Image segmentation using automatic seeded region growing and instance-based learning
作者： Octavio Gómez , Jesús A. González , Eduardo F. Morales
DOI: 10.1007/978-3-540-76725-1_21
关键词:
摘要: Segmentation through seeded region growing is widely used because it fast, robust and free of tuning parameters. However, the algorithm requires an automatic seed generator, has problems to label unconnected pixels (the pixel problem). This paper introduces a new called ASRG-IB1 that performs segmentation color (RGB) multispectral images. The seeds are automatically generated via histogram analysis; each band analyzed obtain intervals representative values. An image considered if its gray values for fall in some interval. After that, our applied segment image. uses instance-based learning as distance criteria. Finally, according user needs, regions merged using ownership tables. was tested on several leukemia medical images showing good results.
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