Research on Algorithm for Feature Extraction of Laryngoscope Image Distribution and Texture Fusion.

Purpose: In the context of rapid development of modern medical technology, the explosive growth of medical data has imposed a heavy diagnostic burden on professional physicians. Especially in the field of computer-assisted treatment research on laryngoscope imaging data, existing studies are still insufficient, which prompts this study to develop a new feature extraction and classification method. The purpose of this study is to improve the accuracy and efficiency of diagnosis for laryngopharyngeal reflux disease by using computer-assisted treatment technology and laryngoscope imaging data and drug treatment results. This not only has important significance in relieving the work pressure of physicians, but also has broad practical value and realistic significance.

Methods: This study utilized the laryngoscope images provided by the Department of Otolaryngology, Jilin University Second Hospital, and proposed an innovative image feature extraction method that integrates distribution features and texture features. The local binary pattern method was used to capture the texture information of the image, while the gray histogram method was used to extract the distribution characteristics of the image. This technology effectively achieved the fusion of features, and the performance of the five classic classification algorithms was compared and analyzed for the features obtained.

Conclusions: The study results show that the feature extraction method proposed in this paper, when combined with the random forest discriminant algorithm, achieves an accuracy rate of 96.61% in laryngoscope image classification, demonstrating excellent performance. Furthermore, the algorithm has low requirements on the number of samples, further proving its high efficiency and practicality in actual applications.