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Improve methodology for tumor detection in mammograms image
Author(s): Luis Cadena
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Paper Abstract

Breast cancer is a serious and become common disease that affects thousands of women in the world each year. Early detection is essential and critical for effective treatment and patient recovery. This work gives an idea of extracting features from the mammogram image to find affected area, which is a crucial step in breast cancer detection and verification. We present the affected area identification through in which place the tumor cells are extracted directly from the grey scale mammogram image. To remove noise from the mammogram image this work presents a simple and efficient technique using fast average filter, to determine the pixel value in the noise less image. To contour detection used shearlet transform and classic filters as like Sobel, Prewitt, and others. To evaluate the quality of contour used SSIM measure. Our experimental results demonstrate that our approach can achieve the better performance in time duration of reduce noise and with shearlet transform select affected area with high efficiency.

Paper Details

Date Published: 6 September 2019
PDF: 7 pages
Proc. SPIE 11137, Applications of Digital Image Processing XLII, 1113707 (6 September 2019); doi: 10.1117/12.2528930
Show Author Affiliations
Luis Cadena, Univ. de las Fuerzas Armadas-ESPE (Ecuador)


Published in SPIE Proceedings Vol. 11137:
Applications of Digital Image Processing XLII
Andrew G. Tescher; Touradj Ebrahimi, Editor(s)

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