Fibrous Dysplasia Characterization Using Lacunarity Analysis.

Area of Science:

• Medical Imaging
• Radiology
• Biomedical Engineering

Background:

• Fibrous dysplasia (FD) is a bone disorder where normal marrow is replaced by fibro-osseous tissue.
• It commonly affects adolescents and young adults, often causing maxillofacial deformities and asymmetry.
• Accurate diagnosis is essential for appropriate treatment planning.

Purpose of the Study:

• To analyze the texture patterns of fibrous dysplasia using computed tomography (CT).
• To evaluate the efficacy of lacunarity analysis in characterizing FD texture.
• To differentiate FD from normal bone tissue based on texture patterns.

Main Methods:

• Computed tomography (CT) imaging was utilized to acquire bone samples.
• Lacunarity analysis, a multiscale spatial dispersion method, was applied to texture patterns.
• Principal component analysis (PCA) and decision trees were used for statistical analysis and classification.

Main Results:

• Fibrous dysplasia samples exhibited lower lacunarity values compared to normal bone.
• This indicates a more homogeneous texture in FD-affected bone.
• High separability was achieved between FD and normal bone using PCA and decision trees.

Conclusions:

• Lacunarity analysis is a valuable tool for characterizing fibrous dysplasia texture on CT images.
• Texture analysis can effectively differentiate FD from normal bone.
• This approach supports improved diagnostic accuracy for fibrous dysplasia.