A Modified Fuzzy C-Means Algorithm for Brain MR Image Segmentation and Bias Field Correction

In quantitative brain image analysis, accurate brain tissue segmentation from brain magnetic resonance image （MRI） is a critical step. It is considered to be the most important and difficult issue in the field of medical image processing. The quality of MR images is influenced by partial volume effect, noise, and intensity inhomogeneity, which render the segmentation task extremely challenging. We present a novel fuzzy c-means algorithm （RCLFCM） for segmentation and bias field correction of brain MR images. We employ a new gray-difference coefficient and design a new impact factor to measure the effect of neighbor pixels, so that the robustness of anti-noise can be enhanced. Moreover, we redefine the objective function of FCM （fuzzy c-means） by adding the bias field estimation model to overcome the intensity inhomogeneity in the image and segment the brain MR images simultaneously. We also construct a new spatial function by combining pixel gray value dissimilarity with its membership, and make full use of the space information between pixels to update the membership. Compared with other state-of-the-art approaches by using similarity accuracy on synthetic MR images with different levels of noise and intensity inhomogeneity, the proposed algorithm generates the results with high accuracy and robustness to noise.Jinan.

Predicting Infiltrative Hepatocellular Carcinoma Patient Outcome Post-TACE:MR Bias Field Correction Effect on 3D-quantitative Image Analysis

Background and Aims:To investigate the impact of MR bias field correction on response determination and survival prediction using volumetric tumor enhancement analysis in patients with infiltrative hepatocellular carcinoma,after transcatheter arterial chemoembolization(TACE).Methods:This study included 101 patients treated with conventional or drug-eluting beads TACE between the years of 2001 and 2013.Semi-automated 3D quantification software was used to segment and calculate the enhancing tumor volume(ETV)of the liver with and without bias-field correction on multi-phasic contrast-enhanced MRI before and 1-month after initial TACE.ETV(expressed as cm3)at baseline imaging and the relative change in ETV(as%change,ETV%)before and after TACE were used to predict response and survival,respectively.Statistical survival analyses included Kaplan-Meier curve generation and Cox proportional hazards modeling.Q statistics were calculated and used to identify the best cut-off value for ETV to separate responders and non-responders(ETV cm3).The difference in survival was evaluated between responders and non-responders using Kaplan-Meier and Cox models.Results:MR bias field correction correlated with improved response calculation from baseline MR as well as survival after TACE;using a 415 cm3 cut-off for ETV at baseline(hazard ratio:2.00,95%confidence interval:1.23-3.26,p=0.01)resulted in significantly improved response prediction(median survival in patients with baseline ETV<415 cm3:19.66 months vs.≥415 cm3:9.21 months,p<0.001,log-rank test).A≥41%relative decrease in ETV(hazard ratio:0.58,95%confidence interval:0.37-0.93,p=0.02)was significant in predicting survival(ETV≥41%:19.20 months vs.ETV<41%:8.71 months,p=0.008,log-rank test).Without MR bias field correction,response from baseline ETV could be predicted but survival after TACE could not.Conclusions:MR bias field correction improves both response assessment and accuracy of survival prediction using whole liver tumor enhancement analysis from baseline MR after initial TACE in patients with infiltrative hepatocellular carcinoma.