safe, noninvasive and can reduce unnecessary biopsies. However, it is difficult to distinguish masses from normal or dense regions because of their morphological characteristics and ambiguous margins. Thus, improvement of image quality, highlighting the tissues details and performing mass segmentation are important tasks for early breast cancer diagnosis. This work presents a mini-Mammographic Image Analysis Society (MIAS) database preprocessing, system which combines classic and efficient techniques of Median, Wiener and Gaussian filters to remove salt and pepper, speckle and gaussian noise in mammography images. The experimental results indicates that the Gaussian filter outperforms other filtering techniques, as shown by evaluated by Peak Signal to Noise Ratio and Mean Square Error metrics.

Conference Presentation

Citation Download Citation

Yuliana Jiménez Gaona, M. J. Rodríguez-Álvarez, Jimmy Freire, Darwin Castillo, and Vasudevan Lakshminarayanan "Preprocessing fast filters and mass segmentation for mammography images", Proc. SPIE 11842, Applications of Digital Image Processing XLIV, 1184213 (1 August 2021); https://doi.org/10.1117/12.2593939

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