Detection of Abnormal Blood Flow Region Based on Near Infrared Correlation Spectroscopy

Blood flow measurement of microvessels in human tissues is of vital importance for the diagnosis and treatment of many diseases. In this paper, the detection method of abnormal blood flow regions based on near-infrared correlation spectroscopy is studied. We used the NL-Bregman-TV imaging algorithm to realize Blood flow imaging. However, due to the limitation of the number and distribution of detectors, the pixels obtained from images are extremely low, which cannot meet the practical requirements of the visual and the abnormal blood flow range measurement. In this paper, the bicubic interpolation method is used to improve the resolution of low-pixel blood flow images. The parameter index of the normalized similarity was proposed to help judge the effect of the interpolation method on the resolution of this kind of image. Aiming at the extraction of abnormal regions, a threshold segmentation algorithm based on the histogram difference method and a morphological processing algorithm is proposed to extract the contour of abnormal blood flow. The method proposed in this paper can be used to accurately locate and extract the clear and smooth contour of abnormal blood flow.