A high-throughput active contour scheme for segmentation of histopathological imagery.
A high-throughput active contour scheme for segmentation of histopathological imagery.
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DOI：
10.1016/j.media.2011.04.002
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发表时间：
2011-12
影响因子：
10.9
通讯作者：
Madabhushi, Anant
中科院分区：
文献类型：
--
作者：
Xu, Jun;Janowczyk, Andrew;Chandran, Sharat;Madabhushi, Anant
本文提出了一种最小交互高通量系统，该系统采用基于颜色梯度的活动轮廓模型来快速准确地分割超大图像上的多个目标对象。虽然测地线活动轮廓(GAC)已经成为非常流行的图像分割工具，但它们往往对模型初始化敏感。GAC模型的第二个限制是边缘检测器功能通常涉及使用灰度梯度；彩色图像通常在梯度计算之前被转换为灰度。然而，对于彩色图像，灰度梯度图像会导致边缘断裂和弱边界，因为在梯度计算中没有利用其他通道。为了克服这些局限性，我们提出了一种新的GAC模型，该模型由一种准确而快速的对象初始化方案--分层规格化切割(HNCut)驱动。HNCut的优势来自于两种强大的分割策略的集成-均值漂移聚类和归一化切割。HNCut首先定义感兴趣对象的色样(通常为几个像素)。然后，多尺度、均值漂移耦合的归一化切割算法快速地产生与样本中的颜色相对应的场景中的所有对象的初始准确检测。该检测结果为GAC模型提供了初始轮廓。GAC模型的边缘检测器函数采用基于局部结构张量的颜色梯度，该颜色梯度是通过计算来自每个颜色通道的局部最小/最大变化而获得的。我们发现，与经典的基于灰度梯度的边缘检测函数相比，基于颜色梯度的边缘检测函数产生了更突出的边界。通过将HNCut初始化方案与基于颜色梯度的GAC(CGAC)相结合，HNCut-CGAC体现了五个独特而新颖的属性：1)分割多个目标结构的效率；2)从非常大的图像中分割多个对象的能力；3)最少的人类交互；4)准确性；5)重复性。HNCut-CGAC模型与其他最先进的活动轮廓方案(包括混合活动轮廓模型(Paragios-Deriche)和基于区域的AC模型(Rousson-Deriche))在196幅数字化前列腺组织病理图像上的定量和定性比较表明，HNCut-CGAC能够超越最先进的混合和基于区域的AC技术。结果表明，HNCut-CGAC算法具有较高的计算效率，可以方便地应用于各种不同的问题和应用。
In this paper a minimally interactive high-throughput system which employs a color gradient based active contour model for rapid and accurate segmentation of multiple target objects on very large images is presented. While geodesic active contours (GAC) have become very popular tools for image segmentation, they tend to be sensitive to model initialization. A second limitation of GAC models is that the edge detector function typically involves use of gray scale gradients; color images usually being converted to gray scale, prior to gradient computation. For color images, however, the gray scale gradient image results in broken edges and weak boundaries, since the other channels are not exploited in the gradient computation. To cope with these limitations, we present a new GAC model that is driven by an accurate and rapid object initialization scheme; hierarchical normalized cuts (HNCut). HNCut draws its strength from the integration of two powerful segmentation strategies—mean shift clustering and normalized cuts. HNCut involves first defining a color swatch (typically a few pixels) from the object of interest. A multi-scale, mean shift coupled normalized cuts algorithm then rapidly yields an initial accurate detection of all objects in the scene corresponding to the colors in the swatch. This detection result provides the initial contour for a GAC model. The edge-detector function of the GAC model employs a local structure tensor based color gradient, obtained by calculating the local min/max variations contributed from each color channel. We show that the color gradient based edge-detector function results in more prominent boundaries compared to the classical gray scale gradient based function. By integrating the HNCut initialization scheme with color gradient based GAC (CGAC), HNCut-CGAC embodies five unique and novel attributes: 1) Efficiency in segmenting multiple target structures; 2) The ability to segment multiple objects from very large images; 3) Minimal human interaction; 4) Accuracy; and 5) Reproducibility. A quantitative and qualitative comparison of the HNCut-CGAC model against other state of the art active contour schemes (including a Hybrid Active Contour model (Paragios-Deriche) and a region based AC model (Rousson-Deriche)), across 196 digitized prostate histopathology images, suggests that HNCut-CGAC is able to outperform state of the art hybrid and region based AC techniques. Our results show that HNCut-CGAC is computationally efficient and may be easily applied to a variety of different problems and applications.
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DOI：
10.1109/tip.2009.2032341
发表时间：
2010-01
期刊：
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
影响因子：
--
作者：
Li L;Yang Y
通讯作者：
Yang Y
影响因子：
10.6
作者：
Chan, TF;Vese, LA
通讯作者：
Vese, LA
DOI：
10.1109/34.368173
发表时间：
1995-02-01
影响因子：
23.6
作者：
MALLADI, R;SETHIAN, JA;VEMURI, BC
通讯作者：
VEMURI, BC
影响因子：
4.5
作者：
Sapiro, G
通讯作者：
Sapiro, G
DOI：
10.1109/34.868688
发表时间：
2000-08-01
影响因子：
23.6
作者：
Shi, JB;Malik, J
通讯作者：
Malik, J