A dynamic infrared imaging-based diagnostic process for breast cancer

Abstract

Background: Dynamic infrared (IR) imaging is an emerging functional imaging modality for the detection of breast cancer without evidence of optimal imaging and diagnostic application. Purpose: To evaluate dynamic IR imaging in breast cancer diagnostics by comparing a stepwise diagnostic scheme to digital mammography and postoperative histopathology. Material and Methods: Dynamic IR imaging of breasts was undertaken preoperatively with a long-wave quantum well (QWIP) and two mid-wave photovoltaic (PV) IR cameras in 10 cases (age 34–80 years) with breast cancer size 6–45 mm on mammography. Image stabilization, two-phase frequency analysis, and two image-processing algorithms were applied. Results: Combining image processing with frequency analysis proved advantageous in detecting breast cancer. The IR imaging process recognized the cancer area independently of tissue density, cancer size, and cancer appearance on mammography. Compared to histopathology, all cancers yielded abnormal analysis results, including one case of ductal carcinoma in situ. Evidence of lymphatic invasion in postoperative histopathology, imaging with PV camera, and image processing with the Wiener filtering combination correlated with highest confidence between normal and cancer tissue measured by the calculated superiority value. Conclusion: Dynamic IR imaging with image-processing-guided frequency analysis is a promising modality for breast cancer detection and may not have the tissue-dependent limitations of mammography. Our results encourage further work on medical IR imaging and comparison to established breast-imaging modalities.