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Multi-scale opening of conjoined structures and its applications to medical imaging

时间： 2012年7月2日（周一）上午10：00

地点：逸夫楼416学术报告厅

报告人简介：

PUNAM KUMAR SAHA教授现任教于美国爱荷华University of Iowa电子计算机工程学院，指导结构成像实验室。他目前的研究方向包括：生物医学成像及应用，骨小梁成像，计算机视觉和图像处理，规模化的不确定论和定量的数字拓扑学和几何学。PUNAM KUMAR SAHA教授已在国际的相关杂志上发表了70多篇论文，在相关的国际会议上发表了150多篇论文（或杂志），其1996年曾获得印度科技协会的青年科学家奖。PUNAM KUMAR SAHA教授曾担任模式识别、计算机医学图像和图形学杂志的副主编；现担任美国电气和电子工程协会（IEEE）生物工程方面的副主编；曾担任过多个国际会议的程序委员会委员。PUNAM KUMAR SAHA教授获得了美国国家卫生研究所给予的多项奖励。

报告简介：

Distinguishing pulmonary arterial and venous (A/V) trees via in vivo imaging is a critical first step in the quantification of vascular geometry for the purpose of diagnosing several pulmonary diseases and to develop new image-based phenotypes. A multi-scale topo-morphologic opening (MSTMO) algorithm has recently been developed in our laboratory for separating A/V trees via non-contrast pulmonary human CT imaging. The method starts with two sets of seeds – one for each of A/V trees and combines fuzzy distance transform and fuzzy connectivity in conjunction with several morphological operations leading to locally-adaptive iterative multi-scale opening of two mutually conjoined structures. Also, we introduce the methods for handling “local update” and “separators” into our previous theoretical formulation and incorporate the algorithm into an effective graphical user interface (GUI). Results of a comprehensive evaluative study assessing both accuracy and reproducibility of the method under the new setup are presented and also, the effectiveness of the GUI-based system toward towards improving A/V separation results areis examined. Accuracy of the method has been evaluated using mathematical phantoms, CT images of contrast-separated pulmonary A/V casting of a pig's lung and non-contrast pulmonary human CT imaging. The method has achieved 99% true A/V labeling in the cast phantom and, almost, 92% to 94% true labeling in human lung data. Reproducibility of the method has been evaluated using multi-user A/V separation in human CT data along with contrast-enhanced CT images of a pig's lung at different positive end-expiratory pressures (PEEPs). The method has achieved, almost, 92% to 98% agreements in multi-user A/V labeling with ICC for A/V measures being over 0.96 to 0.99. Effectiveness of the GUI based method has been evaluated on human data in terms of improvements of accuracy of A/V separation results and results have shown 8% to 22% improvements in true A/V labeling. Both qualitative and quantitative results found are very promising.