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Image filtering techniques for medical image post-processing: an overview

¹ Medical Vision Laboratory, Engineering Science, Oxford University, Parks Road, Oxford OX1 3PJ, ² Mirada Solutions Ltd., 23–38 Hyth Bridge Street, Oxford OX1 2EP, UK and ³ Department of Medical and Molecular Pharmacology, University of California Los Angeles, Los Angeles CA, USA

View larger version (27K): [in a new window]   Figure 1. (a) A small section of a 50 µm mammogram with microcalcifications and a vessel visible. (b) The unprocessed image displayed as a surface map. (c) The filtering of the image segment using diffusion (smoothing) techniques [10] with (d) showing the benefit using a more selective filtering approach such as a wavelet [7] which has better structure preservation.

View larger version (86K): [in a new window]   Figure 2. An example of bias field correction using "smart filters" that can detect inhomogeneities in the image [12, 13]. The left image shows an MRI slice of the colon with clear bias field artefacts. The right image has been significantly improved, both for visualization and the application of quantitative and computer-aided techniques.

View larger version (43K): [in a new window]   Figure 3. Bias field corrected images (refer to Figure 2) can have automated algorithms successfully applied to reconstruct the shape of the colon and provide an important starting point for computer-aided detection algorithms.

View larger version (34K): [in a new window]   Figure 4. Most radiologists do not consider different interpolation techniques as a filtering process, but it clearly is. The magnified image segments illustrate (from left to right) "nearest neighbour", linear and cubic spline interpolation, respectively. These images importantly illustrate how many basic visualization techniques can dramatically impact the presentation – and misrepresentation – of the true image data.

View larger version (49K): [in a new window]   Figure 5. (A) A low quality therapy planning CT with typically thick slab profile and hence poor through-plane resolution. (B) and (C) illustrate the same volume with linear and spline interpolation, respectively. In this example, the spline interpolation (usually considered to be the best method) actually generates a poorer quality re-sampled image with "ringing" artefacts clearly visible as shown in the magnified image D. Although a technical example, illustrated for clarity, caution is relevant to many filtering and re-sampling functions.

View larger version (85K): [in a new window]   Figure 6. A very nice example of physics-based filtering where a model of the image formation process of an X-ray mammography image is used to construct a special filter which can improve the noise and spatial representation of microcalcification clusters [9]. The top insert image shows how scatter and the spectral characteristics of the X-ray source have blurred the image, almost completely masking the calcifications (indicative of ductal carcinoma). The bottom insert image shows a vast improvement in the quality of the appearance of the microcalcifications.

View larger version (45K): [in a new window]   Figure 7. Classification of a mammogram into Wolfe Patterns using textons. (a) A mammogram belonging to Wolfe class N1. (b) The texton "labelled" image of the mammogram. The texton histogram corresponding to this image is used to classify the image into the pattern Wolfe N1. This type of classification forms the basis of "next generation" computer-aided detection (CADe) and diagnosis (CADi) algorithms for digital mammography.

View larger version (86K): [in a new window]   Figure 8. This figure illustrates how feature detection across a series of image frames can be used to estimate the motion of the heart. In the set of images (a), the shape and location of the endocardium and epicardium is estimated from image features over successive frames and then used to drive a deformation model [19, 20]. (b) The wall motion kinetics and thickening measurements between the sets of contours can be used to create a map of the "activity" level of the myocardium which correlates well with other modalities [20].