Mammography is a critical tool in breast cancer screening and diagnosis, where the accuracy of image interpretation plays a vital role in detecting abnormalities. This study presents a novel approach to preprocessing mammography images aimed at enhancing diagnostic accuracy. Traditional preprocessing methods often fall short in addressing various challenges such as noise, contrast variations, and artifacts, which can impede the effectiveness of image analysis. Our proposed method incorporates advanced image enhancement techniques, including adaptive histogram equalization, noise reduction algorithms, and edge-preserving filters, to improve the overall quality of mammographic images.

We applied our preprocessing framework to a dataset of mammograms and conducted a comparative analysis against standard preprocessing techniques. Metrics such as signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and visual quality assessments were employed to evaluate the effectiveness of our method. The results indicate a significant improvement in image clarity and detail retention, facilitating better visualization of critical structures and potential lesions.

Furthermore, we implemented machine learning algorithms to assess the impact of our preprocessing method on diagnostic performance. The classification accuracy of trained models showed marked improvement when using our enhanced images compared to those processed by conventional techniques. This study underscores the potential of our novel preprocessing approach to improve the reliability of mammographic interpretations, thereby contributing to more effective breast cancer screening and diagnosis. Future work will focus on refining the method and exploring its applicability across diverse imaging modalities.

Mammography, image preprocessing, diagnostic accuracy

National Cancer Institate of Canada: Canadian Cancer Statistics. Toronto, Canada, 2003.

Hoyer, A., Spiesberg, W.: Computerized mammogram processing. In: Phillips Technical Review. Volume 38. (1979) 347-355

Lau, T., Bischoff, W.: Automated detection of breast tumors using the asymmetry approach. In: Computers and Biomedical Research. Volume 24. (1991) 273-295.

Masek, M., Attikiouzel, Y.: Skin-air interface extraction from mammograms using an automatic local thresholding algorithm. In: ICB, Brno, CR (2000) 204-206.

Yin, F., Giger, M.: Computerized detection of masses in digital mammogram: analysis of bilateral subtraction images. In: Medical Physics. Volume 28. (1991) 955-963.

Bick, U., Giger, M.: Automated segmentation of digitized mammograms. In: Academic Radiology. Volume 2. (1995) 1-9.

Byng, J., Boyd, N.: Automated analysis of mammographic densities. In: Medical Physics. Volume 41. (1996) 909-923.

Hein, J., Kallargi, M.: Multiresolution wavelet approach for separating the breast region from the background in high resolution digital mammography. In: Digital Mammography, Nijmegen, Kluwer Academic Publishers (1998) 295-298.

Semmlow, J., Shadagopappan, A.: A fully automated system for screening xero-mammograms. In: Computers and Biomedical Reseach. Volume 13. (1980) 350-362.

Mendez, A., Tahoces, P.: Automatic detection of breast border and nipple in digital mammograms. In: Computer Methods and Programs in Biomedicine. Volume 49. (1996) 253-262.

Zhou, C., Chan, H.: Computerized image analysis: Estimation of breast density on mammograms. In: Med. Phys. Volume 28. (2001) 1056- 1069.

Abdel-Mottaleb, M., Carman, C.: Locating the boundary between the breast skin edge and the background in digitized mammograms. In: Digital Mammography. (1996) 467-470.

Morton, A., Chan, H., Goodsitt, M.: Automated model-guided breast segmentation algorithm. In: Med Phys. (1996) 1107-1108.

Karssemeijer, N., te Brake, G.: Combining single view features and asymmetry for detection of mass lesions. In: IWDM. (1998) 95-102.

Stomatakis, E., Cairns, A.: A novel approach to aligning mammograms. In: Digital Mammography. (1994) 255-364.