Thalamic segmentation based on improved fuzzy connectedness in structural MRI

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.