Automated analysis for scintigraphic evaluation of gastric emptying using invariant moments

This study introduces a method for automated analysis of the standard solid-meal gastric emptying test. The purpose was to develop a diagnostic tool to characterize more reproducibly abnormalities of solid-phase gastric emptying. The processing of gastric emptying is automated using geometrical moments that are invariant to scaling, rotation, and shift. Twenty subjects were studied. The first step was to obtain images of the stomach using a nuclear gamma camera immediately after the subject had eaten a radio-labeled meal. The second step was to process and analyze the images by a recently developed automated gastric emptying analysis (AGEA) method, which determines the gastric contour and the geometrical properties include such parameters as area, centroid, orientation, and moments of inertia. Statistical tests showed that some of the moments were sensitive to the patient's gastric status (normal versus abnormal). The difference between the normal and abnormal patients became noticeable approximately 1 h after meal ingestion.     

Least squares approach in measurement-dependent filtering for selective medical images

An image-processing method called measurement-dependent filtering has been introduced to improve the SNR (signal-to-noise ratio) of selective images produced by various medical imaging systems. The basic algorithm involves the combination of the low-frequency information of the selective image with the high-frequency information of a nonselective image. A spatially variant control function modulates the amount of high frequency to be added at each point. A least-mean-square (LMS) control function formed from two basis images, namely the high-passed versions of the nonselective image (M(b)) and the selective image (S(b)), is introduced. The original algorithm is now viewed as a two-stage filtering method, including the low-pass filtering noise reduction and least squares filtering for the edge restoration. An appropriate linear transformation is used to convert the original basis images M(b) and S(b) into a new pair with orthogonal noise. This allows the implementation of the LMS and control function with practically obtainable a priori knowledge.