MRI brain segmentation in combination of clustering methods with Markov random field

Saladi, Saritha; Prabha, N. Amutha

doi:10.1002/ima.22271

Cited by 12 publications

(4 citation statements)

References 27 publications

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“…Saladi et al used the Markov random field (MRF) modeling method for detecting and segmenting the tumor regions. The authors developed multimodel transformation approach using clustering technique for identifying the tumor regions in an effective manner using MRF method.…”

Section: Discussionmentioning

confidence: 99%

Image registration‐based brain tumor detection and segmentation using ANFIS classification approach

Nagarathinam¹,

Ponnuchamy

2019

Int J Imaging Syst Tech

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Abnormal cells in human brain lead to the development of tumors. Manual detection of this tumor region is a time‐consuming process. Hence, this paper proposes an efficient and automated computer‐aided methodology for brain tumor detection and segmentation using image registration technique and classification approaches. This proposed work consists of the following modules: image registration, contourlet transform, and feature extraction with feature normalization, classification, and segmentation. The extracted features are optimized using genetic algorithm, and then an adaptive neuro‐fuzzy inference system classification approach is used to classify the features for the detection and segmentation of tumor regions in brain magnetic resonance imaging. A quantitative analysis is performed to evaluate the proposed methodology for brain tumor detection using sensitivity, specificity, segmentation accuracy, precision, and Dice similarity coefficient.

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Section: Discussionmentioning

confidence: 99%

Image registration‐based brain tumor detection and segmentation using ANFIS classification approach

Nagarathinam¹,

Ponnuchamy

2019

Int J Imaging Syst Tech

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“…Thereafter, the spatial constraints between neighbouring pixels in the image is modelled as a potential function in the Markov random field to reduce noise and enhance image quality. Another contribution (Saladi & Amutha Prabha, 2018) applies a modified fuzzy algorithm to estimate an initial segmentation parameters which are further applied to a Markov random field‐based post processing. The contribution by Jafri et al (2017) employs the expectation maximization algorithm to estimate hidden markov random field model of an image which also serves on the initial segmentation which is further refined using simple processing techniques.…”

Section: Related Workmentioning

confidence: 99%

Enhancement of clustering techniques by coupling clustering tree and neural network: Application to brain tumour segmentation

et al. 2022

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Currently, no classical clustering algorithm is efficient on its own. The predefined number of clusters required for their operation does not consistently produce satisfactory segmentation results. They exhibit cluster instability, are vulnerable to the local optimum trap, and are sensitive to noise and imaging artefacts. Most contributions designed to overcome these drawbacks incorporate prior knowledge such as cluster label information and statistic measures that demand minimal labelled training data. Although these approaches improve the segmentation accuracy, they tend to diminish the advantages of clustering algorithms over the supervised learning methods. This study proposes a shift from the use of a predefined number of clusters to a clustering tree-based method for performance enhancement of classical clustering algorithms. The proposed method is a three-stage algorithm. It begins with the extraction of low-level features from a clustering tree. Clustering trees are sets of labelled clusters of an image at multiple clustering resolutions. The second stage extracts high-level features by coupling the clustering tree to a single-layer feedforward neural network. The third stage is the classification stage, where the basic model of a neural network extracts the tumour from a high-level feature map.Because neither of the neural networks requires training, the proposed method is both fully unsupervised and fully automated and retains all its advantages over supervised methods. A performance evaluation using FLAIR MRI images of brain tumour patients from the BRATS2015 and BRATS2020 databases demonstrates significant performance enhancement over four classical clustering algorithms and two of the four proposed techniques were comparable to deep learning methods.

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“…N ( a j ) is the square window centered on pixel/voxel a j in spatial domain and y, m are parameters of the window. The spatial function improves the inventive membership value, and the clustering results in residues unaffected 25‐27 . MFCM Algorithm steps are:…”

Section: Proposed Modified Fuzzy C Means Algorithmmentioning

confidence: 99%

Segmentation of tumor using PCA based modified fuzzy C means algorithms on MR brain images

Karuna

Saritha²,

Narasimhulu³

2020

Int J Imaging Syst Tech

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In the field of medical sciences, automatic detection of tumor using magnetic resonance (MR) brain images is a major research area. The goal of the proposed work is to identify the tumors in MR images using segmentation methods and to locate the affected regions of the brain more accurately. Medical images have vast information but they are difficult to examine with lesser computational time. An innovative process is proposed to extract tumor cells using the discrete wavelet transform (DWT). After extracting features with DWT feature reduction is carried out with the principal component analysis (PCA). Modified fuzzy C means (MFCM) technique is used for segmenting the tumor cells. The efficiency of the proposed method to identify different abnormalities in real MR images for intracranial neoplasm detection, tuberculoma, and bilateral thalamic fungal granulomas identification is tested. The results obtained are shown in‐terms of Accuracy, Dice Similarity Index (DSI), and Jaccard Index (JI) measures. The performance of the proposed method is tested in terms of performance measures like Accuracy, DSI, and JI. These results are compared with the conventional fuzzy C means (FCM) method.

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MRI brain segmentation in combination of clustering methods with Markov random field

Cited by 12 publications

References 27 publications

Image registration‐based brain tumor detection and segmentation using ANFIS classification approach

Image registration‐based brain tumor detection and segmentation using ANFIS classification approach

Enhancement of clustering techniques by coupling clustering tree and neural network: Application to brain tumour segmentation

Segmentation of tumor using PCA based modified fuzzy C means algorithms on MR brain images

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