Image segmentation using automatic seeded region growing and instance-based learning

Authors:
Octavio Gómez;Jesús A. González;Eduardo F. Morales
Affiliations:
National Institute of Astrophisics, Optics and Electronics, Computer Science Department, Puebla, México;National Institute of Astrophisics, Optics and Electronics, Computer Science Department, Puebla, México;National Institute of Astrophisics, Optics and Electronics, Computer Science Department, Puebla, México
Venue:
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Year:
2007

Citing 9
Cited 2

Computer Vision

Computer Vision
Seeded Region Growing

IEEE Transactions on Pattern Analysis and Machine Intelligence
Induction of Decision Trees

Machine Learning
Seeded region growing: an extensive and comparative study

Pattern Recognition Letters
Image segmentation based on merging of sub-optimal segmentations

Pattern Recognition Letters
Image segmentation by unsupervised sparse clustering

Pattern Recognition Letters
Thresholding based on variance and intensity contrast

Pattern Recognition
Automatic seeded region growing for color image segmentation

Image and Vision Computing
A region growing and merging algorithm to color segmentation

Pattern Recognition

Segmentation and classification of hyperspectral images using minimum spanning forest grown from automatically selected markers

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Colorization of CT images to improve tissue contrast for tumor segmentation

Computers in Biology and Medicine

Quantified Score

Hi-index	0.00

Visualization

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.