Singular value based characterization and analysis of thermal patches for early breast abnormality detection.

The purpose of this study is to develop a novel breast abnormality detection system by utilizing the potential of infrared breast thermography (IBT) in early breast abnormality detection. Since the temperature distributions are different in normal and abnormal thermograms and hot thermal patches are visible in abnormal thermograms, the abnormal thermograms possess more complex information than the normal thermograms. Here, the proposed method exploits the presence of hot thermal patches and vascular changes by using the power law transformation for pre-processing and singular value decomposition to characterize the thermal patches. The extracted singular values are found to be statistically significant (p < 0.001) in breast abnormality detection. The discriminability of the singular values is evaluated by using seven different classifiers incorporating tenfold cross-validations, where the thermograms of the Department of Biotechnology-Tripura University-Jadavpur University (DBT-TU-JU) and Database of Mastology Research (DMR) databases are used. In DMR database, the highest classification accuracy of 98.00% with the area under the ROC curve (AUC) of 0.9862 is achieved with the support vector machine using polynomial kernel. The same for the DBT-TU-JU database is 92.50% with AUC of 0.9680 using the same classifier. The comparison of the proposed method with the other reported methods concludes that the proposed method outperforms the other existing methods as well as other traditional feature sets used in IBT based breast abnormality detection. Moreover, by using Rank1 and Rank2 singular values, a breast abnormality grading (BAG) index has also been developed for grading the thermograms based on their degree of abnormality.

Designing of Ground-Truth-Annotated DBT-TU-JU Breast Thermogram Database Toward Early Abnormality Prediction.

The advancement of research in a specific area of clinical diagnosis crucially depends on the availability and quality of the radiology and other test related databases accompanied by ground truth and additional necessary medical findings. This paper describes the creation of the Department of Biotechnology-Tripura University-Jadavpur University (DBT-TU-JU) breast thermogram database. The objective of creating the DBT-TU-JU database is to provide a breast thermogram database that is annotated with the ground-truth images of the suspicious regions. Along with the result of breast thermography, the database comprises of the results of other breast imaging methodologies. A standard breast thermogram acquisition protocol suite comprising of several critical factors has been designed for the collection of breast thermograms. Currently, the DBT-TU-JU database contains 1100 breast thermograms of 100 subjects. Due to the necessity of evaluating any breast abnormality detection system, this study emphasizes the generation of the ground-truth images of the hotspot areas, whose presence in a breast thermogram signifies the presence of breast abnormality. With the generated ground-truth images, we compared the results of six state-of-the-art image segmentation methods using five supervised evaluation metrics to identify the proficient segmentation methods for hotspot extraction. Based on the evaluation results, the fractional-order Darwinian particle swarm optimization, region growing, mean shift, and fuzzy c-means clustering are found to be more efficient in comparison to k-means clustering and threshold-based segmentation methods.