Bilateral Symmetry-Based Abnormality Detection in Breast Thermograms Using Textural Features of Hot Regions

With an increase in the number of breast cancer cases worldwide, there is an urgent need to develop techniques for early abnormality detection. Thermography is known for its potential to detect breast abnormalities at an early stage. A novel threshold-based non-machine learning asymmetry analysis using textural features is proposed for breast abnormality detection. Breast abnormalities are indicated by regions of elevated temperatures (hot regions), usually, indicated by red color in thermograms. In this work, the breast thermograms are segmented to extract breast tissue profiles and then the red-plane of an RGB thermogram is utilized to analyze the natural contralateral symmetry between the left and right breast of an individual. A novel textural feature based on histogram similarity along with known textural features, such as fractal dimension, hurst exponent, spectral norm, and Frobenius norm, are used as features for asymmetry analysis. Bilateral ratios (BRs) of these features indicate contralateral symmetry between the left and right breast. A BR value closer to 1 indicates such symmetry. Hard voting is done among all the BRs of the textural features to estimate asymmetry between the left and right breast and detect an individual with breast abnormality. The proposed methodology is evaluated on publicly available datasets. It outperforms the state-of-the-art and achieves an accuracy of 96.08%, sensitivity of 100%, and specificity of 93.57%. A comparative analysis of statistical and textural features has also been demonstrated. A novel singular value decomposition (SVD)-based abnormal breast detection technique has been proposed with evaluations on a limited dataset.