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Co-occurrence Statistical Medical Image Processing

Co-occurrence Statistical Medical Image Processing . Bradley D. Clymer Dept of Electrical & Computer Engineering Dept of Biomedical Informatics Participating Faculty in Biomedical Engineering. Outline. Brief overview of medical image research of Clymer’s group

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Co-occurrence Statistical Medical Image Processing

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  1. Co-occurrence Statistical Medical Image Processing Bradley D. Clymer Dept of Electrical & Computer Engineering Dept of Biomedical Informatics Participating Faculty in Biomedical Engineering

  2. Outline • Brief overview of medical image research of Clymer’s group • Using in vivo resolution to infer hidden data in spatial statistics -- osteoporosis, microvessels • Using statistical co-occurrence methods for multispectral medical images -- dynamic contrast MRI • Using 4-D co-occurrence techniques to process raw dynamic contrast data for tissue characterization

  3. Clymer Group Medical Image Research Activities • Image acquisition & reconstruction • Parallel channel MRI (SMASH) at ultrahigh Fields: Dr. P. Schmalbrock (MRI), P. Wassenaar (MS EE) • Electron Paramagnetic Resonance Imaging (EPRI): Dr. J. Zweier (DHLRI), Dr. Y. Deng (DHLRI), Dr. P. Kuppusamy (DHLRI), R. Ahmad (EE PhD Student) • Multimodal Data Fusion & Display • Temporal Bone Surgery Simulator: Dr. G. Wiet, (Children’s Hosp.), D Stredney (OSC), Dr. P. Schmalbrock (MRI), S Raghunathan (BME PhD Student) • Using audio/haptics for multimodal image data fusion and perception: Dr. M. Knopp, Hee Chun (ECE PhD Student).

  4. Clymer Group Medical Image Research Activities(cont.) • Directionally sensitive co-occurrence textures for tissue characterization • Osteoporosis assessment: Dr. K. Powell (CCF), Chad Showalter (ECE PhD Student), plus pending NIH grant with 4 Colleges & 6 Depts. • Microvessel volume density from ultrahigh field MRI: Dr. G. Christoferidis (MRI), Dr. P. Schmalbrock (MRI), Dr. D. Chakeres (MRI), P. Barnes (BME PhD Student) • Dynamic Contrast MRI • Parameter pseudo-image spatial co-occurrence statistics: Dr. M. Knopp (MRI), Dr. J. Heverhagen (MRI), M. Kale, (ECE PhD Student) • 4-D spatial-temporal co-occurrence statistics on raw data: Dr. M. Knopp (MRI), Dr. J. Heverhagen (MRI), Dr. T. Kurc (BMI), B Woods (ECE MS Student)

  5. Statistical Co-occurrence Image Textures (Haralick et al.) • Even when fine structures are not resolved completely, intravoxel mixing effects create statistical spatial patterns • Haralick’s methods can be directionally sensitive – can sense isotropy/anisotropy of statistics in image data • Microvessels: long tubes with diameters smaller than voxel (pixel) size. • Trabeculae: sheets and rods of calcified tissue with cross sections smaller than voxel (pixel) size.

  6. Statistical Co-occurrence Image Textures (cont.) • Haralick’s general approach: • Build a co-occurrence matrix (joint histogram) of nearest neighbor values in a specific direction • Generate a group of moments and entropies to characterize local joint statistics in the given neighbor directions • Use combination of calculated moments and entropies to classify local image content • Have been used since 1973 on 2-D imagery, more recently on 3-D, we are extending to 4-D with parallel cluster computing approaches

  7. Example from bone imaging • We used mCT images of bone core samples (calcaneus): • Simulated blurring to in vivo CT resolution by local averaging of mCT data • Calculated directionally sensitive co-occurrence textures on blurred images • Compared texture outcomes as predictors for morphometric parameters (accepted standard) using linear regression

  8. Texture parameters vs Morphometric parameters at different resolutions

  9. Next phase of bone imaging (pending NIH R21/R33 proposal) • Obtain in situ/vivo images from human cadavers and live thoroughbred horse remodeling with exercise and atrophy • Establish translational model to bone mechanics • Compare in situ/vivo CT & MRI via directional textures with • Morphometric measures from high resolution images after excision • Mechanical testing, localization of fracture sites • Participants: ECE/BME/BMI: Clymer (PI); Radiology: M Knopp, P Schmalbrock; BME/Orth Surg: A. Litsky; Veterinary Science: A. Bertone; Mech Eng: M. Dapino, OSC: D. Stredney

  10. Co-occurrence Processing of Dynamic Contrast MRI – 4 approaches • Spatial pattern textures of diffusion model parameter pseudo-images (k21, kel, Amp) • Use co-occurrence technique to assess parameter combination statistics and moments and entropies similar to textures at matched points in k21-image, kel-image, Amp-image • Combine methods 1 & 2 • Use raw 4-D DCE MRI data and calculate co-occurrence textures in specific directions through hyperspace, build classifier without using diffusion model parameters

  11. Approach 1 (spatial textures of parameter pseudoimages) • John David Fleig (MSEE, MSCIS 2003) • Joel Saltz, BMI • Tahsin Kurc, BMI • Bradley Clymer, ECE, BMI

  12. Approach 2 (pixelwise co-occurrence across different parameter pseudo-images) • Mehmet Kale (MSEE 2004, ECE Phd Student) • Johannes Heverhagen, Radiology • Bradley Clymer, ECE, BMI

  13. Approach 4 (4-D Image texture classification of raw DCE image sequences) • Brent Woods (ECE MS Student) • Tahsin Kurc, BMI • Johannes Heverhagen, Radiology • Bradley Clymer, ECE, BMI

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