Automated Breast Boundary Segmentation to Improve the Accuracy of Identifying Abnormalities in Breast Thermograms

Abstract

Breast thermography is a non-invasive, painless, affordable, and safer method of detecting breast abnormalities. Thermal imaging techniques used for detecting breast anomalies rely on precise breast boundary segmentation. However, the segmentation accuracy of the breast boundary region is affected by unclear boundaries, a low signal-to-noise ratio, and poor contrast of the thermal images. To mitigate this, this paper proposes a novel Distance-based Metrics and High-Temperature Region-based Adaptive Thresholding (DM-HTRAT) method for breast boundary region segmentation and dissecting left and right breasts precisely. In the first section, the Upper Region Boundary (URB) and the intersection point of the breast are segmented using the Distance-based Metrics (DM) method. In the second section, a High-Temperature Region-based Adaptive Thresholding (HTRAT) method is used to segment the Lower Region Boundary (LRB). The result of the segmentation is analysed both quantitatively and qualitatively, also it is compared with the state-of-the-art methods. The proposed method has a significantly better accuracy of 96.5% than the other methods. This segmented image is then fed into the abnormality detection system developed via the asymmetrical analysis of segmented breast boundary regions. The proposed DM-HTRAT-based breast boundary segmentation is robust in segmenting all types of breast thermal images and aids in the more reliable and effective detection of breast abnormalities.

Keywords:

• Breast abnormality detection
• Breast boundary segmentation
• Breast cancer
• Breast thermal imaging
• Breast thermogram
• Breast thermography
• Image segmentation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Nirmala Venkatachalam

Nirmala Venkatachalam received her BE in electronics and communication engineering from Sriram Engineering College, affiliated to Anna University, Chennai, India, an ME degree in computer science and engineering from Annamalai University, India and a PhD in medical image processing from Anna University, Chennai, India. She is currently working as assistant professor in the Department of Artificial Intelligence and Data Science, at Easwari Engineering College, Chennai, India. Her current research interest includes medical image processing, pattern recognition, and machine learning.

Leninisha Shanmugam

Leninisha Shanmugam received the BE degree in computer science and engineering from Madurai Kamaraj University, Tamil Nadu, Madurai, India, an ME degree in computer science and engineering and PhD from the Faculty of Information and Communication Engineering, Anna University, Chennai, India. Currently, working as assistant professor (Sr Grade) in the School of Computer Science and Engineering, Vellore Institute of Technology (VIT University). Her research interests are feature extraction in remote sensing images, pattern recognition, and invariants for object recognition. Email: [email protected]

C. Heltin Genitha

C Heltin Genitha received the BTech degree in information technology from Manormanium Sundaranar University, India, and the ME degree in computer science and engineering from Annamalai University, India, and doctorate in satellite image processing from Anna University, India, in 2014. Currently working as a professor in the Department of Information Technology, St Joseph's College of Engineering, Chennai, India. Her field of interest includes satellite image processing, super resolution mapping, neural networks and genetic algorithms. Email: [email protected]

Selva Kumar

Selva Kumar is senior assistant surgeon, Department of Radiotherapy, Thanjavur Medical College and Dr MGR Medical University. He received the MBBS degree from Stanley Medical College and DMRT from Madras Medical College. His field of interest is radiation oncology. Email: [email protected]