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A new fractional order derivative based active contour model for colon wall segmentation

机译:基于基于基于FORON壁分割的基于活动轮廓模型

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Segmentation of colon wall plays an important role in advancing computed tomographic colonography (CTC) toward a screening modality. Due to the low contrast of CT attenuation around colon wall, accurate segmentation of the boundary of both inner and outer wall is very challenging. In this paper, based on the geodesic active contour model, we develop a new model for colon wall segmentation. First, tagged materials in CTC images were automatically removed via a partial volume (PV) based electronic colon cleansing (ECC) strategy. We then present a new fractional order derivative based active contour model to segment the volumetric colon wall from the cleansed CTC images. In this model, the region-based Chan-Vese model is incorporated as an energy term to the whole model so that not only edge/gradient information but also region/volume information is taken into account in the segmentation process. Furthermore, a fractional order differentiation derivative energy term is also developed in the new model to preserve the low frequency information and improve the noise immunity of the new segmentation model. The proposed colon wall segmentation approach was validated on 16 patient CTC scans. Experimental results indicate that the present scheme is very promising towards automatically segmenting colon wall, thus facilitating computer aided detection of initial colonic polyp candidates via CTC.
机译:结肠壁的分割在推进计算的断层形成朝向筛选模型中起着重要作用。由于CT衰减围绕结肠壁衰减的低对比度,内壁和外壁的边界的精确分割非常具有挑战性。本文基于测地活动轮廓模型,我们开发了一种用于冒号墙分割的新模型。首先,通过基于部分体积(PV)的电子结肠清洁(ECC)策略自动除去CTC图像中的标记材料。然后,我们提出了一种基于新的基于分数阶数的有源轮廓模型,用于将体积冒号壁分段从清洁的CTC图像段。在该模型中,基于地区的Chan-Vese模型被作为整个模型的能量术语结合,使得在分割过程中不仅考虑了边缘/梯度信息,而且还考虑了区域/卷信息。此外,在新模型中也开发了分数级分化衍生术语,以保持低频信息,提高新分段模型的抗噪性。在16例患者CTC扫描上验证了所提出的结肠壁分割方法。实验结果表明,本发明方案非常有前途朝向自动分割结肠壁,从而促进通过CTC对初始结肠息肉候选的计算机辅助检测。

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