Skip to content

IBSI 2 Compliance: Convolutional Filters

Overview

The Image Biomarker Standardisation Initiative Chapter 2 (IBSI 2) focuses on standardizing convolutional image filters for radiomics. This page documents Pictologics' compliance with IBSI 2 Phase 1: technical validation using digital phantoms.

Important

Pictologics implements 3D filters and 3D radiomic features only.

The library is designed specifically for volumetric medical imaging analysis (CT, MRI, PET). 2D slice-by-slice processing is not supported as it loses critical spatial information needed for accurate radiomic feature extraction.

How to Run the Benchmarks

1. Download the Data

The IBSI 2 reference datasets (digital phantoms) are available on the IBSI GitHub repository.

  • Digital Phantoms: Download the phantom NIfTI files (e.g., checkerboard.nii.gz, impulse_response.nii.gz) from the ibsi_2_validation folder.

Place these files in a local directory (e.g., data/ibsi2/) to run the benchmarks.

2. Run Configurations Programmatically using RadiomicsPipeline

You can run IBSI 2 filter configurations programmatically using the RadiomicsPipeline class.

from pictologics import RadiomicsPipeline

pipeline = RadiomicsPipeline()

# Define an IBSI 2 Gabor filter configuration
# (Gabor filter, orthogonal rotation invariant, 3D)
gabor_config = [
    # 1. IBSI 2 Preprocessing
    {"step": "resample", "params": {"new_spacing": (1.0, 1.0, 1.0), "interpolation": "cubic"}},
    {"step": "round_intensities", "params": {}},
    {"step": "resegment", "params": {"range_min": -1000, "range_max": 400}},

    # 2. Apply Gabor Filter (Phase 1 Validation)
    {"step": "filter", "params": {
        "type": "gabor",
        "sigma_mm": 5.0,
        "lambda_mm": 4.0, 
        "gamma": 0.5,
        "rotation_invariant": True,
        "pooling": "average"
    }},

    # 3. Extract Intensity Features from the response map
    {"step": "extract_features", "params": {"families": ["intensity"]}}
]

pipeline.add_config("ibsi2_gabor_demo", gabor_config)

# Run on an image
results = pipeline.run("path/to/phantom.nii.gz", config_names=["ibsi2_gabor_demo"])
print(results["ibsi2_gabor_demo"])

Note

The example above shows a Gabor filter configuration. This is just one example. You can configure any IBSI 2 compliant filter (Mean, LoG, Laws, Wavelet, etc.) similarly. For full specifications of filter parameters, please refer to the Image Filtering guide and the IBSI 2 Reference Manual.

Phase 1 Results

Filter Performance Overview

Test Filter Phantom Error % Time Memory Status
1.a.1 Mean checkerboard 0.00% 2ms 1.0MB ✅ PASS
1.a.2 Mean checkerboard 0.00% 2ms 1.0MB ✅ PASS
1.a.3 Mean checkerboard 0.00% 2ms 1.0MB ✅ PASS
1.a.4 Mean checkerboard 0.00% 2ms 1.0MB ✅ PASS
1.b.1 Mean (2D) impulse_response - - - ⏭ SKIP
2.a LoG impulse_response 0.00% 9ms 3.0MB ✅ PASS
2.b LoG checkerboard 0.03% 11ms 3.0MB ✅ PASS
2.c LoG (2D) checkerboard - - - ⏭ SKIP
3.a.1 Laws impulse_response 0.00% 2ms 2.0MB ✅ PASS
3.a.2 Laws impulse_response 0.00% 54ms 4.0MB ✅ PASS
3.a.3 Laws impulse_response 0.00% 54ms 4.0MB ✅ PASS
3.b.1 Laws checkerboard 0.00% 2ms 2.0MB ✅ PASS
3.b.2 Laws checkerboard 0.00% 50ms 4.0MB ✅ PASS
3.b.3 Laws checkerboard 0.00% 53ms 4.0MB ✅ PASS
3.c.1 Laws (2D) checkerboard - - - ⏭ SKIP
3.c.2 Laws (2D) checkerboard - - - ⏭ SKIP
3.c.3 Laws (2D) checkerboard - - - ⏭ SKIP
4.a.1 Gabor impulse_response 0.27% 26ms 16.5MB ✅ PASS
4.a.2 Gabor impulse_response 0.14% 394ms 20.6MB ✅ PASS
4.b.1 Gabor sphere 0.01% 31ms 34.8MB ✅ PASS
4.b.2 Gabor sphere 0.09% 956ms 49.3MB ✅ PASS
5.a.1 Daubechies 2 impulse_response 0.00% 3ms 3.0MB ✅ PASS
5.a.2 Daubechies 2 impulse_response 0.00% 68ms 6.0MB ✅ PASS
6.a.1 Coiflet 1 sphere 0.00% 3ms 3.0MB ✅ PASS
6.a.2 Coiflet 1 sphere 0.00% 76ms 6.0MB ✅ PASS
7.a.1 Haar checkerboard 0.00% 114ms 6.0MB ✅ PASS
7.a.2 Haar checkerboard 0.00% 117ms 6.0MB ✅ PASS
8.a.1 Simoncelli checkerboard 0.38% 10ms 24.3MB ✅ PASS
8.a.2 Simoncelli checkerboard 0.00% 11ms 24.3MB ✅ PASS
8.a.3 Simoncelli checkerboard 0.00% 10ms 24.3MB ✅ PASS
9.a Riesz-LoG impulse_response 0.05% 14ms 14.4MB ✅ PASS
9.b.1 Riesz-LoG sphere 0.64% 13ms 14.4MB ✅ PASS
9.b.2 Riesz-LoG (aligned) sphere - - - ❗ REF.
10.a Riesz-Simoncelli impulse_response - - - ❗ REF.
10.b.1 Riesz-Simoncelli pattern_1 0.79% 13ms 24.3MB ✅ PASS
10.b.2 Riesz-Simoncelli (aligned) pattern_1 - - - ❗ REF.

Tolerance Criteria

All tests use the IBSI 2 standard tolerance: 

max_difference ≤ 0.01 × (reference_max - reference_min)

Known Deviations

2D Filters Not Implemented (5 Tests Skipped)

Note

Design Decision: 3D Volumetric Processing Only

Pictologics implements only 3D convolutional filters and only 3D radiomic features only. This is a deliberate design choice for clinical radiomics workflows with volumetric medical imaging data (CT, MRI, PET scans).

The following tests are intentionally skipped because they require 2D filter implementations: Test 1.b.1 (Mean Filter 2D), Test 2.c (LoG 2D), and Tests 3.c.1-3.c.3 (Laws 2D).

Structure Tensor Alignment (Reference Missing)

The following tests are currently not implemented because they require structure tensor alignment and the IBSI 2 reference dataset does not contain the corresponding validity response maps:

  • Tests 9.b.2, 10.b.2: Riesz Filter Alignment (ValidCRM missing)
  • Test 10.a: Riesz-Simoncelli Alignment (ValidCRM missing)

Warning

Reference Data Unavailable

The official IBSI 2 reference dataset does not contain the reference validity maps for these tests (9_b_2-ValidCRM.nii, 10_a-ValidCRM.nii, 10_b_2-ValidCRM.nii). Therefore, these tests cannot be validated and are marked as ❗ REF. (Reference Missing).

Summary

In total, 28 tests passed validating all core 3D filter functionality. 5 tests were skipped as they relate to 2D filters which are not applicable to this 3D-focused library. 3 tests are marked as missing reference (structure tensor alignment) because the validation data is not provided by IBSI.