Bio-Photonics Light Scattering Dataset for AI-Based Tissue Imaging
Description
🔬 Research Hypothesis This research hypothesizes that light propagation in biological tissues follows predictable scattering patterns governed by Monte Carlo-based photon transport models. By simulating photon interactions within a biological medium, a synthetic dataset can be generated to support AI-driven medical imaging applications, such as optical coherence tomography (OCT) and diffuse optical tomography. The dataset explores how different optical properties—scattering coefficient, anisotropy, and absorption—affect photon propagation and whether AI models can learn to differentiate between scattering profiles. 📊 Key Findings The dataset consists of 2000 grayscale images, each representing the intensity distribution of photons in a simulated biological medium. Photon propagation follows expected physical principles, with intensity patterns matching theoretical models of light transport in tissues. Key observations include the effect of anisotropic scattering, where high anisotropy values lead to forward-directed light propagation, while increased scattering coefficients result in greater lateral diffusion. Shorter wavelengths (450 nm) show stronger scattering and shallower penetration, whereas longer wavelengths (780 nm) experience lower scattering and deeper penetration, consistent with real tissue optics. Absorption plays a critical role, reducing photon intensity as light penetrates deeper into the simulated medium. The structured variation in light intensity ensures that AI models can be trained to predict tissue properties from scattering patterns. 📖 How to Interpret and Use This Dataset Each image represents a Monte Carlo photon transport simulation, where photons undergo multiple scattering and absorption events. The brightness of each pixel corresponds to photon concentration, with brighter areas indicating regions of higher intensity. Researchers can analyze images by filtering them based on optical properties, such as wavelength or scattering coefficient. AI models can use the dataset for training in tissue classification and reconstruction of subsurface structures. The dataset is also valuable for validating Monte Carlo-based light transport models by comparing the generated images to experimental optical imaging data. 📌 Applications and Use Cases This dataset is applicable to biomedical optics, AI-driven medical imaging, and laser-tissue interaction studies. It provides training data for AI models used in OCT and laser scanning microscopy. In physics and engineering, it supports the study of light transport in scattering media. For AI research, the dataset enables training deep learning models to classify tissue structures based on scattering properties. It is also useful for developing AI algorithms that reconstruct subsurface tissue features from optical measurements.
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🔬 1. Methodology: How the Data Was Generated The dataset was generated using Monte Carlo simulation of photon propagation in biological tissues. This approach accurately models light scattering, absorption, and transmission in biological materials. 1.1 Monte Carlo Light Scattering Model The Monte Carlo method is a probabilistic approach that simulates the random movement of photons in a medium. Each photon undergoes multiple scattering and absorption events, following the Henyey-Greenstein phase function, which models real-world light-tissue interactions. 1.2 Simulation Assumptions & Physics Models The following physical models were used: ✅ Photon Transport Model → Simulates photon interactions in a homogeneous biological medium. ✅ Henyey-Greenstein Phase Function → Governs anisotropic scattering (how much light is scattered forward). ✅ Beer-Lambert Law → Models absorption of photons as they penetrate deeper into the tissue. ✅ Random Walk (Brownian Motion) → Determines randomized photon movement in scattering layers. 2. Workflow & Protocols The following workflow was followed to generate the dataset: Step 1: Define Optical & Simulation Parameters Number of photons per simulation: 5000 photons Number of images generated: 2000 images Image resolution: 256 × 256 pixels Tissue optical properties were randomized per image: Wavelengths (nm): 450, 532, 650, 780 (Blue, Green, Red, Near-Infrared) Scattering Coefficients (cm⁻¹): 0.5 - 2.5 Anisotropy Factors: 0.7 - 0.99 Absorption Coefficients (cm⁻¹): 0.1 - 1.5 Step 2: Implement Monte Carlo Light Scattering Simulation Each photon was assigned a randomized scattering direction using the Henyey-Greenstein function. Each photon moved through the medium, experiencing random absorption and scattering. The final photon density distribution was stored as grayscale intensity values. Step 3: Convert Photon Density Maps into Images The simulated photon density was normalized between 0-255 grayscale intensity. The images were saved in PNG format for easy processing in AI/ML applications. Step 4: Store Metadata for Each Image A metadata CSV file was generated, logging: Image path Wavelength used Scattering coefficient Anisotropy factor Absorption coefficient Step 5: Data Verification & Quality Control Histogram Analysis → Ensured proper light intensity distribution. Heatmap Visualization → Verified light scattering shape. Radial Intensity Decay Analysis → Checked if scattering followed expected Gaussian/exponential trends.