EVALUATION OF DUAL-SPECTRUM IR SPECTROGRAM SYSTEM ON INVASIVE DUCTAL CARCINOMA (IDC) BREAST CANCER

Author:

Lee Chia-Yen1,Chuang Ching-Cheng1,Hsieh Hsin-Yu1,Lee Wan-Rou1,Lee Ching-Yen1,Shih Shyang-Rong12,Lee Si-Chen3,Huang Chiun-Sheng4,Chang Yeun-Chung5,Chen Chung-Ming1

Affiliation:

1. Institute of Biomedical Engineering, National Taiwan University, Taiwan

2. Department of Internal Medicine, National Taiwan University Hospital, Taiwan

3. Institute of Electronics Engineering, National Taiwan University, Taiwan

4. Department of Surgery, National Taiwan University Hospital, Taiwan

5. Department of Medical Imaging, National Taiwan University Hospital, Taiwan

Abstract

Invasive Ductal Carcinoma (IDC) is one of the most frequently diagnosed breast cancers. IDC accounts for about 8 out of 10 of all invasive breast cancers. While early detection of breast cancer is essential for the reduction of death rate, there may be already more than 107 cells in a breast cancer when it can be observed by X-ray mammogram. In contrast, the passive IR spectrogram proposed by Szu et al. was shown to be promising in detecting breast cancers several months ahead of mammogram. With energy readings from two IR cameras, middle wavelength IR (MIR, 3–5 μm) and long wavelength IR (LIR, 8–12 μm), dual-spectrum IR (DS-IR) spectrogram may be computed by using the deterministic neighborhood-based blind source separation algorithm developed by Szu et al..4–7 To evaluate the performance of the DS-IR spectrogram on detection of IDC, a DS-IR spectrogram hardware system is built and a sub-pixel super-resolution registration is developed to implement the deterministic neighborhood-based blind source separation algorithm. Clinical tests have been carried out with the approval of Institutional Review Board of National Taiwan University Hospital. From August 2007 to June 2008, 35 patients aged between 30–66 (average age 49) with IDC breast cancers were recruited in this project. The results demonstrate that 62.86% of success rate for IDC detection may be achieved with the cross-sectional data. Longitudinal study shows that breast cancers may be detected more accurately by cross-referencing s1 maps of multiple time-points.

Publisher

National Taiwan University

Subject

Biomedical Engineering,Bioengineering,Biophysics

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