Enhanced Segmentation of Inflamed ROI to Improve the Accuracy of Identifying Benign and Malignant Cases in Breast Thermogram

Abstract

Effective analysis of breast thermography needs an accurate segmentation of the inflamed region in Infrared Breast Thermal Images (IBTI) which helps in the diagnosis of breast cancer. However, IBTI suffers from intensity inhomogeneity, overlapping regions of interest, poor contrast, and low signal-to-noise ratio (SNR) due to the imperfect image acquisition process. To mitigate this, this work proposes an enhanced segmentation of the inflamed Region of Interest (ROI) using an active contour method driven by the multiscale local and global fitted image (MLGFI) model. The first phase proposes a bilateral histogram difference-based thresholding (BHDT) method for locating the inflamed ROI. This is then used for automatic initialization of active contours driven by MLGFI to segment the inflamed ROI from IBTI effectively. To prove the effectiveness of this segmentation method, its performance is compared with ground truth image and its accuracy is also evaluated with the state-of-the-art methods (Fuzzy C Means (FCM), Chan-Vese (CV-ACM), and K-means). From the analysis, it is found that the proposed method not only increases the precision and the segmentation accuracy but also reduces the oversegmentation and undersegmentation rate significantly. In the second phase, area-based feature (AF) and average intensity-based feature (AIF) along with the GLCM (gray level cooccurrence matrix) based second-order statistical features are extracted from the inflamed ROI. Based on these features, a system is developed to effectively classify the benign and malignant breast conditions. From the results, it is observed that the proposed model exhibits an improved accuracy of 91.5%, sensitivity of 91%, and specificity of 92% compared to the whole breast thermogram. Hence, it is concluded that the proposed method will improve the efficacy of thermal imaging in the diagnosis of breast cancer.

A Review on Thermal Imaging-Based Breast Cancer Detection Using Deep Learning

“…Venkatachalam et al [60] presented a method for segmenting an inflamed ROI using the active contour method. First, a thresholding method based on bilateral histogram differences was employed for localizing the inflamed ROI.…”

Breast cancer diagnosis through an optimization‐driven multispectral gamma correction (ODMGC)

“…As a result, thermography has been considered a substitute in recent research. [3][4][5] In medical settings, infrared cameras are utilised to diagnose breast-related issues by detecting thermal radiation from the breast. Interactions between the environment and heat radiation can cause problems such as scattering and reflecting.…”

“…Although mammography is accepted as the best screening method for cancer detection, it comes with a minor radiation risk of 8.9% when people over 40 get screened every 2 years, as well as discomfort from compression of the breast during imaging. As a result, thermography has been considered a substitute in recent research 3–5 …”

Breast segmentation in infrared thermography from characteristical inframammary shape

“…20 More recently, artificial intelligence algorithms, AIA, have been proposed to automate different steps involved in breast thermography analysis. 21,22 However, the automate breast thermogram analysis required the segmentation of entire breast region and an asymmetrical analysis of left and right breast region are carried out differentiating them as normal and abnormal breast, see 23 and references there are in. In addition, the segmentation isolating the Region of Interest (ROI) will reduce the computation costs for a learning algorithm, in AIA.…”