glam-radiomics 1.2.7

Gray Level Affinity Metrics (GLAM) for quantitative medical image analysis.

GRAY LEVEL AFFINITY METRICS - GLAM RADIOMICS

This project introduces and implements a novel class of quantitative imaging features, termed Gray Level Affinity Metrics (GLAM), derived from the foundational principles of statistical mechanics. By treating image voxels as interacting particles within a multi-component system, the GLAM framework provides a set of physically interpretable metrics that characterize the spatial organization and texture of 3D medical images.

The GLAM framework is written in Python and leverages high-performance libraries for medical image analysis. It operates as a fully standalone extraction engine, meaning it does not require external radiomics packages to compute conventional texture matrices.

Installing from GitHub

You can install GLAM-radiomics using the following command:

pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ glam-radiomics

or

pip install glam-radiomics

Note: This release (v1.2.6) was successfully built and tested using Python 3.12.10 and NumPy 2.3.2.

GPU Acceleration (Optional but Recommended): > GLAM features automatic GPU-accelerated matrix batching for massive speed improvements. To enable this, you must install CuPy. It is highly recommended to install the pre-compiled binary wheel that matches your system's NVIDIA CUDA version (e.g., pip install cupy-cuda12x) rather than running pip install cupy, which requires a complex C++ build environment and can cause installation failures.

Key Dependencies

When you install GLAM, the following core libraries are automatically integrated:

• NumPy & SciPy: Provide the computational backbone for RDF calculations, spatial KD-trees, and Statistical Mechanics descriptors.
• SimpleITK: Handles the loading and normalization of 3D medical imaging formats like NIfTI (.nii.gz).
• Pandas: Manages the structured output of multiscale Radial Distribution Functions and feature aggregation.
• Scikit-image & Scikit-learn: Powers the morphological marching cubes (surface area), K-Means clustering, and advanced geometric descriptors.
• tqdm: Used to display progress bars during long-running feature extraction operations.

Usage Guide

Configuration (config.ini)

GLAM uses a .ini file to standardize extraction parameters across different datasets. This ensures reproducibility, which is critical for multi-center studies.

A typical configuration file is structured as follows:

# Note: configparser keys are case-insensitive, but values preserve case.

[System]
# Number of parallel patient folders to process simultaneously.
# CRITICAL: If using GPU Acceleration (CuPy), this dictates how many 
# matrices are loaded into VRAM. 
# Recommendation for 4GB-8GB GPUs: NumWorkers = 2 to 4
# Recommendation for CPU-only: NumWorkers ~ 2 * Number of CPU Cores
NumWorkers = 4

[GLAM_Settings]
MaxRdfRadius = 100
AnisotropyCutoffRadius = 5
MaxLocalShellRadius = 15
NumRandomisations = 4
RdfSamplePoints = 100

# QuantizationMethod can be "FixedCount" (for MRI) or "FixedWidth" (for CT)
QuantizationMethod = FixedCount

# --- FixedCount Parameters ---
NumGrayLevels = 24

# --- FixedWidth Parameters ---
BinWidth = 25
QuantizationMin = -1000
QuantizationMax = 1000

[File_Naming]
# Segmentation file identifiers.
MaskIdentifiers = ["_seg.nii.gz", "-seg.nii.gz", "_mask.nii.gz", "-mask.nii.gz"]

SequenceIdentifiers = {
    "FLAIR": ["_flair", "-flair"],
    "T2": ["_t2", "-t2"],
    "T1": ["_t1", "-t1"],
    "T1c": ["_t1ce", "-t1ce", "_t1gd", "-t1gd", "_t1c", "-t1c"]
    }

[Label_Mapping]
# JSON keys need to be strings ("1", "99").
LabelMapping = {
    "1": "Enhancing_Tumor",
    "2": "Non_Enhancing_Tumor_Core",
    "4": "Peritumoral_Edema",
    "99": "Whole_Tumor"
    }

LabelsForAnalysis = {
    "99": "Whole_Tumor"
    }

[Algorithm_Parameters]
SavgolWindow = 7
SavgolPoly = 3
PeakProminence = 4

Basic Extraction Script

The following script demonstrates how to initialize the global configuration and process a cohort of scans.

import os
from glam_radiomics.config import load_config
from glam_radiomics.run import process_scans

# 1. Define your project paths
CONFIG_PATH = "path_to/config.ini"
INPUT_DIR = "path_to/mri_scans"
OUTPUT_DIR = "path_to/results"
PROJECT_NAME = "My_GLAM_Project"

def main():
    # 2. Load the configuration globally
    load_config(CONFIG_PATH)

    # 3. Ensure the output directory exists
    if not os.path.exists(OUTPUT_DIR):
        os.makedirs(OUTPUT_DIR)

    # 4. Execute the analysis
    process_scans(
        INPUT_DIR,
        OUTPUT_DIR,
        project_name=PROJECT_NAME,
        config_path=CONFIG_PATH
    )

if __name__ == '__main__':
    main()

Batch Processing Multiple Scans

For multi-center studies or large cohorts, you can configure a script to loop through multiple project directories. This approach utilizes parallel processing defined by NumWorkers in your config.ini.

import os
from glam_radiomics.config import load_config
from glam_radiomics.run import process_scans

CONFIG_PATH = "path/to/config.ini"

# Define project batches (Project Name, Input, Output)
projects_to_run = [
    ("Cohort_A", "C:/data/cohort_a_scans", "C:/results/cohort_a"),
    ("Cohort_B", "C:/data/cohort_b_scans", "C:/results/cohort_b")
]

def main():
    load_config(CONFIG_PATH)

    for proj_name, input_path, output_path in projects_to_run:
        if not os.path.exists(output_path):
            os.makedirs(output_path)

        process_scans(input_path, output_path,
                      project_name=proj_name,
                      config_path=CONFIG_PATH)

if __name__ == '__main__':
    main()

File and Folder Organization

To ensure the pipeline pairs the correct masks with the correct sequences, structure your input directory as follows:

C:\...\Research\GLAM\scans\   <-- Your 'INPUT_DIR'
├── PT011901\
│   ├── PT011901_t1.nii.gz
│   ├── PT011901_t2.nii.gz
│   └── PT011901_mask.nii.gz
├── PT011902\
│   ├── PT011902_t1.nii.gz
│   ├── PT011902_flair.nii.gz
│   └── PT011902_seg.nii.gz
└── ...