Endoscope Image Processing for Mucosa Detection Efficiency

The patent applies preliminary action by performing image quality assessment and foreign body detection before the observation phase. The system automatically evaluates each captured image to determine whether it contains sufficient mucosa and lacks excessive foreign bodies, pre-filtering the image set before user observation. This preliminary classification resolves the contradiction by reducing the volume of images requiring detailed review while preserving all potentially diagnostic images.

The patent applies segmentation by dividing the image evaluation process into distinct stages: first assessing overall image quality and mucosa sufficiency, then separately detecting foreign bodies and their proportions. This multi-stage segmentation allows systematic filtering criteria to be applied at each level, efficiently reducing the image pool while maintaining comprehensive lesion detection capability.

The patent segments the image analysis into separate functional modules: one module evaluates mucosa quality and sufficiency, another module detects and quantifies foreign bodies, and a final module performs lesion detection. This segmentation allows foreign body considerations to be integrated without overwhelming complexity, as each module handles a specific aspect independently. The modular approach resolves the contradiction by systematically incorporating foreign body analysis while maintaining manageable processing complexity.

The patent introduces an intermediary evaluation stage that assesses foreign body presence and proportion before final lesion detection. This intermediary step acts as a mediator between raw image data and lesion identification, preventing false detections by filtering out images where foreign bodies dominate. The intermediary layer resolves the contradiction by adding necessary foreign body consideration without directly complicating the core lesion detection algorithm.