Biological Image Understanding

Stanley M. Dunn

doi:10.1117/12.953289

25 May 1989 Biological Image Understanding

Stanley M. Dunn

Proceedings Volume 1092, Medical Imaging III: Image Processing; (1989) https://doi.org/10.1117/12.953289
Event: Medical Imaging III, 1989, Newport Beach, CA, United States

Abstract

The primary goal of any computer vision system is to construct scene descriptions similar to those formed by the human visual system. Biological image understanding is the automatic processing and interpretation of imagery of natural structures. The key constraint is the absence of explicit models of the objects anticipated to be in the scene. A clinician interprets imagery using principles of physiology and the image acquisition process. He learns to construct explicit models to recognize patterns from these principles, but is also able to infer an interpretation of an unknown structure from examples and physiology. Our overall goal is to develop a computer vision system to be used to interpret imagery of biological data using these principles. In this paper we present a paradigm for constructing image understanding systems designed to process natural images and present some applications of the system to hematology and radiography. The first step of our general approach is data acquisition. The second step is to find intrinsic properties at each pixel. The third step is to group pixels together to for homogeneous regions using the intrinsic properties. The final step is object recognition, where object type and location are determined from the quantitative features of the homogeneous regions. We shall outline the algorithms and data structures used at each step in the process, and their implementation. We shall describe in detail a key feature of this paradigm-forming homogeneous regions by geometry guided segmentation. The fact that classical algorithms do not always yield correct results led us to an algorithm that uses structural information and intensity values to group homogeneous regions. We shall illustrate this problem and the solution, as well as preliminary results of the application of the image understanding system.

Citation Download Citation

Stanley M. Dunn "Biological Image Understanding", Proc. SPIE 1092, Medical Imaging III: Image Processing, (25 May 1989); https://doi.org/10.1117/12.953289

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KEYWORDS

Image segmentation

Image processing

Image understanding

Image processing algorithms and systems

Computing systems

Medical imaging

Radiography

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