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J A Newell

Publications and source records attributed to J A Newell.

10 recordsLinked to original sources
PubMed1990

An objective computer system for the quantification of artery stenoses.

We have developed a low cost, clinically usable system for the objective assessment of the severity of coronary artery stenoses from single view angiograms. The system is based on a desktop computer with incorporated frame grabber. Images are captured by means of a video camera. The user selects a region of interest which encompasses the stenosis. Facilities are provided for automatic or manual definition of the artery centre line and edges. The computer then calculates the artery diameter and cross-sectional area by videodensitometry along profile lines which are orthogonal to the long axis of the artery. These results can be expressed numerically as a percentage stenosis when compared to a normal region of the artery. The image is corrected for geometric distortion using a grid test object. The image grey scale is corrected by means of a ramp test object such that a pixel value is proportional to the attenuator thickness. The ramp is placed on the patient during the X-ray examination and an iterative technique has been developed for subtracting the underlying structures from the superimposed ramp image. The system has been assessed using test objects constructed in Perspex which simulate arteries of known cross-sectional area and stenoses of known severity.

Algorithms↗
PubMed1988

Medical images and automated interpretation.

The application of automated interpretation to medical images is discussed and the main methods of medical imaging are briefly described. The factors behind the process of human clinical interpretation are also considered. Because human interpretation can be aided by processing of the raw image, the standard methods of image processing are mentioned. Automated interpretation of images in other fields is relevant, and segmentation of images is an important initial part of the process. There are already some applications of automated interpretation of medical images. Some are more complete than others. This will undoubtedly be an important developing field of work which will draw on experience from other areas, and be spurred on by the increasing complexity of medical imaging methods and shortage of expertise for human interpretation.

Artificial Intelligence↗
PubMedDate not supplied

Data representation for subsequent image interpretation.

Radiological scans acquired using either the X-ray CT or the NMR imaging techniques provide a wealth of information about tissue behaviour under that imaging modality and contrast agent. To reason about the image in an interpretation stage the scans have to be converted from a pixel by pixel representation to a symbolic form. The technique used by us to generate such a description is region-based segmentation. Each region refers to a pixel or group of pixels having a common attribute. This work has provided a quantitative measure for the partial evaluation of the segmentation which can be applied independent of attribute or combination of attributes. From our initial studies of the behaviour of CT scans a precept for segmentation was developed. The segmentation employs a one-to-one map as an adaptive mechanism. The segmentation criterion at each point in the image therefore depends on the value at the corresponding point in the map. Any process can be used to generate this map, and so easily utilizes new operators as they are developed.

Artificial Intelligence↗
PubMedDate not supplied

A knowledge-based system paradigm for automatic interpretation of CT scans.

The interpretation of X-ray CT scans is a task which relies on specialized medical expertise, comprising anatomical, modality-dependent, non-visual and radiological knowledge. Most medical imaging techniques generate a single scan or sequence of two-dimensional scans. The radiologist's experience is gained by interpreting two-dimensional scans. The more complex three-dimensional anatomical knowledge becomes significant only when non-standard slice orientations are used. Hence, implicit in the radiologist's knowledge is the appearance of anatomical structures in standard two-dimensional planes, transverse, sagittal and coronal. That is, position with respect to both a coordinate reference system and other structures; intensity ranges for tissue types; contrast between structures; and size within the slices. Further to this, neurological landmarking is used to establish points of reference, i.e. more easily identifiable structures are first found and subsequent hypotheses are formed. With this in mind we have developed a knowledge-based system paradigm that partitions an image by applying the domain-dependent knowledge necessary (1) to set constraints on region-based segmentation and (2) to make explicit the expectation of the appearance of the anatomy under the imaging modality for use in the region grouping phase. This paradigm affords both expectation- and event-driven segmentation by representing grouping knowledge as production rules.

Algorithms↗