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Tissue Characterization by Image Analysis for Diagnostic Purposes João Sanches jmrs@ist.utl.pt Institute for Systems and Robotics Instituto Superior Técnico
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Noise Different Biomedical Image Modalities present different types of noise, e.g., additive or multiplicative. Usually, the noise should be removed. CT MRI LSFCM US
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Speckle Processed by José Seabra (Biomedical PhD student) Ultrasound images usually present low quality (low SNR) Images are corrupted by speckle noise (multiplicative)
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Medical Information However, the speckle pattern contains relevant medical information, e.g., fatty Liver.
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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.
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Decomposition Noisy Synthetic Image Noiseless Image Noise Field
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Classification Noise estimation Denoising Textural features Intensity and anatomical features Ultrasound Image Tissue characterization and Diagnosis
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Decomposition Examples
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“Noise” Analysis for Diagnostic Purposes Two Cases Liver Steatosis Atherosclerotic Plaques (carotids and coronaries)
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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
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Steatosis Comparison with histological data
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Steatosis Characterization Intensity Decay (m) Texture (E v,E h )
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Diagnosis Processed by Ricardo Ribeiro (Biomedical PhD student)
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Atherosclerotic Plaques
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3D Diagnosis Global and local analysis
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IVUS
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Collaboration with the Centre de Visió per Computador, Universitat Autònoma de Barcelona, Profª Petia Radeva
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IVUS IVUS Image decomposition
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IVUS Automatic classification GT
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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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