Medical Image Segmentation Based on Improved Watershed Algorithm

Jing-yu Li, Jin Cheng, W. Mu, Kui Geng, Zhang Yan
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Abstract

The medical images have pathological change region and background.The pathological change region that is so-called the region of interest(ROI) is the emphasis of image segmentation.Aimed at resolving the problems of sensitivity to noise and over-segmentation existing in traditional watershed algorithm,an image segmentation strategy on the combination of morphological filtering and multi-scale morphology gradient and marker controlled watershed is proposed.Firstly,use of mathematical morphological closed-open operation to filter out complete pretreatment original medical image to filter the noise and the non-perceptional information,in order to preserve the structural information of the original images;secondly,do closed operations smooth image,and calculate the image multi-scale morphology gradient,thirdly,watershed algorithm is reconstruced it and applied marker controlled to segment the reconstructed gradient image.Finally,the segmentation results is transfored to original scale.Simulation results show that the improvement can not only suppress the over-segmented phenomena properly.but also segment the pathological change regions in medical images efectively and segmentation algorithm is simple,and has the characteristics of multiple scales,thus is quite qualified for classification and information extraction of medical sensed imagery.
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基于改进分水岭算法的医学图像分割
医学图像具有病变区域和背景。病理变化区域即感兴趣区域是图像分割的重点。针对传统分水岭算法存在的对噪声敏感和过度分割的问题,提出了一种形态学滤波与多尺度形态学梯度和标记控制分水岭相结合的图像分割策略。首先,利用数学形态学闭开运算滤除完整预处理的原始医学图像,滤除噪声和非感知信息,以保持原始图像的结构信息;其次,对图像进行闭开运算,并计算图像的多尺度形态学梯度;第三,对图像进行分水岭算法重构,并应用标记控制对重构的梯度图像进行分割。最后,将分割结果转换为原始尺度。仿真结果表明,这种改进不仅能很好地抑制过分割现象。而且还能有效分割医学图像中的病变区域,且分割算法简单,具有多尺度的特点,很适合医学传感图像的分类和信息提取。
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