A new method for thresholding and gradient optimization at different tissue interfaces using class uncertainty

Yinxiao Liu; Punam K. Saha

doi:10.1117/12.811355

27 March 2009 A new method for thresholding and gradient optimization at different tissue interfaces using class uncertainty

Yinxiao Liu, Punam K. Saha

Author Affiliations +

Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 72590H (2009) https://doi.org/10.1117/12.811355
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

The knowledge of thresholding and gradient at different tissue interfaces is of paramount interest in image segmentation and other imaging methods and applications. Most thresholding and gradient selection methods primarily focus on image histograms and therefore, fail to harness the information generated by intensity patterns in an image. We present a new thresholding and gradient optimization method which accounts for spatial arrangement of intensities forming different objects in an image. Specifically, we recognize object class uncertainty, a histogram-based feature, and formulate an energy function based on its correlation with image gradients that characterizes the objects and shapes in a given image. Finally, this energy function is used to determine optimum thresholds and gradients for various tissue interfaces. The underlying theory behind the method is that objects manifest themselves with fuzzy boundaries in an acquired image and that, in a probabilistic sense; intensities with high class uncertainty are associated with high image gradients generally indicating object/tissue interfaces. The new method simultaneously determines optimum values for both thresholds and gradient parameters at different object/tissue interfaces. The method has been applied on several 2D and 3D medical image data sets and it has successfully determined both thresholds and gradients for different tissue interfaces even when some of the thresholds are almost impossible to locate in their histograms. The accuracy and reproducibility of the method has been examined using 3D multi-row detector computed tomography images of two cadaveric ankles each scanned thrice with repositioning the specimen between two scans.

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Yinxiao Liu and Punam K. Saha "A new method for thresholding and gradient optimization at different tissue interfaces using class uncertainty", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72590H (27 March 2009); https://doi.org/10.1117/12.811355

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KEYWORDS

Tissues

Computed tomography

Image segmentation

3D image processing

Magnetic resonance imaging

Interfaces

Error analysis

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