Biomedical subjects

T S Durrani

Publications and source records attributed to T S Durrani.

3 recordsLinked to original sources

PubMed1991

An automatic matching technique for patient alignment.

An automatic system of patient alignment is required in order to monitor changes that occur in the period between magnetic resonance scans. For each scan of the patient a prime requisite is to register the images with respect to each other. The orthogonal relationship between the sagittal and transverse images should, in principle, identify a single common line at the intersection of the two image planes. The basis of the comparison requires spatial registration of the two images to correct for the probable translational and rotational tilts as well as for the geometrical and intensity distortions. This paper describes a number of automatic techniques which compare, pixel-by-pixel, first two synthetic images, and then their application to real images obtained separately from the same head and neck object field. The robustness, computational cost and effectiveness of the techniques presented are discussed, and computed results on real data for the most promising technique based on the Ratio Absolute Difference algorithm are presented.

Algorithms↗

PubMed1990

Automatic chart reader--an example of computer systems for patients' data interpretation.

A new approach to the processing of circular medical charts based on "odd symmetry digital subtraction" is presented. The technique allows automatic digitisation of these charts using an image processing system based on a flat bed scanner. This approach is simple, fast, and reliable; and provides a system in which the digitised image is processed by a microcomputer to extract and quantify the plotted information. The main features and the potential applications of this approach in medical and nonmedical fields are discussed.

Algorithms↗

PubMed1990

Patient realignment in magnetic resonance imaging of the head: an algorithm using mathematical morphology for feature extraction.

To develop a technique for automatic patient realignment in magnetic resonance imaging (MRI), it is essential to extract key features automatically from the various slices of the head as accurately as possible. Such features include the brain, the brain stem, the pons, the corpus callosum and the cerebellum. A feature extraction algorithm has been developed which is based on thresholding a region to a common grey level and then applying mathematical morphology to produce a binary regular region. In addition, a region-filling algorithm has been developed to obtain the complete feature. The scans derived from the T1 spin-lattice relaxation time, which are the fastest of the MRI scans, are used in patient realignment to provide highly textured images. These are difficult to segment using conventional thresholding or edge enhancement techniques due to their 'grainy' appearance, which makes it difficult to isolate key features from the other components found in the slice. We have developed a method for the accurate extraction of the corpus callosum, the cerebellum and the brain area in a sagittal scan of the head. This is carried out by selective thresholding designed to remove the low texture content and then applying morphological techniques.

Algorithms↗