研究目的
To propose a dedicated technique for measuring optical nerve thickness and identifying its quality by processing front eye images at the nanoscale, with a focus on detecting neurological impairments such as epilepsy.
研究成果
The proposed AFM-based algorithm effectively detects abnormalities in optical nerve thickness and capillaries, providing a reliable method for identifying neurological disorders such as epilepsy. The SSIM metric shows statistical differences between normal and pathological images, enhancing diagnostic accuracy.
研究不足
The instrumentation and its specific software do not allow quantitative and automatic detection of capillaries close to the optical nerve, which may introduce uncertainty in measurements.
1：Experimental Design and Method Selection:
The study uses Optical Coherence Tomography (OCT) for imaging and Atomic Force Microscopy (AFM) for signal processing to analyze optical nerve thickness. A signal processing algorithm based on AFM is proposed to detect abnormalities.
2：Sample Selection and Data Sources:
A database of 10 teenagers is used for experimental measurements, involving eye images captured using OCT.
3：List of Experimental Equipment and Materials:
OCT instrumentation (specific model not mentioned), AFM for image processing, and software for data analysis.
4：Experimental Procedures and Operational Workflow:
OCT is used to capture cross-sectional images of the eye. The images are processed using an AFM-based algorithm that fits data with polynomials and analyzes features like Power Spectrum Density (PSD) and image quality metrics (MSE, PSNR, SSIM).
5：Data Analysis Methods:
Data is analyzed using PSD to identify frequency components, and image quality metrics (MSE, PSNR, SSIM) are computed to distinguish between normal and pathological cases.
独家科研数据包，助您复现前沿成果，加速创新突破
获取完整内容
