A new method based on texture analysis for the classification of automatic detection of breast microcalcifications of mammography images

Subject Areas : Biomedical Engineering

Zahra Maghsoodzadeh Sarvestani ¹ , Jasem Jamali ² , mhdi taghizadeh ³ , Mohammad h Fatehi ⁴

1 - Department of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran
2 - Department of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran
3 - Department of electrical and computer engineering, kazerun branch, islamic azad university, kazerun, iran
4 - Department of Electrical Engineering, Kazerun branch, Islamic Azad University

Received: 2023-09-30 Accepted : 2023-10-11 Published : 2023-10-13

Keywords: Texture analysis, fuzzy logic, Gabor filtering, artificial neural network (ANN), decision tree classification,

Abstract :

Mammography is a diagnostic technology used in screening programs to find breast cancer early. By using two techniques for image enhancement and highlighting breast tissue microcalcifications for the desired areas by regional ROI based on fuzzy system and also Gabor filtering method, the study's objective was to assess the viability of automatic separation of images of breast tissue microcalcifications and to assess its accuracy. The decision tree classification algorithm is used to categorize the clusters of breast tissue microcalcifications after the clusters have been identified. The samples that are thought to have microcalcification are next highlighted and masked for segmentation, and in the last step, tissue properties are extracted. Then, it was possible to distinguish between benign and malignant forms of segmented ROI clusters with the aid of an artificial neural network (ANN). The results of this work show a high accuracy of 93% and an improvement of sensitivity of 95%, which shows that the presented solution can be reliably applied to detect breast cancer..

References:

[1] American Cancer Society. QuickFacts (TM) Breast Cancer: What You Need To KnowNOW. Atlanta, American Cancer Society press. 2011.
[2] Panahi GH, Shabahang H, Sahebghalam H. Breast cancer risk assessment in Iranian women by Gail model. Medical Journal of the Islamic Republic of Iran (MJIRI) 2008; 22(1): 37-39.
[3] Caldarone, A., Piccotti, F., Morasso, C., Truffi, M., Sottotetti, F., Guerra, C., & Corsi, F. (2021). Raman analysis of microcalcifications in male breast
cancer. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 263, 120185. [4] Schulz-Wendtland R, Fuchsjäger M, Wacker T, Hermann K-P. Digital mammography: an update.Eur J Radiol, 2009; 72(2): 258-265.
[5] Wallis MG, Walsh MT, Lee JR. A review of false negative mammography in a symptomatic population. Clin Radiol. 1991; 44(1):13-5. [DOI:10.1016/S0009-9260(05)80218-1]
[6] Behnam H, Zakeri F, Gifani P, Torkashvand P, Shalbaf A, [Ultrasound Imaging Processing (Persian)]. Tehran: Ishraqiya Publishing; 2011.
[7] Jalalian A, Mashohor SB, Mahmud HR, Saripan MIB, Ramli ARB, Karasfi B. Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. Clin Imaging. 2013; 37(3):420–6.
[8] Shiraishi J, Li Q, Appelbaum D, Doi K. Computer-aided diagnosis and artificial intelligence in clinical imaging. In: Seminars in Nuclear Medicine. Elsevier; 2011. p. 449–62. https://doi.org/10.1053/j.semnuclmed.2011.06.004.
[9] Guzmán-Cabrera R, Guzmán-Sepúlveda J, Torres-Cisneros M, May-Arrioja D, Ruiz-Pinales J, Ibarra-Manzano O, Aviña-Cervantes G, Parada AG. Digital image processing technique for breast cancer detection. Int J Thermophys. 2013; 34(8-9):1519–31.
[10] Andreadis II, Spyrou GM, and Nikita KS: A CAD scheme for mammography empowered with topological information from clustered
microcalcifications atlases. IEEE J Biomed Health Inform 19(1): 166–173, 2015.