Binarized Image Analysis for Gleason Grade Estimation

The patent replaces the manual visual examination process (mechanical system of pathologist observation) with an automated image analysis system using computer algorithms. The system processes histological images through multiple binarization operations, calculates topological features (Betti numbers), and applies machine learning models to automatically determine Gleason grades, thereby eliminating time consumption and inter-reader variability while maintaining diagnostic accuracy.

The patent transforms the continuous visual assessment process into discrete parameter-based analysis by converting histological images into binarized forms with different reference values, extracting specific topological parameters (Betti numbers representing hole-shaped and connected regions), and using these quantified parameters as input for classification. This parameter transformation enables automated processing while preserving the essential diagnostic information.

The patent replaces the subjective human judgment process with an objective automated system. The image analysis algorithm consistently applies the same processing steps (binarization, feature extraction, classification) to all images, eliminating the variability inherent in different pathologists' interpretations while maintaining the diagnostic accuracy established by expert review.

The patent segments the diagnostic process into distinct, standardized steps: image binarization with multiple reference values, topological feature extraction (calculating Betti numbers for hole-shaped and connected regions), statistical analysis, and machine learning classification. This segmentation creates a reproducible workflow that eliminates inter-reader variability by ensuring every image undergoes the exact same analysis sequence.

The patent enhances the analysis by introducing topological dimensions through Betti number calculations. Instead of relying solely on conventional image features, the system analyzes the topological structure of binarized images by counting hole-shaped regions (one-dimensional Betti numbers) and connected regions (zero-dimensional Betti numbers). This topological dimension provides additional discriminative power that improves accuracy while maintaining full automation.

The patent combines multiple analysis approaches into a composite system: conventional image processing (binarization with multiple reference values), topological analysis (Betti number calculation), statistical analysis, and machine learning classification. This composite approach integrates the strengths of different methods, achieving both high automation and high accuracy that neither approach could achieve alone.