CHARACTERIZATION OF BREAST LESIONS BY PROCESSING DIGITAL BREAST IMAGES
DOI registered with Crossref
https://doi.org/10.37547/ijmsphr/Volume05Issue10-03
Keywords
Image processing
Mammography
Segmentation
Abstract
This Rendering to the World Health Organization, women in both developed and developing nations are most likely to develop breast cancer. This illness causes breast cells to grow and multiply out of control. According to research institutes and international organizations, there are various screening methods available based on age, and breast cancer can be cured if detected in time. The Breast Imaging Reporting and Data System (BIRADS) is a standardized system that is commonly used in these techniques to report results and findings. Results are sorted by BIRADS into six categories, numbered 0 through 6. Furthermore, mammography is the most widely utilized screening technique.
This study suggests using mammography data processing to identify breast lesions. Adaptive filters are used for image cropping and contrast enhancement during the pre-processing phase. The pectoral muscle is then segmented using segmentation techniques that consider morphological and area growth factors. The lesion is then divided into sections at the muscle and breast levels using the Discrete Wavelet Transform (DWT), which finds any micro calcifications. Furthermore, to distinguish between dense lesions and other kinds of lesions, an area cultivation approach combined with multiple thresholding techniques is employed. Lastly, the obtained segmentation is used to extract textural and morphological features.
When expert-segmented and automatically segmented images were compared, the Sorensen Decade similarity index was 0.73, indicating the effectiveness of the suggested method. Considering that the lesion area on a mammogram can only be roughly delineated by hand or automatically, this is a promising outcome.
zenodo DOI:- https://doi.org/10.5281/zenodo.13918417
Downloads
Download data is not yet available.
References
1
American Cancer Society. (2019). Types of Breast Cancer. Consultado el 20 de mayo de 2021 en https://www.cancer.org/cancer/breast-cancer/understanding-a-breast-cancer-diagnosis/types-of-breast cancer.html
2
Aro, A., Pilvikki, S., Van der Ploeg, E. y Van der Kamp, L. J. T. (2000). False-positive findings in mammography screening induces short-term distress - breast cancer-specific concern prevails longer. European Journal of Cancer, Vol. 36, No. 9, pp. 1089–1097.
3
Bahl, M. (2014). Detecting Breast Cancers with Mammography: Will AI Succeed Where Traditional CAD Failed. Radiology, Vol. 290, No. 2, pp. 315-316.
4
Burnside, E. S., Sickles, E. A., Bassett, L. W., Rubin, D. L., Lee, C. H., Ikeda, D. M. y D’Orsi, C. J. (2009). The ACR BI-RADS® Experience: Learning From History. Journal of the American College of Radiology, Vol. 6, No. 12, pp. 851–860.
5
Centers for Disease Control and Prevention. U.S. Department of Health & Human Services. (s/a) Breast Cancer. Consultado el 20 de mayo de 2021 en https://www.cdc.gov/cancer/breast/basic_info/what-is-breast cancer.htm
6
Contreras, W., Nora, P. y Ruiz, M. E. (2010). Procesamiento Digital de textura: Técnicas utilizadas en aplicaciones actuales de CBIR. Revista de Investigación de Sistemas e Informática. Vol. 7, No. 1, pp. 57-64.
7
D’Orsi, C.J., Sickles, E.A., Mendelson, et al. (2013). ACR BI-RADS® Atlas, Breast Imaging Reporting and Data System. Reston, VA, American College of Radiology.
8
Da Cruz, T. N., Da Cruz, T. M. y Pinheiro, W. (2018) Detection and classification of mammary lesions using artificial neural networks and morphological wavelets. IEEE Latin America Transactions, Vol. 16, No. 3, pp. 926–932.
9
Gherghout, Y., Tlili, Y. y Souici, L. (2019). Classification of breast mass in mammography using anisotropic diffusion filter by selecting and aggregating morphological and textural features. Evolving Systems. doi:10.1007/s12530-019-09270-z
10
González, R. C. y Woods, R. E. (2007) Digital Image Proccesing. Pearson Education International, pp. 976.
11
Henderson, L. M., Hubbard, R. A., Sprague, B. L. et al (2015). Increased Risk of Developing Breast Cancer after a False-Positive Screening Mammogram. Cancer Epidemiology Biomarkers & Prevention, Vol. 24, No. 12, pp. 1882–1889.
12
Henriksen, E. L., Carlsen, J. F., Vejborg, I. M. et al (2018). The efficacy of using computer-aided detection for detection of breast cancer in mammography screening: a systematic review. Acta Radiologica, Vol. 60, No. 1, pp. 13-18.
13
Kamalakannan, J., Krishna, P. V., Babu, M. R. y Mukeshbhai,K. D. (2015) Identification of abnormility from digital mammogram to detect breast cancer.International Conference on Circuits, Power and Computing Technologies, India, pp. 1-5.
14
Katzen, J. y Dodelzon, K. (2018). A review of computer aided detection in mammography. Clinical Imaging, Vol. 52, pp. 305-309.
15
Key, T. J., Verkasalo, P.K. y Banks, E. (2001). Epidemiology of Breast Cancer. Lancer Oncol, Vol. 2, No. 3, pp. 133-40.
16
Kohli, A. y Jha, S. (2017). Why CAD Failed in Mammography. Journal of the American College of Radiology, Vol. 15, No. 3, pp. 535–537.
17
Lång, K., Nergården, M., Andersson, I. et al. (2016). False positives in breast cancer screening with one-view breast tomosynthesis: An analysis of findings leading to recall, work-up and biopsy rates in the Malmö Breast Tomosynthesis Screening Trial. Eur Radiol, Vol. 26, No. 11, pp. 3899–3907.
18
Liga Colombiana contra el cáncer. (s/a) La Mamografía: "Un derecho de las mujeres para la detección temprana del cáncer de mama". Consultado el 20 de mayo de 2021 en https://www.ligacancercolombia.org/wp-content/uploads/2020
19
Löfstedt, T., Brynolfsson, P., Asklund, T., Nyholm, T. y Garpebring, A. (2019). Gray-level invariant Haralick texture features. PLOS ONE, Vol. 14, No. 2, pp. 1-18.
20
Margaliot M. (2008) Pattern recognition. (Theodoridis, S. and Koutroumbas, K.; 2006) [book reviews]. IEEE Trans Neural Network, Vol. 19, No.2, pp. 376
21
MathWorks (s/a) RegionProps. Consultado el 1 de junio de 2021 en https://www.mathworks.com/help/images/ref/regionprops.html
22
Menéndez, R., López-Potilla, B., Duarte, A. y Orea, I. (2016) Detección de masas en mamografía asistida por computadora. Revista Cubana de Informática Médica, Vol. 8, No. 3, pp. 582-594.
23
Organización Mundial de la Salud. (2018, Septiembre) Cancer. Consultado el 20 de Mayo de 2021 en https://www.who.int/news-room/fact-sheets/detail/cancer
24
Osorio, D. I. (2015) Procesamiento y análisis de mamografías para la detección de tumores. Escuela Técnica Superior de Ingeniería Informática.
25
Phung, M. T., Tin, S. y Elwood, J. M. (2019). Prognostic models for breast cancer: a systematic review. BMC cancer, Vol. 19, No. 1, pp. 230.
26
Ranchod, T. y Herndon, J. (2019) A Comprehensive Guide to Breast Cancer. Consultado el 20 de mayo de 2021 en https://www.healthline.com/health/breast-cancer
27
Reeder J.G. y Vogel V.G. (2012) "Breast cancer prevention". Advances in Breast Cancer Management, Second Edition. Springer.
28
Román, R., Sala, M., De La Vega, M. et al. (2011). Effect of false-positives and women’s characteristics on long-term adherence to breast cancer screening. Breast Cancer Res Treat, Vol. 130, No. 2, pp. 543-552.
29
Romieu I., Biessy C., Torres-Mejía G., Ángeles-Llerenas A., Sánchez G.I., Borrero M., et al. (2019). Project profile: a multicenter study on breast cancer in young women in Latin America. Salud Publica Mex. Vol. 61, No. 5, pp. 601-608.
30
Sayeed, A., Bhattacharya, P., Mudur, S. P. y Krishnamurthy, S. (2011). Local relative GLRLM-based texture feature extraction for classifying ultrasound medical images. Proceedings of the 24th Canadian Conference on Electrical and Computer Engineering, CCECE 2011, Canada.
31
Saslow, D., Hannan, J., Osuch, J., Alciati, M.H., Baines, C., Barton, M., et al. (2004) Clinical breast examination: practical recommendations for optimizing performance and reporting. CA: A Cancer Journal for Clinicians, Vol. 54, No. 6, pp. 327–344.
32
Suárez, E.D., Pérez, M. y Orozco, R. Diseño de Sistema Automatizado para Detección de Anomalías en Imágenes Digitales de Mama. Journal health med. sci. Vol. 5, No. 4, pp. 229-243.
33
Susan G. Komen ORG. (s/a). Breast Cancer Information. Consultado el 20 de Mayo de 2021 en https://www.komen.org/breast-cancer/
34
Taglicfacio, A. S., Piana, M., Schenone, D, Lai, R., Massone, A. M. y Houssami, N. (2020). Overview of radiomics in breast cancer diagnosis and prognostication. The Breast, Vol. 49, pp. 74-80.
35
Ventura-Alfaro, C. (2018). Errores de medición en la interpretación mamográfica por radiólogos. Revista de Salud Pública, Vol. 20, No. 4, pp. 518-524.
36
Weingart S.N., Saadeh M.G., Simchowitz B, Gandhi T.K., Nekhlyudov L. et al. (2009) Process of care failures in breast cancer diagnosis. Journal of General Internal Medicine, Vol. 24, No. 6, pp. 702-709.
37
Wiesner, C., Diaz, S., Sánchez, O., Puerto, D., Bravo, L.E. y Murillo, R. (2020). Políticas basadas en la evidencia científica: El caso del control del cáncer de mama en Colombia. Revista Colombiana de Cancerología, Vol. 24, No. 3, pp.103-112.
38
Zielonke, N., Gini, A., Jansen, E. E. L., Anttila, A., Segnan, N., Ponti, A. y Priaulx, J. (2020). Evidence for reducing cancer-specific mortality due to screening for breast cancer in Europe: A systematic review. European Journal of Cancer, Vol. 127, pp. 191-206.