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Enhanced Display Tool for High-Dynamic-Range Medical Imaging Using Bilateral Filtering

This paper discusses a software tool designed for the enhanced display of high-dynamic-range (HDR) medical images through bilateral filtering techniques. It emphasizes the importance of contrast enhancement for effective clinical diagnostics. The tool aims to provide interactive image manipulation capabilities, utilizing various strategies such as histogram windowing, global and local equalization, and unsharp masking. The study also examines the clinical efficacy of image enhancement methods, demonstrating their potential improvement in diagnostic scenarios, such as detection tasks in mammography.

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Enhanced Display Tool for High-Dynamic-Range Medical Imaging Using Bilateral Filtering

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  1. A Software Tool for the Enhanced Display ofHigh-Dynamic-Range Medical Images using Bilateral Filtering Joshua Stough COMP 238 December 17, 2002

  2. Display of HDR Images Requires Contrast Enhancement Goals: -Build a tool for the interactive, enhanced display of HDR medical images. -Use bilateral filtering [Durand, Dorsey 2002]. Why: -Opine on the usefulness of BF. -Clinical diagnostic tool.

  3. Outline • Introduction • Clinical Apps • Global Schemes • Histogram Windowing, GHE • Local Schemes • Unsharp Masking, BF, AHE, CLAHE, SHAHE • Studies of diagnostic efficacy • Demo

  4. Contrast Enhancement • Contrast Enhancement – not optional • Mapping: Recorded Intensities  Display Scale • Optimal – w/r to characteristics useful to clinicians • Example: segmentation, texture

  5. Histogram Windowing • Idea: Limited Footprint • Problems: • Distinct footprints • Isoboundries move with windowing

  6. Global Histogram Equalization • Mapping: recorded i gets mapped to its rank percentile of the maximum displayable intensity. • Result: peaks are dedicated a larger range. • Problem – contextual region too large.

  7. Unsharp Masking • New image is linear combination of background and detail (difference with original). • Key: background image • Example uses BF for background image 

  8. Bilateral Filtering • Two Gaussians: • One in the spatial domain (higher weights to closer pixels) • Other in the intensity domain (giving higher weights to pixels of intensity similar to i).

  9. Adaptive Histogram Equalization • Square contextual region, local histogram equalization • Problems • Not all regions are equal • Shadowing at sharp high-contrast boundaries

  10. Contrast Limited AHE • Clip histogram peaks • Redistribute cutoff pixels • Display by rank of pixel w/r to redistributed histogram

  11. Sharpened AHE • Simply, Unsharp Masking followed by CLAHE

  12. Clinical Efficacy of Image Enhancement • Seems obvious, but proof is rare • Study One: Portal film field isocenter correction • UNC, Duke. SHAHE. (first try, unpublished, CLAHE) • Conclusion: Enhancement makes bad a little better • Study Two: Simulated mass detection in dense mammograms, enhanced with IW or CLAHE • Conclusion: Compared to nothing (linear mapping) • IW slightly better • CLAHE no different

  13. Bibliography • Hemminger BM, S Zong, KE Muller, CS Coffey, MC DeLuca, RE Johnston, ED Pisano (2001). Improving the Detection of Simulated Masses in Mammograms through two Different Image-Processing Techniques, Academic Radiology, 8 : 845-855. • Pizer SM, Hemminger BM, Johnston, “Display of Two Dimensional Images”, in Image-Processing Techniques for Tumor Detection, edited by Strickland. 2002 Marcel Deckker, Basel, Switzerland. ISBN 0-8247-0637-4. • Rosenman J, CA Roe, R Cromartie, KE Muller, SM Pizer (1993). Portal Film Enhancement: Technique and Clinical Utility. I. J. Radiation Oncology, 25 (2): 333-338.

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