计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2015年
9期
152-157
,共6页
肝脏%图像分割%区域填充%活动轮廓模型
肝髒%圖像分割%區域填充%活動輪廓模型
간장%도상분할%구역전충%활동륜곽모형
liver%image segmentation%region filling%active contour model
肝脏模型的个性化是肝脏虚拟手术系统中的一个关键技术,而肝脏模型的个性化又是以肝脏CT图像的三维分割为前提的。针对B-Snake模型的特点,提出一种结合区域填充的改进B-Snake模型图像分割算法。将相邻的上一张切片的分割结果映射到当前切片上,根据一定的规则进行区域填充,并将填充后的结果与前一张切片的分割结果按一定的算法进行比较,进一步优化。得到的初始轮廓很接近肝脏的真实边界,而且大部分曲线已在边界上,将其作为改进的B-Snake模型算法的初始轮廓,只需对其进行部分控制点的优化调整,就可得到准确的分割结果。以此类推,直到处理完所有切片图。实验表明,该算法能有效提高分割的准确度,获得较满意的分割结果。
肝髒模型的箇性化是肝髒虛擬手術繫統中的一箇關鍵技術,而肝髒模型的箇性化又是以肝髒CT圖像的三維分割為前提的。針對B-Snake模型的特點,提齣一種結閤區域填充的改進B-Snake模型圖像分割算法。將相鄰的上一張切片的分割結果映射到噹前切片上,根據一定的規則進行區域填充,併將填充後的結果與前一張切片的分割結果按一定的算法進行比較,進一步優化。得到的初始輪廓很接近肝髒的真實邊界,而且大部分麯線已在邊界上,將其作為改進的B-Snake模型算法的初始輪廓,隻需對其進行部分控製點的優化調整,就可得到準確的分割結果。以此類推,直到處理完所有切片圖。實驗錶明,該算法能有效提高分割的準確度,穫得較滿意的分割結果。
간장모형적개성화시간장허의수술계통중적일개관건기술,이간장모형적개성화우시이간장CT도상적삼유분할위전제적。침대B-Snake모형적특점,제출일충결합구역전충적개진B-Snake모형도상분할산법。장상린적상일장절편적분할결과영사도당전절편상,근거일정적규칙진행구역전충,병장전충후적결과여전일장절편적분할결과안일정적산법진행비교,진일보우화。득도적초시륜곽흔접근간장적진실변계,이차대부분곡선이재변계상,장기작위개진적B-Snake모형산법적초시륜곽,지수대기진행부분공제점적우화조정,취가득도준학적분할결과。이차유추,직도처리완소유절편도。실험표명,해산법능유효제고분할적준학도,획득교만의적분할결과。
The personalization of liver models, which is premised on the 3d segmentation of liver CT images, is a key technology in the virtual surgery of liver. Considering the features of B-Snake model, this paper presents an improved B-Snake segmentation algorithm combined with Region Filling. The contour of the adjacent and processed section is mapped on the current section. Based on the contour, it gets a connected region according to Region Filling algorithm and compares the region with the liver region of the adjacent and processed section according to certain algorithm in order to obtain a more accurate contour. The resulting contour is close to the liver boundary, and large amount of the control points are on the right boundary. Then, the contour is served as the initial contour of the improved B-Snake algorithm for further processing, resulting in the final segmentation result after the evolution of part of the initial contour. The algorithm will not end untill all sections are processed. Experimental results show that the algorithm can obtain segmentaion result of liver CT images efficiently and accurately.