期刊：
Medical Physics,2016年43(3):1487-1500 ISSN：0094-2405
通讯作者：
Sun, Kaiqiong
作者机构：
[Sun, Kaiqiong] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.;[Tong, Yubing; Zhao, Liming; Udupa, Jayaram K.; Odhner, Dewey; Torigian, Drew A.] Univ Penn, Dept Radiol, Med Image Proc Grp, Philadelphia, PA 19104 USA.;[Zhao, Liming] Chongqing Univ Posts & Telecommun, Res Ctr Intelligent Syst & Robot, Chongqing 400065, Peoples R China.
通讯机构：
[Sun, Kaiqiong] W;Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.
关键词：
biological organs;computerised tomography;fuzzy set theory;image recognition;image registration;image segmentation;medical image processing;object recognition;physiological models;Computed tomography;Segmentation;Registration;Computerised tomographs;Biological material, e.g. blood, urine;Haemocytometers;Digital computing or data processing equipment or methods, specially adapted for specific applications;Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints;Image data processing or generation, in general;shape modeling;fuzzy models;object recognition;fuzzy connectedness;segmentation;registration;Ion-mobility spectrometry;Computed tomography;Lungs;Medical image segmentation;Data sets;Computer modeling;Collective models;Biomedical modeling
摘要：
Purpose: In an attempt to overcome several hurdles that exist in organ segmentation approaches, the authors previously described a general automatic anatomy recognition (AAR) methodology for segmenting all major organs in multiple body regions body-wide [J. K. Udupa et al., "Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images,"Med. Image Anal. 18(5), 752-771 (2014)]. That approach utilized fuzzy modeling strategies, a hierarchical organization of organs, and divided the segmentation task into a recognition step to localize organs which was then followed by a delineation step to demarcate the boundary of organs. It achieved speed and accuracy without employing image/ object registration which is commonly utilized in many reported methods, particularly atlas-based. In this paper, our aim is to study how registration may influence performance of the AAR approach. By tightly coupling the recognition and delineation steps, by performing registration in the hierarchical order of the organs, and through several object-specific refinements, the authors demonstrate that improved accuracy for recognition and delineation can be achieved by judicial use of image/ object registration. Methods: The presented approach consists of three processes: model building, hierarchical recognition, and delineation. Labeled binary images for each organ are registered and aligned into a 3D fuzzy set representing the fuzzy shape model for the organ. The hierarchical relation and mean location relation between different organs are captured in the model. The gray intensity distributions of the corresponding regions of the organ in the original image are also recorded in the model. Following the hierarchical structure and location relation, the fuzzy shape model of different organs is registered to the given target image to achieve object recognition. A fuzzy connectedness delineation method is then employed to obtain the final segmentation result of organs with seed points provided by recognition. The authors assess the performance of this method for both nonsparse (compact blob-like) and sparse (thin tubular) objects in the thorax. Results: The results of eight thoracic organs on 30 real images are presented. Overall, the delineation accuracy in terms of mean false positive and false negative volume fractions is 0.34% and 4.02%, respectively, for nonsparse objects, and 0.16% and 12.6%, respectively, for sparse objects. The two object groups achieve mean boundary distance relative to ground truth of 1.31 and 2.28 mm, respectively. Conclusions: The hierarchical structure and location relation integrated into the model provide the initial pose for registration and make the recognition process efficient and robust. The 3D fuzzy model combined with hierarchical affine registration ensures that accurate recognition can be obtained for both nonsparse and sparse organs. Tailoring the registration process for each organ by specialized similarity criteria and updating the organ intensity properties based on refined recognition improve the overall segmentation process. (C) 2016 American Association of Physicists in Medicine.
摘要：
This paper is concerned with the convergence of stochastic theta-methods for stochastic pantograph equations with Poisson-driven jumps of random magnitude. The strong order of the convergence of the numerical method is given, and the convergence of the numerical method is obtained. Some earlier results are generalized and improved.
