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13 FRONTIERS OF IMAGE PROCESSING FOR MEDICINE
Pages 187-198

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From page 187...
... Despite a recent trend toward its use, digital representation of medical images has not yet become generally accepted, and its widespread application will require further developments in the hardware 187
From page 188...
... These observations make it clear that for medical image analysis, the fundamental mathematical need is the derivation of procedures for extracting the clinically important features from one or more large data sets for example, quantitative information on tumor volume for each of several studies over a time period, to help gauge the efficacy of different treatments, or a parametric map created to represent rate constants from a time series of tracer movements in the brain or heart. The procedures associated with this type of contemporary image analysis can be separated into several different classes: · Image segmentation, · Computational anatomy, · Registration of multimodality images, · Synthesis of parametric images, · Data visualization, an · Treatment planning.
From page 189...
... Segmentation involves associating a pixel with a particular object class based on the local intensity, spatial position, neighboring pixels, or prior information about the shape characteristics of the object class. The focus of research into segmentation is to determine logic rules or strategies that accomplish acceptably accurate segmentation with as little interactive analysis as possible.
From page 190...
... Neuroanatomy is an example of a field that is beginning to employ these approaches to develop quantitative descriptors of anatomic variability across subjects, age ranges, gender, and species. KnowIeclge of the range of variability in normal anatomy wouIcl allow the detection and quantitative characterization of pathological cleviations, for example, Region growing is the process of identifying some pixels in the image that are clearly associated with different structures and then adding to each its neighboring pixels with similar intensities until regions of similar pixel intensities have been built up.
From page 191...
... Computational anatomy also includes the characterization of tissue architecture or surface texture, as in, for example, the analysis of changes in trabecular bone structure associated with osteoporosis. In this case, conventional methods of parameterizing the changes in image intensity have recently been extended by the use of descriptors based on Fourier space images and by fractal analyses.
From page 192...
... Two examples of situations in which parametric images are able to provide information that is not available from conventional anatomic images are (1) in distinguishing between radiation necrosis and an active tumor through metabolite images calculated from MR spectroscopic imaging data and (2)
From page 193...
... 13.5 Data Visualization As biomedical imaging advances in terms of the sophistication of data acquisition techniques, the need to clevelop improved tools for image processing and visualization has become a major bottleneck. This need is particularly acute for the combined interpretation of three-dimensional anatomic en c!
From page 194...
... The first is to define the target to be treated, which in almost ah cases requires the visualization of the lesion relative to the normal anatomy using diagnostic imaging modalities such as CT or MRT. Once the size and location of the lesion have been determined either by manual examination of the data or by more sophisticated image segmentation, it is necessary to determine how best to deliver the therapy.
From page 195...
... 13.7 Research Opportunities There are numerous research opportunities in the field of contemporary biomedical image processing. Some of the most challenging research opportunities fall in the area of extending traditional approaches to segmentation and object cIassification in order to include shape information rather than merely image intensity.
From page 196...
... This research area includes the application of statistical approaches for identification of subtle changes in time series of three-dimensional images obtained for mapping brain function, the use of prior anatomic information to constrain the reconstruction of Tow signal-to-noise metabolic data, and the derivation of parametric images that accurately describe the kinetics of biologically relevant tracers. Improved techniques for visualizing multi-dimensional data are critical for establishing the relevance of new types of image data, making the information that they represent accessible to a wide audience, and understanding their relationship to conventional anatomic images.
From page 197...
... SUGGESTED READING 197 6. Thirion, J.-P., Fast Non-Rigid Matching of ED Medical Images, INRIA research report 2547, INRIA, Le Chesnay, France, 1995.


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