Chest X-Ray Opacity Detection Using a Normal Image Model

The patent segments the abnormality detection task into distinct functional components: image acquisition, preprocessing (noise reduction, contrast enhancement), feature extraction (edge detection, texture analysis), and abnormality identification. This segmentation allows each component to be optimized independently and implemented using simpler, more accessible technologies rather than requiring a single complex machine learning system.

The patent introduces intermediate processing steps between raw image input and final abnormality detection, including preprocessing operations and feature extraction algorithms. These intermediaries transform the raw image data into a form that is more suitable for analysis, bridging the gap between simple image acquisition and complex abnormality identification without requiring sophisticated end-to-end machine learning systems.

The patent performs preliminary actions during the system setup and model creation phase, including collecting representative X-ray images, preprocessing them, and establishing reference models of normal and abnormal conditions. This preliminary work is done once during system development, allowing the operational system to function with simpler, more accessible algorithms that compare patient images against these pre-established models, thereby improving reliability without requiring complex real-time training capabilities.

The patent creates reference models by copying and analyzing numerous normal X-ray images to establish baseline characteristics of healthy anatomy. These reference models serve as templates for comparison, allowing the system to reliably identify abnormalities by detecting deviations from the copied normal patterns, thereby achieving high reliability through simple comparison operations rather than complex training algorithms.