Biomedical Image Interpretation Research Articles

Computer-based Learning of Neuroanatomy: A Longitudinal Study of Learning, Transfer, and Retention.

A longitudinal experiment was conducted to evaluate the effectiveness of new methods for learning neuroanatomy with computer-based instruction. Using a 3D graphical model of the human brain, and sections derived from the model, tools for exploring neuroanatomy were developed to encourage adaptive exploration. This is an instructional method which incorporates graphical exploration in the context of repeated testing and feedback. With this approach, 72 participants learned either sectional anatomy alone or whole anatomy followed by sectional anatomy. Sectional anatomy was explored either with perceptually continuous navigation through the sections or with discrete navigation (as in the use of an anatomical atlas). Learning was measured longitudinally to a high performance criterion. Subsequent tests examined transfer of learning to the interpretation of biomedical images and long-term retention. There were several clear results of this study. On initial exposure to neuroanatomy, whole anatomy was learned more efficiently than sectional anatomy. After whole anatomy was mastered, learners demonstrated high levels of transfer of learning to sectional anatomy and from sectional anatomy to the interpretation of complex biomedical images. Learning whole anatomy prior to learning sectional anatomy led to substantially fewer errors overall than learning sectional anatomy alone. Use of continuous or discrete navigation through sectional anatomy made little difference to measured outcomes. Efficient learning, good long-term retention, and successful transfer to the interpretation of biomedical images indicated that computer-based learning using adaptive exploration can be a valuable tool in instruction of neuroanatomy and similar disciplines.

Implementing a teleradiology pilot system in Medellín, Colombia.

Objective: To implement a teleradiology pilot system in Medellín city using software for remote access, this allows the communication and interpretation of biomedical images at distance. Materials and methods: Images from different magnetic resonance and computed tomography studies stored in DICOM format were used. Data were transmitted in a point-to-point network using an integrated services digital network (ISDN) line between two radiological diagnostic centers. The system was developed under PC Intel’s x86 architecture, with Windows® 2000 as the operating system. Results: In order to read and visualize images stored in DICOM format, a Java application with functions that allow their manipulation and the option to export them to other formats such as JPEG, TIFF and BMP was developed. Conclusion: The system allows a clinically reliable diagnostic and remote interpretation for modalities such as computed tomography (CT) and magnetic resonance (MR) images, with acceptable response times.

Nearest-Neighbor Analysis of Spatial Point Patterns: Application to Biomedical Image Interpretation

Analysis of the spatial distributions of objects is fundamental to biomedical image interpretation. Nearest-neighbor (NN) methods are generally used to assess whether objects are arranged at random or in a deterministic manner. Simple standard NN techniques, however, may fail to identify complex spatial organizations. To overcome this problem the present study proposes a NN iterative algorithm that enables deterministic spatial patterns to be detected by identifying the distances between objects for which there is the greatest deviation from randomness and hence the amplitude of the areas of maximum reciprocal influence between objects. The performance of the algorithm is evaluated by applying it to both manufactured and experimental data. The manufactured data example showed that the proposed procedure produced neither false positives or negatives. The method proved to be extremely sensitive, detecting even small deviations from randomness. The experimental analysis was applied to the study of the spatial distribution of apoptotic structures in malignant neoplastic tissue. It showed that the apoptotic cells and bodies are characterized by a complex spatial pattern, and aggregate closely.

Trainable models for the interpretation of biomedical images

In this paper we report on the use of explicit models in the interpretation of echocardiogram images. The problem is considered as an example of a general biomedical image interpretation task, and the modelling techniques used can be applied to a wide range of problems. The models are built as a hierarchy of components and the parameters of each component are determined from training examples and prior ‘expert’ knowledge. For each component the model encodes information about the average case and information about the expected distributions, for example sizes, shapes and positions. The models are used within the interpretation process to assess hypothesized matches and to guide further processing. Details of the design and implementation of the model components, the refinements and training techniques and the results of application to the echocardiogram images are presented.

Knowledge based interpretation of images: a biomedical perspective

AbstractThe traditions of image processing and knowledge engineering have developed separately. Work on AI vision systems lies between the two traditions but only recently has attention been given to combining practical imaging systems with methods for exploiting knowledge in interpreting the contents of an image. Five general approaches to combining knowledge based expert systems with imaging technologies are discussed. Particular attention is paid to the requirement for techniques which transform a pixel array into a symbolic form suitable for interpretation, and current obstacles to a general solution. Interpretation of biomedical images is particularly problematic because of statistical, structural and temporal variation in morphology of objects and structures. Some ways in which knowledge of shape, structure, and object classifications may contribute to this interpretation are discussed. The survey focuses on biomedical images but many of the issues are of general relevance to work in image understanding.

A knowledge-based system for biomedical image processing and recognition

The purpose of this paper is to describe a rule-based system named IBIS (Interpretation of Biomedical Images of the Slice type), which, starting from conventional image processing, can recognize slices and locate automatically their principal anatomical organs in nuclear magnetic resonance (NMR) images of the head. The main aspects of the system are: knowledge representation, with 3-D and 2-D aspects for anatomical description; error recovery and conflict resolution facilities at different backtracking levels; and a general system structure easy to extend to other application fields. Production rules are employed in order to define the criteria for deciding what action is to be performed on the basis of the current problem status. However, in order to obtain a more explicit and appropriate representation, anatomical knowledge has not been spread throughout the production rules, but it has been incorporated into an extended semantic network that well represents relational properties. The control structure consults production rules and, according to their content, activates the various processing procedures in order to attain the recognition goal. A strategy is applied that can be driven by data or models, depending on the processing phase and on progressive results. Applications of the system to different patients and slices are described and discussed. The importance of the paper lies basically in the methodological approach to image processing and interpretation, and in possible interesting applications to the medical field.