共查询到20条相似文献,搜索用时 58 毫秒
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关于医学图像分割的综述 总被引:1,自引:0,他引:1
医学图像分割是医学图像处理中最基本和最重要的技术,其目的是把图像空间分割成一些有意义的区域.医学图像分割技术的发展决定着医学图像处理中其它相关技术的发展.本文在大量阅读国内外近期文献的基础上,对近年来医学图像分割技术的发展进行了分类综述. 相似文献
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为了提高医学图像分割质量,提出了一种基于X光医学图像的改进分水岭算法。算法在应用分水岭算法前首先对感兴趣图像进行预处理,包括对感兴趣区域进行最小阈值法,分离背景区,对前景区运用腐蚀和膨胀运算得到候选区;在分水岭变换过程中,通过像素聚类合并准则,将与主像素聚类有相同特性的次像素聚类加入到分割结果中,最终得到合并区域。试验证明,这种改进的分水岭算法使过分割现象得到减少,有效地分割和提取医学图像中的病变区域。 相似文献
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针对传统分水岭算法对噪声敏感和易于产生过分割的问题,提出了一种将同态滤波增强与控制标记符分水岭相结合的分割策略.该方法先进行同态滤波增强预处理,再采用改进控制标记符的分水岭分割算法进行分割.仿真实验表明,提出的算法很好地抑制了过分割,实现了有意义的医学图像区域分割,同时还具有较强的区域轮廓定位能力,不需要再进行后续的合并处理,算法简单,并且能够适应医学图像分类与信息提取的需求. 相似文献
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随着社会的快速发展,图像边缘检测的方法也逐步的多样化,为了能够全面提升其图像边缘检测的效率,需要对整体的检测方法进行全面的创新.但在实际的检测过程中,其检测环境还相对复杂,导致边缘检测的难度相对较大.所以,对检测法进行优化十分关键.本文主要针对图像边缘检测法进行比较分析.并提出了相应的优化措施. 相似文献
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脉冲涡流热成像检测技术是一种新型的无损检测方法,研究热成像表面缺陷的数据特征,将otsu分割算法与最大熵分割算法相结合,提出基于最大熵的otsu分割算法,既能较好的分割热图像中的缺陷又能有效的识别目标缺陷的边缘。实验结果表明,相比较其他传统分割算法,具有更好的缺陷提取效果。 相似文献
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Region-based image coding schemes, the so-called second generation techniques, have gained much favour in recent years. For still picture coding, they can increase the compression ratio obtained by transform coding by an order of magnitude, while maintaining adequate image representation. The success of these techniques relies on the ability to describe regions in an image succinctly by their shape and size. The algorithms presented describe methods for segmenting images. Unlike most other region based algorithms, these algorithms incorporate knowledge of the border coding process in deciding how to partition the image. The extension from single image compression to sequential image compression is also considered. A new, efficient segmentation scheme is proposed that exploits temporal redundancies between successive images, and reduces some problems associated with error accumulation in error images 相似文献
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基于模糊和遗传算法的阈值分割方法 总被引:3,自引:1,他引:2
提出了一种阈值分割方法。本算法将模糊集理论和遗传算法有机地结合起来,它是一种有监督的分割方法,首先确定图像阈值的数目,然后将灰度图像模糊化,确定准则函数,采用遗传算法求得意了优解,最后经过后处理得到最终分割结果。实验表明该方法运算速度快,且对噪声不敏感,具有较高的鲁棒性。 相似文献
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In this paper, we propose a novel predictive model, active volume model (AVM), for object boundary extraction. It is a dynamic "object" model whose manifestation includes a deformable curve or surface representing a shape, a volumetric interior carrying appearance statistics, and an embedded classifier that separates object from background based on current feature information. The model focuses on an accurate representation of the foreground object's attributes, and does not explicitly represent the background. As we will show, however, the model is capable of reasoning about the background statistics thus can detect when is change sufficient to invoke a boundary decision. When applied to object segmentation, the model alternates between two basic operations: 1) deforming according to current region of interest (ROI), which is a binary mask representing the object region predicted by the current model, and 2) predicting ROI according to current appearance statistics of the model. To further improve robustness and accuracy when segmenting multiple objects or an object with multiple parts, we also propose multiple-surface active volume model (MSAVM), which consists of several single-surface AVM models subject to high-level geometric spatial constraints. An AVM's deformation is derived from a linear system based on finite element method (FEM). To keep the model's surface triangulation optimized, surface remeshing is derived from another linear system based on Laplacian mesh optimization (LMO). Thus efficient optimization and fast convergence of the model are achieved by solving two linear systems. Segmentation, validation and comparison results are presented from experiments on a variety of 2-D and 3-D medical images. 相似文献
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In recent years, deep learning has been successfully applied to medical image segmentation. However, as the network extends deeper, the consecutive downsampling operations will lead to more loss of spatial information. In addition, the limited data and diverse targets increase the difficulty for medical image segmentation. To address these issues, we propose a multi-path connected network (MCNet) for medical segmentation problems. It integrates multiple paths generated by pyramid pooling into the encoding phase to preserve semantic information and spatial details. We utilize multi-scale feature extractor block (MFE block) in the encoder to obtain large and multi-scale receptive fields. We evaluated MCNet on three medical datasets with different image modalities. The experimental results show that our method achieves better performance than the state-of-the-art approaches. Our model has strong feature learning ability and is robust to capture different scale targets. It can achieve satisfactory results while using only 0.98 million (M) parameters. 相似文献
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Deep learning algorithms have been successfully used in the field of medical image analysis and have greatly improved application of intelligent algorithms to medical diagnosis. However, existing deep-learning-based diagnostic methods still suffer from several drawbacks: (1) In most medical image multi-tasking methods, focus segmentation and disease classification are often performed linearly, resulting in excessive reliance on the final results of focus segmentation. (2) The computational cost of the traditional attention mechanism for performing the segmentation task is very high and the convolutional architecture cannot be used to model long-distance dependencies, which in turn affects the segmentation accuracy. To address these issues, we propose a disease diagnosis and lesion segmentation model, Dual-Branch with Transformer Axial-attention Segmentation Net (DB-TASNet). DB-TASNet is built by the DenseNet-121 classification network and U-Net segmentation network improved using an axial-attention transformer model. Moreover, DB-TASNet also includes a lesion integration module to integrate segmentation results with the classification network in order to increase its attention to lesions and improve the diagnosis results. Experimental results on the Pneumothorax dataset provided by the Society for Imaging Informatics in Medicine (SIIM) show that the average AUC of the DB-TASNet classification task reaches 0.939, and the DICE coefficient of the segmentation task reaches 0.886. Such performance suggests that the proposed model may provide an efficient and effective diagnosis tool for medical personnel. 相似文献