title: Thalamic segmentation based on improved fuzzy connectedness in structural MRI.
abstract: Thalamic segmentation serves an important function in localizing targets for deep brain stimulation (DBS). However, thalamic nuclei are still difficult to identify clearly from structural MRI. In this study, an improved algorithm based on the fuzzy connectedness framework was developed. Three-dimensional T1-weighted images in axial orientation were acquired through a 3D SPGR sequence by using a 1.5 T GE magnetic resonance scanner. Twenty-five normal images were analyzed using the proposed method, which involved adaptive fuzzy connectedness combined with confidence connectedness (AFCCC). After non-brain tissue removal and contrast enhancement, the seed point was selected manually, and confidence connectedness was used to perform an ROI update automatically. Both image intensity and local gradient were taken as image features in calculating the fuzzy affinity. Moreover, the weight of the features could be automatically adjusted. Thalamus, ventrointermedius (Vim), and subthalamic nucleus were successfully segmented. The results were evaluated with rules, such as similarity degree (SD), union overlap, and false positive. SD of thalamus segmentation reached values higher than 85%. The segmentation results were also compared with those achieved by the region growing and level set methods, respectively. Higher SD of the proposed method, especially in Vim, was achieved. The time cost using AFCCC was low, although it could achieve high accuracy. The proposed method is superior to the traditional fuzzy connectedness framework and involves reduced manual intervention in time saving.
authors.affiliation: College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100022, China. Electronic address: clyang@bjut.edu.cn.
authors.forename: Chunlan
authors.initials: C
authors.lastname: Yang
authors.affiliation: College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100022, China.
authors.forename: Qian
authors.initials: Q
authors.lastname: Wang
authors.affiliation: College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100022, China.
authors.forename: Weiwei
authors.initials: W
authors.lastname: Wu
authors.affiliation: Department of Radiotherapy, Beijing Geriatric Hospital, Beijing 100095, China.
authors.forename: Yanqing
authors.initials: Y
authors.lastname: Xue
authors.affiliation: Center of Neurosurgery, PLA NAVY General Hospital, Beijing 100037, China.
authors.forename: Wangsheng
authors.initials: W
authors.lastname: Lu
authors.affiliation: College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100022, China.
authors.forename: Shuicai
authors.initials: S
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods; D003287:Contrast Media / Q000737:chemistry; D046690:Deep Brain Stimulation; D005189:False Positive Reactions; D017143:Fuzzy Logic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D020531:Subthalamic Nucleus / Q000502:physiology; D013788:Thalamus / Q000502:physiology; D020651:Ventral Thalamic Nuclei / Q000502:physiology
chemical_list: D003287:Contrast Media
keywords: Fuzzy connectedness; Magnetic resonance imaging (MRI); Segmentation; Thalamic