Several algorithms are selected based on their basic tissue differentiation characteristics.
Graphical representation of the typical acoustic signature of the three tissue characterisation algorithms for malignant and non-malignant areas. Graphs (a-c) represent a malignant lesion and graphs (d-f) represent a non-malignant area. Note the difference in the y axis value between (a) and (d), (b) and (e), (c) and (f).
Individual and composite differentiation algorithms are trained on patient data sets (scan + histology) to achieve maximum statistical separation.
Comparing normal and malignant areas in the prostate resulted in different distributions of numerical patterns, with distributions related to cancerous areas (in orange) systematically shifted to the right (higher values) when compared to distributions related to the normal area (in blue).
Trained algorithms are implemented into the system to provide optimized differentiation.
Mathematical integration of the distributions provided by the three characterization algorithms allowed the definition of numerical patterns likely to be specific of non-malignant or of malignant prostatic tissues.