Investigation of smoothing techniques for positron emission tomography imaging

Recently, many techniques have been introduced to improve on the reconstruction and enhancement of biomedical images, in particular, positron emission tomography. Often these techniques either perform poorly in the presence of noise or are computationally expensive. Nonintensive computational techniques are presented that aim to reduce noise while preserving edges working in conjunction with the filtered convolution back-projection method of reconstruction. First we present an adaptive windowing technique that bases window size on the count value. Then a maximum likelihood technique is developed. Finally, a method of image enhancement based on directional operators and template matching attempts to diminish noise while preserving edges. These techniques are applied both in the image domain and in the sinogram domain and then evaluated using a similarity measure against the original Shepp-Logan phantom.