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Medical Image Segmentation in Digitial Mammography

  • Sameer Singh
  • Keir Bovis
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

Medical image segmentation is of primary importance in the development of Computer Assisted Detection (CAD) in mammographic systems. The identification of calcifications and masses requires highly sophisticated techniques that can isolate regions of interest from noisy backgrounds. The main objective of this chapter is to highlight the various issues related to digital mammography by providing a brief overview of the segmentation techniques used in this area. We first introduce the role of image segmentation in mammography in section 9.2. This section discusses a typical image analysis system for digital mammography and discusses the issues related to difficulties with segmentation. In order to understand the segmentation process, it is important to discuss some salient aspects of breast anatomy. This is detailed in Section 9.3. Various breast components are explained alongside the description of breast cancers. Image acquisition and storage formats are important as a predecessor to image analysis in mammography and these are discussed next in Section 9.4. This section also discusses different modes of mammography, image digitization and commonly used formats. Image segmentation cannot be isolated from some form of pre-processing that improves the visibility of objects of interest from their background. Section 9.5 discusses some of the enhancement techniques widely used in mammographic research. Often, the process of enhancement is difficult to verify, and therefore quantitative measures are sorely needed to select an optimal enhancement technique for a given data set.

Keywords

Gray Level Digital Mammography Medical Image Segmentation Computer Assist Detection Local Texture Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London 2002

Authors and Affiliations

  • Sameer Singh
  • Keir Bovis

There are no affiliations available

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