title: 3D Fast Automatic Segmentation of Kidney Based on Modified AAM and Random Forest.
abstract: In this paper, a fully automatic method is proposed to segment the kidney into multiple components: renal cortex, renal column, renal medulla and renal pelvis, in clinical 3D CT abdominal images. The proposed fast automatic segmentation method of kidney consists of two main parts: localization of renal cortex and segmentation of kidney components. In the localization of renal cortex phase, a method which fully combines 3D Generalized Hough Transform (GHT) and 3D Active Appearance Models (AAM) is applied to localize the renal cortex. In the segmentation of kidney components phase, a modified Random Forests (RF) method is proposed to segment the kidney into four components based on the result from localization phase. During the implementation, a multithreading technology is applied to speed up the segmentation process. The proposed method was evaluated on a clinical abdomen CT data set, including 37 contrast-enhanced volume data using leave-one-out strategy. The overall true-positive volume fraction and false-positive volume fraction were 93.15%, 0.37% for renal cortex segmentation; 83.09%, 0.97% for renal column segmentation; 81.92%, 0.55% for renal medulla segmentation; and 80.28%, 0.30% for renal pelvis segmentation, respectively. The average computational time of segmenting kidney into four components took 20 seconds.
authors.forename: Chao
authors.initials: C
authors.lastname: Jin
authors.forename: Fei
authors.initials: F
authors.lastname: Shi
authors.forename: Dehui
authors.initials: D
authors.lastname: Xiang
authors.forename: Xueqing
authors.initials: X
authors.lastname: Jiang
authors.forename: Bin
authors.initials: B
authors.lastname: Zhang
authors.forename: Ximing
authors.initials: X
authors.lastname: Wang
authors.forename: Weifang
authors.initials: W
authors.lastname: Zhu
authors.forename: Enting
authors.initials: E
authors.lastname: Gao
authors.forename: Xinjian
authors.initials: X
authors.lastname: Chen
mesh_terms: D003663:Decision Trees; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007668:Kidney / Q000000981:diagnostic imaging*; D000069550:Machine Learning; D008962:Models, Theoretical; D011860:Radiography, Abdominal; D014057:Tomography, X-Ray Computed