Hyperspectral imaging for intraoperative brain tumor identification through fusion of spectral, textural, and spectral index features

Abstract

Brain tumor is a common neurological surgical disease, where surgical resection is the primary treatment method. Neurosurgeons need to accurately determine the location of the tumor during tumor resection surgery, but existing clinical tumor identification technologies face numerous challenges, such as high equipment costs, long processing times, a certain degree of invasiveness, and insufficient image clarity. In this work, we propose a hyperspectral image detection algorithm based on the fusion of multiple features to maximize the determination of tumor boundaries. The algorithm establishes the machine learning models of Support Vector Machine (SVM) and Random Forest (RF) by integrating data features from optimal wavelengths, spectral indices, and textural features. Experimental results show that on different datasets, the classification accuracy of the three-feature fusion model is significantly higher than that of models using only two features or a single feature. Hyperspectral tumor image recognition can effectively help distinguish the tumors from the surrounding tissue, thereby enhancing the safety and thoroughness of tumor surgery.