A computer implemented simulation and evaluation method simulates interventions to a patient by a user, and evaluates the interventions responsive to predetermined criteria and the interventions. The method includes defining a test area to evaluate the user to at least one of predetermined criteria and a user profile, selecting genetic information of the patient responsive to the test area, and generating a patient history responsive to the test area and the genetic information. The method also includes receiving at least one intervention input by the user, and evaluating the user responsive to the intervention and predetermined criteria.

Computer architecture and process of patient generation, evolution, and simulation for computer based testing system

Object. The goal of this study was to construct a model of normal changes in intracranial volume occurring throughout childhood from age 7 days to 15 years. Methods. Using the technique of segmentation on magnetic resonance images obtained in healthy children, intracranial volume was measured and plotted against age. Conclusions. Intracranial volume in the first few months of life is on average 900 cm3 in males and 600 cm3 in females. By the age of 15 years, it increases up to 1500 cm3 in males and 1300 cm3 in females, increased by factors of 1.6 and 2.1, respectively. By the time the child reaches 2 years of age, intracranial volume has reached 77% (1150 cm3 in males and 1000 cm3 in females) and, by 5 years, 90% (1350 cm3 in males and 1200 cm3 in females) of the volume observed at age 15 years. The change in intracranial volume that occurs with age is not linear, but there seems to be a segmental pattern. Three main periods can be distinguished, each lasting approximately 5 years (0–5, 5–10, and 10–15 ye...

Intracranial volume change in childhood.

A method and system for patient generation and evolution for a computer-based testing system and/or expert system. One or more belief networks, which describe parallel health state networks are accessed by a user or a computer. A knowledge base, at least in part, is scripted from the one or more belief networks by the computer. A model patient at least in part, is instantiated by the computer from the scripted knowledge base. Optionally, the model patient is evolved by the computer in accordance with the parallel health state networks and responsive to a received course of action.

Computer architecture and process of patient generation, evolution, and simulation for computer based testing system using bayesian networks as a scripting language

Automatic medical image segmentation is an unsolved problem that has captured the attention of many researchers. The purpose of this survey is to identify a representative set of methods that have been used for automatic medical image segmentation over the past 35 years and to provide an opportunity to view the transitions that have occurred as this research area has developed. To facilitate this, the existing research is divided into three generations, each generation adding an additional level of algorithmic complexity. These generations indicate progress towards accurate, fully-automatic, medical image segmentation and their identification provides a framework for classifying the wide variety of methods that have been devised. The first generation is composed of the simplest forms of image analysis such as the application of intensity thresholds and region growing. The second generation is characterized by the application of uncertainty models and optimization methods, and the third generation incorporates knowledge into the segmentation process. The progress toward accurate, fully-automatic segmentation is discussed and sources of segmentation software from industry and academia are identified, along with databases for segmentation validation.

A Review of Medical Image Segmentation: Methods and Available Software

Describes a knowledge-based image interpretation system for the segmentation and labeling of a series of 2-D brain X-ray CT-scans, parallel to the orbito-meatal plane. The system combines the image primitive information produced by different low level vision techniques in order to improve the reliability of the segmentation and the image interpretation. It is implemented in a blackboard environment that is holding various types of prior information and which controls the interpretation process. The scoring model is applied for the fusion of information derived from three types of image primitives (points, edges, and regions). A model, containing both analogical and propositional knowledge on the brain objects, is used to direct the interpretation process. The linguistic variables, introduced to describe the propositional features of the brain model, are defined by fuzzy membership functions. Constraint functions are applied to evaluate the plausibility of the mapping between image primitives and brain model data objects. Procedural knowledge has been integrated into different knowledge sources. Experimental results illustrate the reliability and robustness of the system against small variations in slice orientation and interpatient variability in the images. >

Object recognition in brain CT-scans: knowledge-based fusion of data from multiple feature extractors

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, ie more easily identifiable structures are first found and subsequent hypotheses are formed With this in mind we ha

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

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...

Data representation for subsequent image interpretation.

Cranial anatomy described in texts depicts a part of hierarchy whose divisions reflect both artificial, sometimes historical, and physical composition and positioning. We have constructed a model of normal anatomy using a frame based representation. Each frame corresponds to a single structure in the hierarchy (or tree) and gives its composition in terms of smaller structures. Differentiation of features by properties takes place at a high level through classification by tissue type. The four classes used are: solids, deformables, fluids, cavities. The base level structures are described in detail for each slice in which they occur, using relative position, shape and size. This has been done on a coarse scale over a restricted range of slices in the first instance. This is a 3-dimensional representation from which an expectation of a 2-dimensional slice, obtained from a tomographic technique, can be generated. The base level features which occur in the specified slice range are taken from the anatomical model and used to construct a dynamic current world view. This is an initial hypothesis which can be altered in light of new information. The relevant parts of the rest of the hierarchy are then constructed above these features, considerably simplifying the anatomical model. Modality dependent knowledge is applied to the current world and the tissue type instantiated to the grey level values normally expected from that modality. Other attributes are then derived to evaluate the distinguishability of each of the base level features from its neighbours or beyond. Those which cannot be distinguished are merged. There are two types of merging which occur. (a) If neighbouring features are of the same tissue type then they are merged — they are parts of the same feature. (b) Either the modality or the subsequent interpretation information may determine the minimum grey level contrast, below which neighbouring features can be merged. Contrast and quality of gradient have been successfully used with an interpretation system to recognise the lateral ventricles and caudate nucleus, distinguishing the latter from the internal capsule.

Automatic Generation of a Dynamic Anatomical Model for Use in Subsequent Radiological Image Interpretation

When attempting to interpret tomographic images such as X-ray Computerised Tomography scans many types of knowledge are employed. At the highest level a clinician employs domain knowledge about the anatomy and imaging modality, whilst at the lower levels he uses visual perception knowledge (that is, how to group pixels). In order to correctly recognise the anatomical features present in a sequence of CT scans it is necessary to incorporate domain-dependent knowledge — in the form of constraints — when applying knowledge on the ambiguities present in the image (such as the partial volume effects, artefacts and noise). We describe a knowledge-based system applied to Cranial X-ray Computer Tomography scans, comprising frames and rules. The frames have two purposes, firstly as a convenient mechanism for storing the anatomical descriptions and hierarchies present in the cranium and secondly as a dynamic structure which at any stage of the segmentation task reflects both (a) the underlying anatomy under the specific imaging modality and (b) the current grouping of pixels along with any inter-relationships that have been derived. The rules provide a mechanism for representing (a) declarative knowledge used to cluster pixels; and (b) control knowledge. It is the combination of the above that allows domain-dependent information to be propagated as constraints and expectations to the image partitioning stage.

A Knowledge-Based System for the Automatic Interpretation of X-Ray CT Scans

With increasing application of artificial intelligence techniques to automated interpretation of images in many different fields, there is incentive to attempt this for medical images. Very large numbers of images are produced in the course of medical diagnostic investigations and it is apparent that some automated aid in interpretation would be of benefit, both to relieve the experienced person of time and to bring the expertise of others to the aid of the less experienced.

M. G. Cawley

Papers

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

Data representation for subsequent image interpretation.

Automatic Generation of a Dynamic Anatomical Model for Use in Subsequent Radiological Image Interpretation

A Knowledge-Based System for the Automatic Interpretation of X-Ray CT Scans

Computer Aided Recognition of CT Scan Images