SIDF: A Novel Framework for Accurate Surgical Instrument Detection in Laparoscopic Video Frames

Background and Objectives: Identification of surgical instruments in laparoscopic video images has several biomedical applications. While several methods have been proposed for accurate detection of surgical instruments, the accuracy of these methods is still challenged high complexity of the laparoscopic video images. This paper introduces a Surgical Instrument Detection Framework (SIDF) for accurate identification of surgical instruments in complex laparoscopic video frames. Methods: Based on the Generalized Near-Set Theory, a novel image segmentation algorithm, termed Generalized Near-Set Theory-based Image Segmentation Algorithm (GNSTISA) was developed. According to SIDF, first GNSTISA is executed to segment the laparoscopic images. Next, the segments generated by GNSTISA are filtered based on their color and texture. The remaining segments would then indicate surgical instruments. Findings: Using the laparoscopic videos of varicocele surgeries obtained from Hasheminezhad Kidney Center, the performance of GNSTISA was compared with previous image segmentation methods. The results showed that GNSTISA outperforms the earlier algorithms in term of accurate segmentation of laparoscopic images. Moreover, the accuracy of SIDF in identifying the surgical instruments was found superior to that of other methods. Conclusions: SIDF eliminates the limitations of previous image segmentation methods, and can be used for precise identification of surgical instrument detection.