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This chapter highlights the application of image processing for automated analysis of images generated by reconstructive imaging techniques (CT, MRI). It is shown that, in addition to the pure image information, contextual knowledge about the imaged structures as well as knowledge about acquisition- or reconstruction-related artifacts is always necessary in order to obtain qualitatively good results. The consideration of this contextual information, however, is complicated in the classical way and is rarely directly transferable to modified questions. The application of deep neural networks to such questions offers a great potential. This is explained, demonstrated, and discussed using the semantic segmentation of image data.

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Image Processing 2D/3D with Emphasis on Image Segmentation

  • 工具书词条

  • 发表日期：

    2025年

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  • Andreas H. J. Tewes,
  • Astrid Haibel,
  • Rainer P. Schneider

摘要

This chapter highlights the application of image processing for automated analysis of images generated by reconstructive imaging techniques (CT, MRI). It is shown that, in addition to the pure image information, contextual knowledge about the imaged structures as well as knowledge about acquisition- or reconstruction-related artifacts is always necessary in order to obtain qualitatively good results. The consideration of this contextual information, however, is complicated in the classical way and is rarely directly transferable to modified questions. The application of deep neural networks to such questions offers a great potential. This is explained, demonstrated, and discussed using the semantic segmentation of image data.