A hybrid method based region of interest segmentation for continuous wave terahertz imaging

Terahertz (THz) imaging is an innovative technology in many areas such as security, biology and hidden targets detection. Region of interest (ROI) segmentation is essential for applications of THz imaging. However, THz images are often severely degraded by motion blur, poor resolution and severe noises. In this paper, a hybrid ROI segmentation method is proposed for continuous wave (CW) THz images. The method combines block matching 3D denoising (BM3D), fuzzy c-means (FCM) clustering, morphology operation and canny edge detection. The hybrid method is applied to a THz image of pattern plate to segment the letters. The letters are accurately segmented with accuracy, sensitivity and specificity of 94.3%, 91.6% and 95.5% respectively. Moreover, a THz image of ex vivo rat brain is taken. The tumor area is preciously segmented using this hybrid ROI segmentation method with accuracy, sensitivity and specificity of 95.6%, 84.5% and 97.7% respectively. It is suggested that the proposed hybrid ROI segmentation method performs well for CW THz images and even THz biological images.