Diagnosing data or object detection in medical images is one of the important parts of image segmentation especially those data which is less effective to identify inMRI such as low-grade tumors or cerebral spinal flu...Diagnosing data or object detection in medical images is one of the important parts of image segmentation especially those data which is less effective to identify inMRI such as low-grade tumors or cerebral spinal fluid(CSF)leaks in the brain.The aim of the study is to address the problems associated with detecting the low-grade tumor and CSF in brain is difficult in magnetic resonance imaging(MRI)images and another problem also relates to efficiency and less execution time for segmentation of medical images.For tumor and CSF segmentation using trained light field database(LFD)datasets of MRI images.This research proposed the new framework of the hybrid k-Nearest Neighbors(k-NN)model that is a combination of hybridization of Graph Cut and Support Vector Machine(GCSVM)and Hidden Markov Model of k-Mean Clustering Algorithm(HMMkC).There are four different methods are used in this research namely(1)SVM,(2)GrabCut segmentation,(3)HMM,and(4)k-mean clustering algorithm.In this framework,on the one hand,phase one is to perform the classification of SVM and Graph Cut algorithm to create the maximum margin distance.This research use GrabCut segmentation method which is the application of the graph cut algorithm and extract the data with the help of scaleinvariant features transform.On the other hand,in phase two,segment the low-grade tumors and CSF using a method adapted for HMkC and extract the information of tumor or CSF fluid by GCHMkC including iterative conditional maximizing mode(ICMM)with identifying the range of distant.Comparative evaluation is also performing by the comparison of existing techniques in this research.In conclusion,our proposed model gives better results than existing.This proposed model helps to common man and doctor that can identify their condition of brain easily.In future,this will model will use for other brain related diseases.展开更多
trast （HC） method is proposed to define saliency value of each pixel, then auto Grabcut segmenta- tion method is used to segment the salient region so as to obtain a region of interest （ROI）. After that, normalize...trast （HC） method is proposed to define saliency value of each pixel, then auto Grabcut segmenta- tion method is used to segment the salient region so as to obtain a region of interest （ROI）. After that, normalized histograms and cumulative histograms for ROI and region of background （ROB） are calculated. The mapping functions of the corresponding regions are derived from reference image to distorted image through the nearest cumulative histogram matching method, so that color correction can be finally achieved. Experimental results show that benefitting from the separate treatment to ROI and ROB, the proposed color correction method could avoid error propagation between the two different regions, which achieves good color correction result in comparison with other correction methods.展开更多
文摘Diagnosing data or object detection in medical images is one of the important parts of image segmentation especially those data which is less effective to identify inMRI such as low-grade tumors or cerebral spinal fluid(CSF)leaks in the brain.The aim of the study is to address the problems associated with detecting the low-grade tumor and CSF in brain is difficult in magnetic resonance imaging(MRI)images and another problem also relates to efficiency and less execution time for segmentation of medical images.For tumor and CSF segmentation using trained light field database(LFD)datasets of MRI images.This research proposed the new framework of the hybrid k-Nearest Neighbors(k-NN)model that is a combination of hybridization of Graph Cut and Support Vector Machine(GCSVM)and Hidden Markov Model of k-Mean Clustering Algorithm(HMMkC).There are four different methods are used in this research namely(1)SVM,(2)GrabCut segmentation,(3)HMM,and(4)k-mean clustering algorithm.In this framework,on the one hand,phase one is to perform the classification of SVM and Graph Cut algorithm to create the maximum margin distance.This research use GrabCut segmentation method which is the application of the graph cut algorithm and extract the data with the help of scaleinvariant features transform.On the other hand,in phase two,segment the low-grade tumors and CSF using a method adapted for HMkC and extract the information of tumor or CSF fluid by GCHMkC including iterative conditional maximizing mode(ICMM)with identifying the range of distant.Comparative evaluation is also performing by the comparison of existing techniques in this research.In conclusion,our proposed model gives better results than existing.This proposed model helps to common man and doctor that can identify their condition of brain easily.In future,this will model will use for other brain related diseases.
基金Supported by the Natural Science Foundation of China(No.61311140262,61171163,61271021)
文摘trast （HC） method is proposed to define saliency value of each pixel, then auto Grabcut segmenta- tion method is used to segment the salient region so as to obtain a region of interest （ROI）. After that, normalized histograms and cumulative histograms for ROI and region of background （ROB） are calculated. The mapping functions of the corresponding regions are derived from reference image to distorted image through the nearest cumulative histogram matching method, so that color correction can be finally achieved. Experimental results show that benefitting from the separate treatment to ROI and ROB, the proposed color correction method could avoid error propagation between the two different regions, which achieves good color correction result in comparison with other correction methods.
