Tensor-scale-based diffusive filtering of medical images

Punam K Saha

doi:10.1117/12.535676

Medical imaging techniques often suffer from low signal-to-noise ratio (SNR)/signal-to-contrast ratio (CNR). Filtering is an effective technique commonly used to improve the SNR/CNR. A fundamental difficulty in filtering is its smearing effect on important structures along with the suppression of noise. Anisotropic diffusion attempts to partially arrest smoothing across boundaries, which are recognized by high gradients. Improvements to this approach were demonstrated by bringing local "object scale" into the framework that effects intense filtering inside homogeneous regions while constricting the process inside regions containing fine details. A major drawback of previously used spherical object scale concept is its failure to recognize structural orientation and anisotropy. Therefore, the method failed to smooth along an edge or a thin elongated structure. Recently, the notion of tensor scale (in short, t-scale)-a local morphometric descriptor-has been introduced that provides a unified description of local structure size, orientation, and anisotropy. At any point its t-scale is the parametric representation of the largest ellipse (ellipsoid in 3D) centered at that point and contained inside the same homogeneous region. In this paper, a new anisotropic diffusive filtering method is presented, wherein at any location, along any direction, the degree of filtering is governed by t-scale that restricts filtering across boundaries and fine structures while encouraging smoothing along edges or elongated structures. Results of applications of the proposed method on several phantoms and real medical images are presented and its effectiveness is evaluated by comparing it with both spherical scale-based and ordinary diffusive filtering.

Tensor-scale-based diffusive filtering of medical images

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