Extraction of tumours from MR images of the brain by texture and clustering

Batista, João Luı́s Ferreira; Kitney, R.I.

doi:10.1007/3-540-60298-4_264

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…[ 1 2 3 4 5 6 7 8 9 10 11 12 ] It is used for rapid medical treatment because of the importance of correct diagnosis of brain disease. [ 13 14 15 16 ] In addition, as diagnosis becomes easier with image processing techniques, routine tests and checkups for patients can be performed more quickly and easily. There are a number of brain diseases that alter brain tissues in some areas.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Feature Extraction Methods Based on Dual-Tree Complex Wavelet Transform Subimages of Brain Magnetic Resonance Imaging for the Classification of Multiple Diseases

Bazdar

Hatamian

Ostadieh

et al. 2023

Journal of Medical Signals &Amp; Sensors

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It has been a long time since we use magnetic resonance imaging (MRI) to detect brain diseases and many useful techniques have been developed for this task. However, there is still a potential for further improvement of classification of brain diseases in order to be sure of the results. In this research we presented, for the first time, a non-linear feature extraction method from the MRI sub-images that are obtained from the three levels of the two-dimensional Dual tree complex wavelet transform (2D DT-CWT) in order to classify multiple brain disease. After extracting the non-linear features from the sub-images, we used the spectral regression discriminant analysis (SRDA) algorithm to reduce the classifying features. Instead of using the deep neural networks that are computationally expensive, we proposed the Hybrid RBF network that uses the k-means and recursive least squares (RLS) algorithm simultaneously in its structure for classification. To evaluate the performance of RBF networks with hybrid learning algorithms, we classify nine brain diseases based on MRI processing using these networks, and compare the results with the previously presented classifiers including, supporting vector machines (SVM) and K-nearest neighbour (KNN). Comprehensive comparisons are made with the recently proposed cases by extracting various types and numbers of features. Our aim in this paper is to reduce the complexity and improve the classifying results with the hybrid RBF classifier and the results showed 100 percent classification accuracy in both the two class and the multiple classification of brain diseases in 8 and 10 classes. In this paper, we provided a low computational and precise method for brain MRI disease classification. the results show that the proposed method is not only accurate but also computationally reasonable.

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Section: Introductionmentioning

confidence: 99%

Nonlinear Feature Extraction Methods Based on Dual-Tree Complex Wavelet Transform Subimages of Brain Magnetic Resonance Imaging for the Classification of Multiple Diseases

Bazdar

Hatamian

Ostadieh

et al. 2023

Journal of Medical Signals &Amp; Sensors

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“…It tends to perform better in narrow domains, e.g., medical images, where visual variability is limited. Most previous work on automated brain tumor segmentation is based on machine learning, both supervised [1][2][3][4][5][6][7][8] and unsupervised [9][10][11][12][13][14][15][16][17]. Among the supervised methods, approaches include using Support Vector machines [3,4], Bayesian classifier [5], fractal features [6], outlier detection [7], Markov Random Fields [8].…”

Section: Introductionmentioning

confidence: 99%

“…Because of its conceptual simplicity K-means is the best known and most commonly used clustering algorithm, which is reflected in the large number of methods, e.g., [9,16,17,19], that are based on K-means clustering or variations thereof. However, its performance depends heavily on the number of initial cluster centers and on the selection of the initial cluster centers.…”

Section: Introductionmentioning

confidence: 99%

Automated Localization of Brain Tumors in MRI Using Potential-K-Means Clustering Algorithm

Cabria

Gondra

2015

2015 12th Conference on Computer and Robot Vision

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The manual localization and precise segmentation of brain tumors from magnetic resonance images (MRI) is time-consuming and error-prone. In T2 and FLAIR MRI, tumors appear as bright areas of higher signal intensity than their surroundings. In this paper we view the intensity of a pixel as equal to its "workload" and employ an unsupervised learning algorithm called potential-K-means that generates a balanced distribution of the pixels into clusters of approximately equal total intensity. The algorithm is based on an analogy with the gravitational force exerted by masses. This balancing requirement introduces a search bias that tends to generate either small clusters of higher intensity pixels, which overlap with the tumor area, or large clusters of lower intensity pixels. We evaluate the proposed algorithm on the publicly available brain tumor image segmentation (BRATS) MRI benchmark by comparing the center of the cluster that overlaps with the tumor, with the center of the tumor in the corresponding ground truth segmentation. We compare the proposed algorithm with the well-known K-means and with the Force clustering algorithm by Kalantari et al. (2009), which follows a different Physics analogy, but it is also based on a balancing criteria. Experimental results show that K-means is not suitable for tumor localization and that potential-Kmeans and Kalantari's approach are comparable. However, the performance of Kalantari's approach is highly dependent on a parameter whose value needs to be set a priori, but without an informed way of doing so, which makes the present proposed method more practical.

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“…The previously proposed clustering 15 and fuzzy clustering methods 16,17 for MRI tumor segmentation, attempt a direct tumor, non-tumor classification. In this paper we perform clustering to parsimoniously describe the salient information in the extracted MRI features.…”

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confidence: 99%

3D variational brain tumor segmentation on a clustered feature set

et al. 2009

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Tumor segmentation from MRI data is a particularly challenging and time consuming task. Tumors have a large diversity in shape and appearance with intensities overlapping the normal brain tissues. In addition, an expanding tumor can also deflect and deform nearby tissue. Our work addresses these last two difficult problems. We exploit the various MRI modalities and their texture characteristics to construct a multi-dimensional feature set. Further, we extract clusters which provide a compact representation of the essential information in these features. The main idea in this paper is to incorporate these clustered features into the 3D variational segmentation framework. In contrast to the previous variational approaches, we propose a segmentation method that evolves the contour in a supervised fashion. The segmentation boundary is driven by the learned inside and outside region voxel probabilities in the cluster space. We incorporate prior knowledge about the normal brain tissue appearance, during the estimation of these region statistics. In particular, we use a Dirichlet prior that discourages the clusters in the ventricles to be in the tumor and hence better disambiguate the tumor from brain tissue. We show the performance of our method on real MRI scans. The experimental dataset, includes difficult instances with tumors that are inhomogeneous in appearance, small in size and in proximity to the major structures in the brain. Our method shows good results on these test cases.

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Extraction of tumours from MR images of the brain by texture and clustering

Cited by 6 publications

References 7 publications

Nonlinear Feature Extraction Methods Based on Dual-Tree Complex Wavelet Transform Subimages of Brain Magnetic Resonance Imaging for the Classification of Multiple Diseases

Nonlinear Feature Extraction Methods Based on Dual-Tree Complex Wavelet Transform Subimages of Brain Magnetic Resonance Imaging for the Classification of Multiple Diseases

Automated Localization of Brain Tumors in MRI Using Potential-K-Means Clustering Algorithm

3D variational brain tumor segmentation on a clustered feature set

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