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.