首页> 外文会议>2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro >Active Contour for Overlap Resolution using Watershed BASED initialization (ACOReW): Applications to histopathology
【24h】

Active Contour for Overlap Resolution using Watershed BASED initialization (ACOReW): Applications to histopathology

机译:使用分水岭基础化初始化(ACOReW)的重叠轮廓有效轮廓:在组织病理学中的应用

获取原文
获取外文期刊封面目录资料

摘要

In recent years, shape based active contours have emerged as a natural solution to overlap resolution. However, most of these shape-based methods are limited to finding and resolving one object overlap per scene and require user intervention for model initialization. In this paper, we present a novel synergistic segmentation scheme called Active Contour for Overlap Resolution using Watershed (ACOReW). ACOReW combines shape priors with boundary and region-based active contours in a level set formulation with a watershed scheme for model initialization for identifying and resolving multiple object overlaps in an image scene. The energy functional for the variational active contour model is composed of three complimentary terms (a) a shape model which constrains the active contour to a pre-defined shape, (b) boundary based term which directs the active contour model to the image gradient, and (c) a third term driving the shape prior and the active contour towards a homogeneous intensity region. In this paper we show an application of ACOReW in the context of segmenting nuclear and glandular structures on prostate and breast cancer histopathology. The results of qualitative and quantitative evaluation on 100 prostate and 14 breast cancer histology images reveals that ACOReW outperforms two state of the art segmentation schemes (Geodesic Active Contour (GAC) and Rousson's shape based model) and resolves up to 92% of overlapping/occluded lymphocytes and nuclei on prostate and breast cancer histology images.
机译:近年来,基于形状的活动轮廓已经成为重叠分辨率的自然解决方案。但是,这些基于形状的方法大多数都限于查找和解决每个场景的一个对象重叠,并且需要用户干预才能进行模型初始化。在本文中,我们提出了一种新的协同分割方案,即使用分水岭(ACOReW)进行重叠分辨率的主动轮廓。 ACOReW将形状先验与基于边界和区域的活动轮廓结合在一起,并在分水岭方案中采用分水岭方案进行模型初始化,以识别和解决图像场景中的多个对象重叠。可变活动轮廓模型的能量函数由三个互补项组成:(a)将活动轮廓约束为预定形状的形状模型;(b)将活动轮廓模型指向图像梯度的基于边界的项, (c)第三项将形状先验形状和有效轮廓推向均匀强度区域。在本文中,我们显示了ACOReW在分割前列腺和乳腺癌组织病理学上的核和腺结构方面的应用。对100份前列腺和14份乳腺癌组织学图像进行定性和定量评估的结果表明,ACOReW优于两种最新的分割方案(大地主动轮廓(GAC)和基于Rousson形状的模型),并且可以解决多达92%的重叠/封闭前列腺和乳腺癌组织学图像上的淋巴细胞和细胞核。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号