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brain image segmentation Based on Multi-Weight Probability Map

IEEE ACCESS 2019年 7卷 14736-14746页

作者： Liu, Dan Yu, Xiaoe Feng, Qianjin Chen, Wufan Manogaran, Gunasekaran Southern Med Univ Dept Biomed Engn Guangzhou 510515 Guangdong Peoples R China Univ Calif Davis John Muir Inst Environm Davis CA 95616 USA

Due to the complexity of the brain image itself, the brain image segmentation technology has become a bottle for further application and development of the system. Considering the inconsistency of intensity, partial volume effect, and noise in medical images, this paper studies the brain image segmentation technology based on the multi-weight probability. The multi-weight probability method mainly models the data set with outliers and non-Gaussian noise. First, the probabilistic local ELM model is established. Based on this, the Parzen window method is used to establish the probability distribution of the local model, and then, the probability distribution is used as the weight to fuse. All local models are used to build a global robustness model. The method successfully applied the brain and UCI examples and compared with traditional ELM, regularized ELM, and robust ELM. The results show that the probability weight ELM shows better modeling performance.

关键词： brain image segmentation multi-weight probability algorithm partial volume effect pixel correlation

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brain image segmentation Based on FCM Clustering Algorithm and Rough Set

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IEEE ACCESS 2019年 7卷 12386-12396页

作者： Huang, Hong Meng, Fanzhi Zhou, Shaohua Jiang, Feng Manogaran, Gunasekaran Southwest Univ Sci & Technol Sch Comp Sci & Technol Mianyang 621010 Peoples R China China Acad Engn Phys Inst Comp Applicat Mianyang 621900 Peoples R China Harbin Inst Technol Sch Comp Harbin 150001 Heilongjiang Peoples R China Univ Calif Davis John Muir Inst Environm Davis CA 95616 USA

In this paper, a new image segmentation method is proposed by combining the FCM clustering algorithm with a rough set theory. First, the attribute value table is constructed based on the segmentation results of FCM under different clustering numbers, and the image is divided into several small regions based on the indistinguishable relationship of attributes. Then, the weight values of each attribute are obtained by value reduction and used as the basis to calculate the difference between regions and then the similarity evaluation of each region is realized through the equivalence relationship defined by the difference degree. Finally, the final equivalence relation defined by similarity is used to merge regions and complete image segmentation. This method is validated in the segmentation of artificially generated images, brain CT images, and MRI images. The experimental results show that compared with the FCM method, the proposed method can reduce the error rate and achieve better segmentation results for the fuzzy boundary region. And, the experimental results also prove that the algorithm has strong anti-noise ability.

关键词： brain image segmentation FCM clustering rough set system

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brain image segmentation using semi-supervised clustering

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 52卷 50-63页

作者： Saha, Sriparna Alok, Abhay Kumar Ekbal, Asif Indian Inst Technol Dept Comp Sci Engn Patna 801103 Bihar India

The objective of brain image segmentation is to partition the brain images into different non-overlapping homogeneous regions representing the different anatomical structures. Magnetic resonance brain image segmentation has large number of applications in diagnosis of neurological disorders like Alzheimer diseases, Parkinson related syndrome etc. But automatically segmenting the MR brain image is not an easy task. To solve this problem, several unsupervised and supervised based classification techniques have been developed in the literature. But supervised classification techniques are more time consuming and cost-sensitive due to the requirement of sufficient labeled data. In contrast, unsupervised classification techniques work without using any prior information but it suffers from the local trap problems. So, to overcome the problems associated with unsupervised and supervised classification techniques, we have proposed a new semi-supervised clustering technique using the concepts of multiobjective optimization and applied this technique for automatic segmentation of MR brain images in the intensity space. Multiple centers are used to encode a cluster in the form of a string. The proposed clustering technique utilizes intensity values of the brain pixels as the features. Additionally it also assumes that the actual class label information of 10% points of a particular image data set is also known. Three cluster validity indices are utilized as the objective functions, which are simultaneously optimized using AMOSA, a modern multi objective optimization technique based on the concepts of simulated annealing. First two cluster validity indices are symmetry distance based Sym-index and Euclidean distance based I-index, which are based on unsupervised properties. Last one is a supervised information based cluster validity index, Minkowski Index. The effectiveness of this proposed semi-supervised clustering technique is demonstrated on several simulated MR normal bra

关键词： brain image segmentation Multiobjective optimization Semi-supervised clustering AMOSA Cluster validity index Sym-index I-index MS-index

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Application of Partial Differential Equation in brain image segmentation and Registration

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IEEE ACCESS 2019年 7卷 19631-19642页

作者： Tian, Chong Zhang, Haixia Zhoukou Normal Univ Sch Math & Stat Zhoukou 466001 Peoples R China Baoji Univ Arts & Sci Coll Fine Arts Baoji 721013 Peoples R China

Due to the problem that local binary fitting model is easy to fall into local extremum, based on the hypothesis that the brightness of the image on both sides of the target boundary has contrast, an improved method of adding contrast constraints to the fitting function is proposed in this paper. The contrast-constrained model can effectively overcome the problem that energy is easily trapped in local extremum, but there is still a problem that requires the initialization curve to be close to the edge of the target. Therefore, balloon force is added to the contrast-constrained model, which enlarges the scope of external force and enhances the robustness of the model to initialization. Meanwhile, the diffusion filtering method with adaptive image structure is introduced into image registration, and the anisotropic diffusion function with the ability of feature preservation and consistency enhancement is defined as the regularization term of the model. The improved model can effectively maintain image features and achieve accurate registration of complex images. In this paper, brain image (MRI) in Mcgill database is taken as an example to verify the simulation results. The simulation results verify the reliability and validity of the partial differential equation-based algorithm.

关键词： brain image segmentation partial differential equation (PDE) LBF model diffusion filtering method image registration

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Fuzzy clustering using gravitational search algorithm for brain image segmentation

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第20期81卷 29633-29652页

作者： Hooda, Heena Verma, Om Prakash Delhi Technol Univ Dept IT Delhi India Delhi Technol Univ Dept ECE Delhi India

Clustering is a key activity in numerous data mining applications such as information retrieval, text mining, image segmentation. Clustering also plays a major role in medical image processing. Manual image segmentation is very tedious and time consuming task and the results of manual segmentation are subjected to errors due to huge and varying data. Therefore, automated segmentation systems are gaining enormous importance nowadays. This paper presents an automated system for segmentation of brain tissues namely white matter, gray matter and cerebrospinal fluid from brain MRI images. In this work, we propose a novel clustering approach, Fuzzy-Gravitational Search Algorithm(GSA) for MRI brain image segmentation. The proposed approach is based on GSA, and uses fuzzy inference rules for controlling the parameter alpha as search progresses. The results of the system are compared with GSA and recent work on brain image segmentation algorithms for both real and simulated database on the basis of Dice Coefficient values. The performance of the Fuzzy-GSA algorithm is also evaluated against four benchmark datasets from the UC Irvine repository. The results illustrate that the Fuzzy-GSA approach attains the highest quality clustering over the selected datasets when compared with several other clustering algorithms.

关键词： Magnetic resonance imaging brain image segmentation Gravitational search algorithm Fuzzy clustering Fuzzy inference rules

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MR brain image segmentation based on Unsupervised and Semi-Supervised Fuzzy Clustering Methods

MR Brain Image Segmentation based on Unsupervised and Semi-S...

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International Conference on Digital image Computing - Techniques and Applications (DICTA)

作者： Al-Dmour, Hayat Al-Ani, Ahmed Univ Technol Sydney Sch Elect Mech & Mechatron Syst Sydney NSW 2007 Australia

ISBN: (纸本)9781509028962

In medical imaging applications, the segmentation of Magnetic Resonance (MR) brain images plays a crucial role for measuring and visualizing the anatomical structures of interest. In general, the brain image segmentation aims to divide the image pixels into non-overlapping homogeneous regions for analyzing the changes in the brain for surgical planning. Several supervised and unsupervised clustering methods have been developed over the years to segment the magnetic resonance brain image. However, most of these methods have certain limitations such as requiring user interaction and high computational complexity. In this context, this paper proposes a methodology that combines semi-supervised and unsupervised classification techniques for achieving efficient and fully-automatic segmentation of brain images. Firstly, the algorithm applies a median filter to remove the noise inherent in MR images prior to the clustering step. Secondly, the background of the MR image is removed by using a global thresholding technique. Thirdly, we utilize the subtractive clustering method to overcome the deficiency of randomly initialized Fuzzy C-Means (FCM) parameters. This method is used for estimating the clustering number and to generate the initial centers, which is used as initialization parameter for FCM clustering. Finally, a semi-supervised algorithm with Standard Fuzzy Clustering is selected to divide the brain MR image into different classes based on the generated membership function from FCM. The efficiency of the proposed method is demonstrated on various MR brain images and compared with some of the well-known clustering techniques.

关键词： brain image segmentation Magnetic Resonance Imaging Fuzzy clustering Semi-supervised fuzzy clustering

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Robust Intuitionistic Fuzzy c-means Clustering Algorithm for brain image segmentation 7

Robust Intuitionistic Fuzzy c-means Clustering Algorithm for...

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7th IEEE International Conference on Communication and Signal Processing (IEEE ICCSP)

作者： Monalisa, Achalla Swathi, Dasari Karuna, Yepuganti Saladi, Saritha Vellore Inst Technol Sch Elect & Commun Engn Vellore Tamil Nadu India

ISBN: (纸本)9781538635216

The segmentation of the human brain magnetic resonance imaging (MRI) plays a highly decisive role in diagnosing numerous diseases like tumors, Alzheimer's disease, edema, dementia etc. But it is a very challenging task because of presence of noise in the MRI images and also because the boundaries between different tissues of the brain cannot be easily distinguished. Standard fuzzy c-means clustering (FCM) method is proposed to segment the brain MRI accurately and to handle the noise. There are many variants of FCM and one such variant is the Intuitionistic fuzzy c-means clustering algorithm (IFCM). It incorporates the advantages of intuitionistic fuzzy set theory. The IFCM handles the uncertainty, but is not robust to noise as it does not consider any local spatial information. Hence, in this paper a novel approach, namely the Robust and improved intuitionistic fuzzy c-means clustering algorithm (RIIFCM) is proposed. This algorithm is robust to noise as it considers local spatial information. We have demonstrated the efficiency of the RIIFCM algorithm compared to six other algorithms used for the brain image segmentation. The segmentation is carried out on a simulated MRI brain image and we demonstrate that the RIIFCM algorithm outperforms the other existing algorithms by calculating the similarity indices, false positive ratio (FPR) and false negative ratio (FNR).

关键词： brain image segmentation Fuzzy c-means Intuitionistic fuzzy c-means Magnetic Resonance Imaging

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Taguchi Design for Setting EHO Variants Parameters: Application in brain image segmentation Using HMRF

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SN Computer Science 2023年第6期4卷 1-18页

作者： Mahiou, Ramdane Guerrout, EL-Hachemi Sannef, Mohammed Elhadi Laboratoire LMCS Ecole nationale Supérieure en Informatique Oued-Smar Algiers Algeria

Approximation methods are employed to remedy the significant computational time required by exact methods. They provide an approximate solution, close to the optimum, for complex problems (NP-hard problems). The performance guarantees of these methods vary depending on the chosen parameter values. However, selecting parameter values that yield good performance is a challenging task. Since the 1950s, Genichi Taguchi, a Japanese engineer, has developed robust design techniques to improve the quality of manufactured goods. Recently, these techniques have found applications in various domains, including engineering and biotechnology. In this paper, we investigate the application of the Taguchi design for the first time to simplify the process of selecting suitable parameter values for Elephant Herding Optimization (EHO) variants. These later are recent and known by their simplicity and ease implementation. A study case of using EHO variants is the segmentation of brain Magnetic Resonance (MR) images using Hidden Markov Random Fields (HMRF) to help physician get the right decision. HMRF is a powerful model widely used for segmenting brain MR images which can be formulated as a problem of minimization an objective function. Subsequently, we compare the obtained results with those of well-known brain images’ segmentation tools such as FSL (the Functional Magnetic Resonance Imaging of the brain Software Library) to demonstrate the usefulness of the Taguchi design. The quality of the segmentation is measured and tested using the Dice coefficient criterion and established on brainWeb and IBSR images. Our findings indicate that the Taguchi design is effective, and Sine Cosine EHO (SCEHO) and Enhanced EHO (EEHO) methods yield excellent results based on the conducted tests. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： brain image segmentation Dice Coefficient metric Elephant Herding Optimization Hidden Markov Random Field Optimization Taguchi design

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An improved label fusion approach with sparse patch-based representation for MRI brain image segmentation

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2017年第1期27卷 23-32页

作者： Yan, Meng Liu, Hong Xu, Xiangyang Song, Enmin Qian, Yuejing Pan, Ning Jin, Renchao Jin, Lianghai Cheng, Shaorong Hung, Chih-Cheng Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Hubei Peoples R China Hubei Key Lab Med Informat Anal & Tumor Diag Trea Wuhan 430074 Hubei Peoples R China Zhejiang Ind & Trade Vocat Coll Wenzhou 325000 Zhejiang Peoples R China South Cent Univ Natl Coll Biomed Engn Wuhan 430074 Hubei Peoples R China Wuhan Gen Hosp Guangzhou Mil Dept Radiol Wuhan 430074 Hubei Peoples R China Kennesaw State Univ Ctr Machine Vision & Secur Res Georgia GA 30144 USA

The multi-atlas patch-based label fusion (LF) method mainly focuses on the measurement of the patch similarity which is the comparison between the atlas patch and the target patch. To enhance the LF performance, the distribution probability about the target can be used during the LF process. Hence, we consider two LF schemes: in the first scheme, we keep the results of the interpolation so that we can obtain the labels of the atlas with discrete values (between 0 and 1) instead of binary values in the label propagation. In doing so, each atlas can be treated as a probability atlas. Second, we introduce the distribution probability of the tissue (to be segmented) in the sparse patch-based LF process. Based on the probability of the tissue and sparse patch-based representation, we propose three different LF methods which are called LF-Method-1, LF-Method-2, and LF-Method-3. In addition, an automated estimation method about the distribution probability of the tissue is also proposed. To evaluate the accuracy of our proposed LF methods, the methods were compared with those of the nonlocal patch-based LF method (Nonlocal-PBM), the sparse patch-based LF method (Sparse-PBM), majority voting method, similarity and truth estimation for propagated segmentations, and hierarchical multi-atlas LF with multi-scale feature representation and label-specific patch partition (HMAS). Based on our experimental results and quantitative comparison, our methods are promising in the magnetic resonance image segmentation. (C) 2017 Wiley Periodicals, Inc.

关键词： brain image segmentation sparse representation patch-based segmentation distribution probability label fusion

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Hidden Markov random field model and Broyden-Fletcher-Goldfarb-Shanno algorithm for brain image segmentation

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2018年第3期30卷 415-427页

作者： Guerrout, EL-Hachemi Ait-Aoudia, Samy Michelucci, Dominique Mahiou, Ramdane Ecole Natl Super Informat Lab LMCS Oued Smar Algeria Univ Bourgogne Lab LE2I Dijon France

Many routine medical examinations produce images of patients suffering from various pathologies. With the huge number of medical images, the manual analysis and interpretation became a tedious task. Thus, automatic image segmentation became essential for diagnosis assistance. segmentation consists in dividing the image into homogeneous and significant regions. We focus on hidden Markov random fields referred to as HMRF to model the problem of segmentation. This modelisation leads to a classical function minimisation problem. Broyden-Fletcher-Goldfarb-Shanno algorithm referred to as BFGS is one of the most powerful methods to solve unconstrained optimisation problem. In this paper, we investigate the combination of HMRF and BFGS algorithm to perform the segmentation operation. The proposed method shows very good segmentation results comparing with well-known approaches. The tests are conducted on brain magnetic resonance image databases (brainWeb and IBSR) largely used to objectively confront the results obtained. The well-known Dice coefficient (DC) was used as similarity metric. The experimental results show that, in many cases, our proposed method approaches the perfect segmentation with a Dice Coefficient above .9. Moreover, it generally outperforms other methods in the tests conducted.

关键词： brain image segmentation hidden Markov random field Broyden-Fletcher-Goldfarb-Shanno algorithm Dice coefficient criterion

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