AbstractudNuclear Medicine (NM) images inherently suffer from large amounts of noise andudblur. The purpose of this research is to reduce the noise and blur while maintainingudimage integrity for improved diagnosis. The proposal is to further improve imageudquality after the standard pre- and post-processing undertaken by a gamma cameraudsystem.udMean Field Annealing (MFA), the image processing technique used in this research isuda well known image processing approach. The MFA algorithm uses two techniquesudto achieve image restoration. Gradient descent is used as the minimisation technique,udwhile a deterministic approximation to Simulated Annealing (SA) is used forudoptimisation. The algorithm anisotropically diffuses an image, iteratively smoothingudregions that are considered non-edges and still preserving edge integrity untiluda global minimum is obtained. A known advantage of MFA is that it is able toudminimise to this global minimum, skipping over local minima while still providingudcomparable results to SA with significantly less computational effort.udImage blur is measured using either a point or line source. Both allow for theudderivation of a Point Spread Function (PSF) that is used to de-blur the image. Theudnoise variance can be measured using a flood source. The noise is due to the randomudfluctuations in the environment as well as other contributors. Noisy blurredudNM images can be difficult to diagnose particularly at regions with steep intensityudgradients and for this reason MFA is considered suitable for image restoration.udFrom the literature it is evident that MFA can be applied successfully to digitaludphantom images providing improved performance over Wiener filters. In this paperudMFA is shown to yield image enhancement of planar NM images by implementinguda sharpening filter as a post MFA processing technique.
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