Detection of heterogeneity in multi-spectral transmission image based on spatial pyramid matching model and deep learning

摘要

The absorption and scattering effects of light source during the transmission of biological tissues make it difficult to identify heterogeneity in multi-spectral images. This paper firstly proposes a combination method of modulation-demodulation-frame accumulation technique (MDFAT), spatial pyramid matching (SPM) model and deep learning to realize heterogeneous detection in multi-spectral images. Firstly, the acquisition experiment of phantom image is designed. Then, the high-quality multi-spectral images are obtained by the MDFAT and SPM model. Finally, the pseudo-color maps of high-quality multi-spectral images fusion are served as the input of Faster-RCNN and Single Shot Multi-Box Detector (SSD) network models to realize heterogeneous detection. The results show that Faster-RCNN and SSD both have good detection results. Among them, Faster-RCNN model has the best detection effect on the images containing three types of heterogeneity, and the mean average precision (mAP) reaches 93.91%. SSD model has the most ideal detection effect for the images containing two and five types of heterogeneity, with mAP reaching 94.16% and 94.78% respectively. In conclusion, this paper has verified the feasibility of detecting heterogeneities in multi-spectral images through deep learning network (Faster-RCNN and SSD), which will promote the clinical application of multi-spectral transmission imaging in early screening of breast tumors.