基于形態(tài)學(xué)尺度空間和梯度修正的分水嶺分割
Watershed Segmentation Based on Morphological Scale-Space and Gradient Modification
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摘要: 分水嶺是一種有效的圖像分割方法，但存在過分割現(xiàn)象，為此提出了一種基于形態(tài)學(xué)尺度空間和梯度修正的分水嶺圖像分割方法，該方法利用形態(tài)學(xué)混合開閉重建尺度空間和梯度修正技術(shù)，在平滑原始圖像的同時保留了重要的區(qū)域輪廓而去除了易造成過分割的區(qū)域細(xì)節(jié)和噪聲，克服了傳統(tǒng)的形態(tài)學(xué)開閉尺度空間在平滑細(xì)節(jié)和噪聲時，部分重要區(qū)域輪廓也被平滑及不滿足尺度因果性的問題。對平滑后的圖像采用梯度修正分水嶺變換，保持了尺度和分割區(qū)域數(shù)目間的因果性，進(jìn)一步消除了標(biāo)準(zhǔn)分水嶺的過分割現(xiàn)象。仿真實(shí)驗(yàn)表明，該方法能有效地消除過分割現(xiàn)象，分割的區(qū)域數(shù)目滿足尺度因果性，且具有較高的區(qū)域輪廓定位能力。
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關(guān)鍵詞:
- 圖像分割;形態(tài)學(xué)尺度空間;梯度修正;分水嶺
Abstract: A method for watershed image segmentation based on morphological scale-space and gradient modification is proposed to avoid over-segmentation and the drawbacks of some improved watershed segmentations. Firstly, morphological hybrid opening and closing by reconstruction scale-space is employed to smooth the original image, after smoothing, the essential region contours are preserved and unimportant details and noise which are often the causes of over-segmentation are removed, and the problem of the traditional morphological opening and closing scale-space, including the lost of partial essential region contours and not satisfying scale causality, are both avoided. Secondly, in order to eliminate over-segmentation and to keep the scale causality from the extreme to the segmented regions, gradient modification is used before the standard watershed transform, to remove the regional minimum in the gradient image caused by the regional maximum in the smoothed image. Simulations show that this method can efficiently not only avoid over-segmentation, but also satisfy scale causality, and the localization of region contours is precise. -
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