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Tissue Characterization by Image Analysis for Diagnostic Purposes

Tissue Characterization by Image Analysis for Diagnostic Purposes. João Sanches jmrs@ist.utl.pt Institute for Systems and Robotics Instituto Superior Técnico. Noise. Different Biomedical Image Modalities present different types of noise, e.g., additive or multiplicative.

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Tissue Characterization by Image Analysis for Diagnostic Purposes

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  1. Tissue Characterization by Image Analysis for Diagnostic Purposes João Sanches jmrs@ist.utl.pt Institute for Systems and Robotics Instituto Superior Técnico

  2. Noise • Different Biomedical Image Modalities present different types of noise, e.g., additive or multiplicative. • Usually, the noise should be removed. MRI LSFCM US CT

  3. Speckle • Ultrasound images usually present low quality (low SNR) • Images are corrupted by speckle noise (multiplicative) Processed by José Seabra (Biomedical PhD student)

  4. Medical Information However, the speckle pattern contains relevant medical information, e.g., fatty Liver.

  5. Goal • Decompose the image in textural and anatomical/morphological components. • Characterize the texture/“noise” (speckle) • Associate the texture characteristics with the disease. • Classification and Quantification to Detect (Diagnosis) and Quantify (Severity Assessment) the disease.

  6. Decomposition Noisy Synthetic Image Noiseless Image Noise Field

  7. Ultrasound Image Denoising Noise estimation Intensity and anatomical features Textural features Tissue characterization and Diagnosis Classification

  8. Decomposition Examples

  9. “Noise” Analysis for Diagnostic Purposes Two Cases • Liver Steatosis • Atherosclerotic Plaques (carotids and coronaries)

  10. Steatosis • Steatosis is mainly a textural abnormality of the hepatic parenchyma due to fat accumulation on the hepatic vesicles (genetic, alcohol and obesity) • Today, the assessment is subjectively performed by visual inspection

  11. Steatosis • Comparison with histological data

  12. Steatosis Characterization • Intensity Decay (m) • Texture (Ev,Eh)

  13. Diagnosis Processed by Ricardo Ribeiro (Biomedical PhD student)

  14. Atherosclerotic Plaques

  15. 3D Diagnosis Global and local analysis

  16. IVUS

  17. IVUS Collaboration with the Centre de Visió per Computador, Universitat Autònoma de Barcelona, Profª Petia Radeva

  18. IVUS IVUS Image decomposition

  19. IVUS Automatic classification GT

  20. Present and Future Work • New texture characterization and classification methods • Aterosclerotic plaques: 3D Extension of the IVUS • Liver steatosis quantification with additional information from laboratorial analysis data.

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