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Lesion Classification by Model-Based Feature Extraction: A Differential Affine Invariant Model of Soft Tissue

Weiguo Cao¹, Marc J Pomeroy^2,3, Zhengrong Liang^4,5

¹Department of Radiology, Stony Brook University, Stony Brook, NY, 11794, USA. george.wg.cao@gmail.com.

Journal of Imaging Informatics in Medicine

|August 20, 2024

Summary

This study introduces a novel method for classifying lesions using computed tomography (CT) by modeling soft tissue elasticity. This approach enhances diagnostic accuracy for conditions like colon polyps and lung nodules.

Keywords:

Affine transformation Elastic deformation Invariant characteristics Lesion classification Tissue elasticity

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Area of Science:

Medical Imaging
Biophysics
Machine Learning

Background:

Soft tissue elasticity is crucial for differentiating healthy tissues from lesions.
Existing elasticity imaging modalities (ultrasound, MRI, optical coherence) directly measure elasticity.
Computed tomography (CT) lacks direct elasticity measurement capabilities.

Purpose of the Study:

To propose an alternative method for modeling soft tissue elasticity using CT imaging.
To extract tissue elastic characteristic features for machine learning (ML)-based lesion classification.
To improve the accuracy of lesion classification by incorporating elastic properties.

Main Methods:

Developed a model describing dynamic non-rigid soft tissue deformation on a differential manifold.
Formulated a local deformation invariant using 1st and 2nd order derivatives of volumetric CT images.
Generated elastic feature maps and extracted tissue elastic features for ML classification.

Main Results:

Achieved an area under the curve (AUC) of 94.2% for colon polyp classification.
Achieved an AUC of 87.4% for lung nodule classification.
Demonstrated an average performance gain of 5-20% over existing state-of-the-art methods.

Conclusions:

The proposed modeling strategy effectively extracts tissue elastic features from CT images.
Incorporating elastic features significantly improves lesion classification accuracy compared to image features alone.
This approach offers a valuable tool for prior knowledge-based lesion characterization in medical imaging.