A clustering fusion technique for MR brain tissue segmentation

Al-Dmour, Hayat; Al-Ani, Ahmed

doi:10.1016/j.neucom.2017.08.051

Cited by 45 publications

(17 citation statements)

References 31 publications

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“…Specificity quantifies the number of false positives (FPs), pixels that in truth do not belong to the region of interest but are classified as belonging to it; higher number of false positives lowers the Specificity . The Recall is a ratio of TPs to all positives, which is the sum of TPs and false negatives (FNs) [20, 31]. These indicators are calculated as follows:T17Dice=TP+TNTP+TN+FP+FNSensitivity=TPTP+FPSpecificity=FNTN+FNRecall=TPTP+FN…”

Section: Experimental Classification Results and Analysismentioning

confidence: 99%

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Brain Tumor Segmentation Based on Hybrid Clustering and Morphological Operations

Zhang

Shen

Cheng

et al. 2019

International Journal of Biomedical Imaging

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Inference of tumor and edema areas from brain magnetic resonance imaging (MRI) data remains challenging owing to the complex structure of brain tumors, blurred boundaries, and external factors such as noise. To alleviate noise sensitivity and improve the stability of segmentation, an effective hybrid clustering algorithm combined with morphological operations is proposed for segmenting brain tumors in this paper. The main contributions of the paper are as follows: firstly, adaptive Wiener filtering is utilized for denoising, and morphological operations are used for removing nonbrain tissue, effectively reducing the method’s sensitivity to noise. Secondly, K-means++ clustering is combined with the Gaussian kernel-based fuzzy C-means algorithm to segment images. This clustering not only improves the algorithm’s stability, but also reduces the sensitivity of clustering parameters. Finally, the extracted tumor images are postprocessed using morphological operations and median filtering to obtain accurate representations of brain tumors. In addition, the proposed algorithm was compared with other current segmentation algorithms. The results show that the proposed algorithm performs better in terms of accuracy, sensitivity, specificity, and recall.

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Section: Experimental Classification Results and Analysismentioning

confidence: 99%

“…The algorithm is called C-FAFCM. Al-Dmour and Al-Ani [20] proposed a fully automatic algorithm for brain tissue segmentation, based on the clustering fusion methodology. They combined three clustering techniques (K-means, FCM, and self-organizing map (SOM)) with neural network models for training and testing.…”

Section: Related Workmentioning

confidence: 99%

Brain Tumor Segmentation Based on Hybrid Clustering and Morphological Operations

Zhang

Shen

Cheng

et al. 2019

International Journal of Biomedical Imaging

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“…This method is inspired by the method presented in that is tried to improve its performance with some modifications. The next section examines the proposed method [25].…”

Section: Related Workmentioning

confidence: 99%

“…And, the input image is segmented into three clusters using each of these methods by considering the segmented image available in the dataset. Then, using the post processing method presented in [25], the clustering result is improved. In [25], post processing is done in the last test step.…”

Section: Ensemble Clusteringmentioning

confidence: 99%

EL-IGrowCut: MRI Brain Tissue Segmentation Based on Ensemble Learning and Improve GrowCut Techniques

Jafari¹,

Ghaffari²

2021

Preprint

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Background and ObjectiveMagnetic resonance (MR) technology enables physicians to employ digital imaging as a tool to identify and analyze brain tumors as well as to differentiate between healthy and tumor tissues. Since precisely defining the tumor position is a challenging task, segmentation is necessary stage in medical image processing. Segmentation is defined as the division of a given image into non-overlapping regions with relatively similar features. Recently, various techniques have been proposed for MR image segmentation. MethodsIn this paper, a new ensemble clustering technique based on neural network algorithm (NNA) was introduced for segmentation of MRI Brain tissues, which consists of two main phases: training phase and testing phase. In the former, pixels with the same amount of light intensity were put into one group via simple linear iterative clustering (SLIC) algorithm, creating superpixels. Then, the combination of GrowCut methods and Zernik’s feature extraction method and clustering and classification ensemble learning, including multi-layer perceptron neural network algorithm (MLP), Support vector machine (SVM) and K nearest neighbor (KNN), are based on geometric features in order to detect the division of MRI Brain tissues. The improved GrowCut method, based on the geometric properties of the images, has been used to segmentation the image of the brain from MRI images. Then, with the help of Zernik’s technique, image features were extracted based on the features of the edges and the texture of the images. Finally, the ensemble learning methods such as KNN, SVM, and MLP combine together for attempted to detect MRI Brain tissues. Results and ConclusionsThe performance of the proposed method was evaluated on the IBSR20 dataset and the obtained results were compared separately with the six basic clustering methods (that together are creating the ensemble clustering technique). The results demonstrated that the proposed technique outperforms other clustering algorithms in terms of segmentation accuracy in most of the experimental cases. Therefore, by presenting experiments to diagnose MRI Brain tissues, it was observed that the proposed method detected the MR with acceptable accuracy and the combination of ensemble learning methods and GrowCut and Zernik’s feature extraction methods was successful.

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“…Through merging pixels, the number of superpixels in the image is largely decreased, which greatly reduces the burden of subsequent processing and, therefore, improves the efficiency. Owing to these advantages, the superpixel has become an important part of computer vision algorithms and has been widely studied [13][14][15][16].…”

Section: Related Workmentioning

confidence: 99%

Image Segmentation of Brain MRI Based on LTriDP and Superpixels of Improved SLIC

Wang

Shen

2020

Brain Sciences

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Non-uniform gray distribution and blurred edges often result in bias during the superpixel segmentation of medical images of magnetic resonance imaging (MRI). To this end, we propose a novel superpixel segmentation algorithm by integrating texture features and improved simple linear iterative clustering (SLIC). First, a 3D histogram reconstruction model is used to reconstruct the input image, which is further enhanced by gamma transformation. Next, the local tri-directional pattern descriptor is used to extract texture features of the image; this is followed by an improved SLIC superpixel segmentation. Finally, a novel clustering-center updating rule is proposed, using pixels with gray difference with original clustering centers smaller than a predefined threshold. The experiments on the Whole Brain Atlas (WBA) image database showed that, compared to existing state-of-the-art methods, our superpixel segmentation algorithm generated significantly more uniform superpixels, and demonstrated the performance accuracy of the superpixel segmentation in both fuzzy boundaries and fuzzy regions.

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A clustering fusion technique for MR brain tissue segmentation

Cited by 45 publications

References 31 publications

Brain Tumor Segmentation Based on Hybrid Clustering and Morphological Operations

Brain Tumor Segmentation Based on Hybrid Clustering and Morphological Operations

EL-IGrowCut: MRI Brain Tissue Segmentation Based on Ensemble Learning and Improve GrowCut Techniques

Image Segmentation of Brain MRI Based on LTriDP and Superpixels of Improved SLIC

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