Scatterer size-based analysis of optical coherence tomography images using spectral estimation techniques

Area of Science:

• Biomedical optics
• Medical imaging analysis
• Spectral signal processing

Background:

• Optical Coherence Tomography (OCT) is a powerful imaging modality.
• Accurate scatterer size estimation and tissue classification are crucial for OCT applications.
• Existing spectral analysis methods may require prior sample information.

Purpose of the Study:

• To develop and validate a novel spectral analysis technique for OCT images.
• To enable scatterer size estimation and tissue classification using OCT spectral data.
• To assess the performance of statistical analysis methods in OCT image interpretation.

Main Methods:

• Utilized spectral analysis based on Optical Coherence Tomography (SOCT) autoregressive spectral estimation.
• Applied two statistical analysis methods: variance analysis with prior information and k-means clustering without prior information.
• Tested the technique on OCT images from tissue phantoms.

Main Results:

• The novel spectral analysis technique demonstrated effectiveness in scatterer size estimation.
• Successful classification of dissimilar areas in phantoms and tissues was achieved.
• Sensitivity and specificity exceeded 90% for classification tasks.

Conclusions:

• Spectral content of OCT signals is a viable parameter for scatterer size estimation.
• The developed technique offers high accuracy for classifying different tissue types and areas.
• This approach shows significant potential for enhancing OCT-based diagnostics.