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An automated pulmonary parenchyma segmentation method based on an improved region growing algorithm in PET-CT imaging 被引量:6
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作者 Juanjuan ZHAO guohua ji +2 位作者 Xiaohong HAN Yan QIANG Xiaolei LIAO 《Frontiers of Computer Science》 SCIE EI CSCD 2016年第1期189-200,共12页
To address the incomplete problem in pulmonary parenchyma segmentation based on the traditional methods, a novel automated segmentation method based on an eight- neighbor region growing algorithm with left-right scann... To address the incomplete problem in pulmonary parenchyma segmentation based on the traditional methods, a novel automated segmentation method based on an eight- neighbor region growing algorithm with left-right scanning and four-corner rotating and scanning is proposed in this pa- per. The proposed method consists of four main stages: image binarization, rough segmentation of lung, image denoising and lung contour refining. First, the binarization of images is done and the regions of interest are extracted. After that, the rough segmentation of lung is performed through a general region growing method. Then the improved eight-neighbor region growing is used to remove noise for the upper, mid- dle, and bottom region of lung. Finally, corrosion and ex- pansion operations are utilized to smooth the lung boundary. The proposed method was validated on chest positron emis- sion tomography-computed tomography (PET-CT) data of 30 cases from a hospital in Shanxi, China. Experimental results show that our method can achieve an average volume overlap ratio of 96.21 ± 0.39% with the manual segmentation results. Compared with the existing methods, the proposed algorithm segments the lung in PET-CT images more efficiently and ac- curately. 展开更多
关键词 pulmonary parenchyma segmentation bot-tom region of lung image binarization iterative threshold seeded region growing four-corner rotating and scanning denoising contour refining PET-CT
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Exploring the synergy of building massing and façade design through evolutionary optimization 被引量:1
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作者 Likai Wang Han Zhang +1 位作者 Xuehan Liu guohua ji 《Frontiers of Architectural Research》 CSCD 2022年第4期761-780,共20页
In performance-based architectural design optimization, the design of building massings and façades is commonly separated, which weakens the effectiveness in performance improvement. In response, this study propo... In performance-based architectural design optimization, the design of building massings and façades is commonly separated, which weakens the effectiveness in performance improvement. In response, this study proposes a hybrid massing-façade integrated design generation and optimization workflow to integrate the two elements in an evolutionary design process. Compared with the existing approaches, the proposed workflow emphasizes the diversity of building design generation, with which various combinations of building massing forms and façade patterns can be systematically explored. Two case studies and a corresponding comparison study are presented to demonstrate the efficacy of the proposed workflow. Results show that the optimization can produce designs coupling the potential of building massings and façades in performance improvement. In addition, the optimization can provide information that supports early-stage architectural design exploration. Such information also enables the architect to understand the performance implications associated with the synergy of building massing and façade design. 展开更多
关键词 Performance-based design Building massing Façade DAYLIGHTING Solar irradiation Discomfort glare Design exploration Early design stage Design optimization EvoMass
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