AI Article Synopsis
- * Current methods have limitations in accuracy, prompting research into better techniques, particularly by analyzing speckle patterns in OCT images.
- * The study found that local brightness fluctuations from wavelet analysis of OCT data improve the differentiation of glioma from healthy brain tissue, suggesting this approach could enhance neurosurgical diagnostics.
Article Abstract
Application of optical coherence tomography (OCT) in neurosurgery mostly includes the discrimination between intact and malignant tissues aimed at the detection of brain tumor margins. For particular tissue types, the existing approaches demonstrate low performance, which stimulates the further research for their improvement. The analysis of speckle patterns of brain OCT images is proposed to be taken into account for the discrimination between human brain glioma tissue and intact cortex and white matter. The speckle properties provide additional information of tissue structure, which could help to increase the efficiency of tissue differentiation. The wavelet analysis of OCT speckle patterns was applied to extract the power of local brightness fluctuations in speckle and its standard deviation. The speckle properties are analysed together with attenuation ones using a set of ex vivo brain tissue samples, including glioma of different grades. Various combinations of these features are considered to perform linear discriminant analysis for tissue differentiation. The results reveal that it is reasonable to include the local brightness fluctuations at first two wavelet decomposition levels in the analysis of OCT brain images aimed at neurosurgical diagnosis.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11087587 | PMC |
| http://dx.doi.org/10.1038/s41598-024-61292-z | DOI Listing |
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