Introduction

  • Yefeng Zheng
  • Dorin Comaniciu
Chapter

Abstract

The last decade saw tremendous advances in all major medical imaging modalities, with significant improvement in signal-to-noise ratio, sensitivity and specificity, spatial and temporal resolution, and radiation dose reduction. All these developments translated into direct benefits for the quality of care. Most imaging modalities generate now high resolution, isotropic or near-isotropic true 3D volumes, resulting in a large amount of complex data to process. This, however, presents a significant challenge to the already loaded radiologists. As a consequence, intelligent medical image analysis systems are critical to help radiologists to improve accuracy and consistency of diagnosis, increase patient throughput, and optimize daily workflow. The main operations of a computer supported, enhanced reading workflow consist in the detection, segmentation and quantification of various semantic structures in the image data. The quantification of images helps answering questions such as: “Are there inflamed lymph nodes in this volume?”, “Does this scan contain bone lesions?”, and “Has this lesion decreased following treatment?”. This chapter reviews recent advances in medical imaging and presents some of the new challenges and opportunities for image processing and analysis. We then review applications of automatic detection and segmentation in medical imaging, followed by a literature survey on the existing detection and segmentation methods. A brief introduction to the Marginal Space Learning (MSL) based anatomical structure detection and segmentation is presented, followed by the outline of the book content.

Keywords

Catheter Pyramid 

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Yefeng Zheng
    • 1
  • Dorin Comaniciu
    • 1
  1. 1.Imaging and Computer VisionSiemens Corporate TechnologyPrincetonUSA

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