Adipose Distribution Analysis for Abnormal Shadow Detection

The patent segments the continuous adipose distribution curve into discrete measurement points and compares these segmented data points against a database of known cancer cases. By dividing the adipose distribution data into comparable units, the system can systematically identify patterns that correlate with malignant tumor presence, thereby establishing the reliability connection between adipose distribution and cancer probability while maintaining precise measurement capabilities.

The patent introduces an intermediary processing layer that automatically identifies local maximum points in the adipose distribution curve and generates abnormal shadow candidate indicators. This intermediary system acts as a mediator between the raw adipose measurement data and the final diagnostic interpretation, automatically performing the complex analysis of identifying characteristic points and comparing them against reference databases, thereby maintaining high detection accuracy while reducing the apparent complexity for the end user.

The patent performs preliminary actions by pre-processing and storing characteristic adipose distribution patterns from known cancer cases in a reference database before actual diagnosis. The system pre-identifies key features and local maximum points from historical cancer data, creating a ready-to-use comparison library. During actual diagnosis, the system quickly matches patient data against these pre-prepared patterns, significantly reducing the time required for analysis while maintaining reliable cancer probability indication through the established pattern matching methodology.