Image Segmentation Using Automatic Seeded Region Growing and Instance-Based Learning

  • Octavio Gómez
  • Jesús A. González
  • Eduardo F. Morales
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)

Abstract

Segmentation through seeded region growing is widely used because it is fast, robust and free of tuning parameters. However, the seeded region growing algorithm requires an automatic seed generator, and has problems to label unconnected pixels (the unconnected pixel problem). This paper introduces a new automatic seeded region growing algorithm called ASRG-IB1 that performs the segmentation of color (RGB) and multispectral images. The seeds are automatically generated via histogram analysis; the histogram of each band is analyzed to obtain intervals of representative pixel values. An image pixel is considered a seed if its gray values for each band fall in some representative interval. After that, our new seeded region growing algorithm is applied to segment the image. This algorithm uses instance-based learning as distance criteria. Finally, according to the user needs, the regions are merged using ownership tables. The algorithm was tested on several leukemia medical images showing good results.

Keywords

Image Segmentation Seeded Region Growing Instance-based learning Color image Multispectral image 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Octavio Gómez
    • 1
  • Jesús A. González
    • 1
  • Eduardo F. Morales
    • 1
  1. 1.National Institute of Astrophisics, Optics and Electronics, Computer Science Department, Luis Enrique Erro Num 1, PueblaMéxico

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