[Feature analysis of superficial soft tissue interface based on wave numbers].

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Ultrasound Technology

Background:

• Accurate characterization of superficial soft tissue interfaces is crucial for medical diagnosis.
• Existing methods for analyzing interfacial echo signals face challenges in feature extraction.
• Understanding wave number variations in echo signals can provide insights into tissue properties.

Purpose of the Study:

• To analyze the causes of wave number variation in interfacial echo signals using a deconvolution model.
• To develop a method for feature recognition of superficial soft tissue interfaces.
• To evaluate the effectiveness of the proposed method in identifying and locating tissue defects.

Main Methods:

• A simple deconvolution model was applied to multi-layer interfaces.
• Mallat multisolution analysis was used to decompose and reconstruct interfacial echo signal data.
• The number of reconstructed interface signals was utilized as a key feature.

Main Results:

• The deconvolution model proved effective in extracting interface echo signal features from superficial soft tissues.
• The number of reconstructed interface signals served as a reliable feature for analysis.
• The method demonstrated capability in identifying and locating tissue defects.

Conclusions:

• The developed deconvolution model offers a robust approach for analyzing superficial soft tissue interfaces.
• This technique enhances the feature recognition of interfacial echo signals.
• The findings support the potential application of this method in clinical settings for defect detection.