Breast cancer segmentation based on modified Gaussian mean shift algorithm for infrared thermal images

ABSTRACT

Accurate segmentation of infrared thermal images is a challenging issue in the breast cancer detection. This paper presents and evaluates novel segmentation method for breast cancer detection using infrared thermal images. The developed segmentation method uses developed segmentation algorithm that is hybrid of Gaussian Mean Shift (GMS) and roulette wheel selection approach. In the first stage of the developed segmentation method, the redundant portions of the infrared thermal image are removed and then, the infrared thermal image is divided into five and six clusters by applying the developed segmentation algorithm. The segmented infrared thermal image is created by multiplying the selected best image of each cluster. This image is used to diagnosis the normal breast from abnormal breast. The proposed method is developed using MATLAB 2017 and infrared thermal images of 64 patients. The results show that the average Dice similarity coefficient, Jaccard index and Hausdorff distance in the proposed segmentation method are 91.81%, 84.86%, and 4.87, respectively. The comparison results demonstrate that using the proposed segmentation method can improve the performance of the Computer-Aided-Detection (CAD) system compared to the CAD systems that use the Mean Shift (MS) and Fuzzy C-Means (FCM) segmentation algorithms.

KEYWORDS:

• Breast cancer thermography
• image processing
• segmentation
• Computer-Aided-Detection (CAD) system

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Notes on contributors

Mahnoosh Zarei

Mahnoosh Zarei received the B.S. degrees from the Islamic Azad University, Kazerun Branch, kazerun, Iran, in 2016 and M.S. degrees from the ACECR institute of higher education, Isfahan branch, Isfahan, Iran,in 2019, all in biomedical engineering. her current research interests include Image Processing.

Abdalhossein Rezai

Abdalhossein Rezai is an assistant professor in Academic Center for Education, Culture and Research (ACECR), Isfahan University of Technology (IUT) branch, Isfahan, Iran. He received Ph.D. degree in electrical engineering from Semnan University, Semnan, Iran in 2013, M.S. and B.S. degree in electrical engineering from Isfahan University of technology, Isfahan, Iran in 1999, and 2003, respectively. His research interests include VLSI design, nanoelectronics, computer arithmetic, and image processing.

Seyedeh Shahrbanoo Falahieh Hamidpour

Seyedeh Shahrbanoo Fallahieh Hamidpour is a assistance professor of ACECR institute of higher education, Isfahan branch, Isfahan, Iran. She has been graduated Ph.D. Degree in Biomedical Eng., Iranian Research Organization for Science and Technology (IROST), Tehran, Iran in 2019. She received M.S. degree in Biomedical Eng. (Bioelectrical Eng.), form department of biomedical engineering, Tehran University of medical science, Tehran, Iran, in 2007.  She also received B.S. degree in Electrical Eng. (Electronics), from department of electrical engineering, Yazd University, Yazd, Iran, in 1999.Her research interests include image and signal processing and logic circuits Design.