Depth-resolved texture mapping in focus-extended optical coherence microscopy using gray-level co-occurrence matrix

光学相干层析成像 光学 连贯性(哲学赌博策略) 人工智能 光学(聚焦) 计算机视觉 计算机科学 纹理(宇宙学) 镜头(地质) 判别式 图像分辨率 特征(语言学) 显微镜 材料科学 样品(材料) 基质(化学分析) 分辨率(逻辑) 可视化 图像纹理 焦点深度(构造) 理论(学习稳定性) 特征提取 表征(材料科学) 显微镜 模式识别(心理学) 稳健性(进化)
作者
Dasuni Wimalachandra,Amila Dhanushka Karunanayaka,Nipun Shantha Kahatapitiya,Gehan Sathsara Vithanage,Mansik Jeon,Bhagya Nathali Silva,Ruchire Eranga Wijesinghe
出处
期刊:Optics and Laser Technology [Elsevier BV]
卷期号:204: 116693-116693
标识
DOI:10.1016/j.optlastec.2026.116693
摘要

Optical coherence microscopy (OCM) is a powerful extension of Optical Coherence Tomography (OCT) that provides en-face images with exceptional microscopic lateral resolution. However, the limited depth of focus (DOF) of OCM constrains the ability to resolve structural features across multiple layers of a specimen. Although conventional approaches achieve a high lateral resolution near the focal plane, the resolution degrades over extended depth ranges, limiting their effectiveness for precise structural differentiation. To overcome this tradeoff, an optically deviated focusing incorporated focus-extended OCM (FE-OCM) system was developed to improve the DOF, while preserving a lateral resolution of 2.19 µm. This work demonstrates that the proposed scheme provides significant performance gains compared to the constant focal length baseline with respect to intensity. We analyze image intensity at depths 600–850 µm, 850–1100 µm, 1100–1350 µm, and 1350–1600 µm, revealing improved DOF at deviating lens distances 2 mm, 1.5 mm, 1 mm and, 0.5 mm respectively, establishing the proposed system’s superiority in variable depth focusing. Using these optimized focusing distances improved the stability and discriminative capability of depth-resolved texture features, enabling more consistent GLCM-based characterization across different sample layers. The technical advancement of the developed method was further verified by incorporating the grey-level co-occurrence matrix (GLCM), which relatively characterizes en-face and subsurface texture patterns, enhancing multi-depth feature differentiation. This proposed method significantly enhances system performance, delivering high-resolution, depth-extended imaging and a robust comparative framework for texture differentiation, thereby establishing a powerful pathway for precise, depth-aware imaging and analysis in advanced biomedical and material applications.
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