Image Segmentation of Brain MR Images Using Otsu’s Based Hybrid WCMFO Algorithm

In this study,a novel hybrid Water Cycle Moth-Flame Optimization(WCMFO)algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance(MR)image slices.WCMFO constitutes a hybrid between the two techniques,comprising the water cycle and moth-flame optimization algorithms.The optimal thresholds are obtained by maximizing the between class variance(Otsu’s function)of the image.To test the performance of threshold searching process,the proposed algorithm has been evaluated on standard benchmark of ten axial T2-weighted brain MR images for image segmentation.The experimental outcomes infer that it produces better optimal threshold values at a greater and quicker convergence rate.In contrast to other state-of-the-art methods,namely Adaptive Wind Driven Optimization(AWDO),Adaptive Bacterial Foraging(ABF)and Particle Swarm Optimization(PSO),the proposed algorithm has been found to be better at producing the best objective function,Peak Signal-to-Noise Ratio(PSNR),Standard Deviation(STD)and lower computational time values.Further,it was observed thatthe segmented image gives greater detail when the threshold level increases.Moreover,the statistical test result confirms that the best and mean values are almost zero and the average difference between best and mean value 1.86 is obtained through the 30 executions of the proposed algorithm.Thus,these images will lead to better segments of gray,white and cerebrospinal fluid that enable better clinical choices and diagnoses using a proposed algorithm.

Magnetic-resonance image segmentation based on improved variable weight multi-resolution Markov random field in undecimated complex wavelet domain

To solve the problem that the magnetic resonance(MR)image has weak boundaries,large amount of information,and low signal-to-noise ratio,we propose an image segmentation method based on the multi-resolution Markov random field(MRMRF)model.The algorithm uses undecimated dual-tree complex wavelet transformation to transform the image into multiple scales.The transformed low-frequency scale histogram is used to improve the initial clustering center of the K-means algorithm,and then other cluster centers are selected according to the maximum distance rule to obtain the coarse-scale segmentation.The results are then segmented by the improved MRMRF model.In order to solve the problem of fuzzy edge segmentation caused by the gray level inhomogeneity of MR image segmentation under the MRMRF model,it is proposed to introduce variable weight parameters in the segmentation process of each scale.Furthermore,the final segmentation results are optimized.We name this algorithm the variable-weight multi-resolution Markov random field(VWMRMRF).The simulation and clinical MR image segmentation verification show that the VWMRMRF algorithm has high segmentation accuracy and robustness,and can accurately and stably achieve low signal-to-noise ratio,weak boundary MR image segmentation.

Emulsifier-free Emulsion Polymerized Poly(MMA-HEMA-Eu(AA)3Phen)/Fe3O4 Magnetic Fluorescent Bifunctional Nanospheres for Magnetic Resonance and Optical Imaging

Integration of biocompatibility with superparamagnetic Fe3O4 nanoparticles and luminescence rare earth complexes Eu（AA）3Phen was carried out to form bifunctional nanospheres for using in bioimaging applications. The nanospheres Poly（MMA-HEMA-Eu（AA）3Phen）/Fe3O4 exhibit magnetic and fluorescent properties that are favorable for the use in drug delivery, magnetic separation and MR imaging for biomedical research. The TEM and SEM studies reveal that the bifunctional nanospheres have core-shell structure, in a spherical shape with a size ranging from 140 nm to 180 nm. In MRI experiments, a clear negative contrast enhancement in T2 images and the r2 reaches 568.82 （mmol·L^-1）^-1·s^-1. In vivo magnetic and fluorescence resonance imaging results suggest the nanospheres are able to preferentially accumulate in liver and spleen tissues to allow dual-modal detection of cancer cells in a living body.

Mn-doped Ti-based MOFs for magnetic resonance imaging-guided synergistic microwave thermal and microwave dynamic therapy of liver cancer

Currently,precise ablation of tumors without damaging the surrounding normal tissue is still an urgent problem for clinical microwave therapy of liver cancer.Herein,we synthesized Mn-doped Ti MOFs(Mn-Ti MOFs)nanosheets by in-situ doping method and applied them for microwave therapy.Infrared thermal imaging results indicate Mn-Ti MOFs can rapidly increase the temperature of normal saline,attributing to the porous structure improving microwave-induced ion collision frequency.Moreover,Mn-Ti MOFs show higher 1O2 output than Ti MOFs under 2 W of low-power microwave irradiation due to the narrower band-gap after Mn doping.At the same time,Mn endows the MOFs with a desirable T1 contrast of magnetic resonance imaging(r2/r1=2.315).Further,results on HepG2 tumor-bearing mice prove that microwave-triggered Mn-Ti MOFs nearly eradicate the tumors after 14 days of treatment.Our study offers a promising sensitizer for synergistic microwave thermal and microwave dynamic therapy of liver cancer.