A Neural Network based Method for Brain Abnormality Detection in MR Images Using Gabor Wavelets

Lashkari, AmirEhsan

doi:10.5120/841-1140

Cited by 22 publications

(13 citation statements)

References 30 publications

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“…The ensemble method performs similar (0.0006 ± 0.01) to the best individual result for 21 cases (no. 1,2,3,5,6,7,15,16,17,19,21,27,29,30,31,32,34,35,39,42,46). Two main observations may contribute to this result.…”

Section: Resultsmentioning

confidence: 87%

“…Recently, other features other than intensity were studied, including grayscale concurrence matrix (GLCM) features, 30 discrete cosine transform (DCT) features, 31 and the Gabor wavelet filter. 32 In summary, most of the literature reports the use of multichannel MR to segment GBM tumors, while segmentation on a single-channel MR has only been reported infrequently. 8 Although multichannel MR sequences are useful in differentiating brain tissues and disease, they are usually acquired at low resolutions, with slice gaps, and images from different sequences are often not aligned.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble segmentation for GBM brain tumors on MR images using confidence‐based averaging

et al. 2013

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Section: Resultsmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Ensemble segmentation for GBM brain tumors on MR images using confidence‐based averaging

et al. 2013

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“…Cuadra () produced 37.19 as the mean MSE value. 0.025 is the average MSE value obtained by Lashkar () through segmenting the input MR brain images. Lower MSE value indicates minimum deformation in the segmentation of brain tissues and tumor region.…”

Section: Resultsmentioning

confidence: 99%

A complete automated algorithm for segmentation of tissues and identification of tumor region in T1, T2, and FLAIR brain images using optimization and clustering techniques

Govindaraj

Murugan

2014

Int J Imaging Syst Tech

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Tissues in brain are the most complicated parts of our body, a clear examination and study are therefore required by a radiologist to identify the pathologies. Normal magnetic resonance (MR) scanner is capable of producing brain images with bounded tissues, where unique and segregated views of the tissues are required. A distinguished view upon the images is manually impossible and can be subjected to operator errors. With the assistance of a soft computing technique, an automated unique segmentation upon the brain tissues along with the identification of the tumor region can be effectively done. These functionalities assist the radiologist extensively. Several soft computing techniques have been proposed and one such technique which is being proposed is PSO‐based FCM algorithm. The results of the proposed algorithm is compared with fuzzy C‐means (FCM) and particle swarm optimization (PSO) algorithms using comparison factors such as mean square error (MSE), peak signal to noise ratio (PSNR), entropy (energy function), Jaccard (Tanimoto Coefficient) index, dice overlap index and memory requirement for processing the algorithm. The efficiency of the PSO‐FCM algorithm is verified using the comparison factors.

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“…The authors in [20] introduced a novel approach for finding any abnormality in different brain parts. The proposed approach was applied to different types of imaging modalities including T2 weighted images, T1 weighted images, PD images and MRI images.…”

Section: Figure 1: Brain Mri Classification Methodologymentioning

confidence: 99%

Using Probabilistic Classification Technique and Statistical Features for Brain Magnetic Resonance Imaging (MRI) Classification: An Application of AI Technique in Bio-Science

Wahid

Ghazali

Fayaz

et al. 2017

IJBSBT

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There are many medical imaging modalities used for the analysis and cure of various diseases. One of the most important of these modalities is Magnetic Resonance Imaging (MRI). MRI is advantageous over other modalities due to its high spatial resolution and the excellent capability of discrimination of soft tissues. In this paper, an automated classification approach of normal and pathological MRI is proposed. The proposed model three simple stages; preprocessing, feature extraction and classification. Two types of features; color moments and texture features have been considered as main features for the description of brain MRI. A probabilistic classifier based on logistic function has been used for the MRI classification. A standard data set consisting of one hundred and fifty images has been used in the experiments, which was divided into 66% training and 34% testing. The proposed approach gave 98% accurate results for training data set and 94% accurate results for the testing data set. For validation of the proposed approach, 10-Fold cross validation was applied, which gave 90.66% accurate results. The classification capability of probabilistic classifier has been compared with the different state of art classifiers, including Support Vector Machine (SVM), Naïve Bayes, Artificial Neural Network (ANN), and Normal densities based linear classifier.

show abstract

A Neural Network based Method for Brain Abnormality Detection in MR Images Using Gabor Wavelets

Cited by 22 publications

References 30 publications

Ensemble segmentation for GBM brain tumors on MR images using confidence‐based averaging

Ensemble segmentation for GBM brain tumors on MR images using confidence‐based averaging

A complete automated algorithm for segmentation of tissues and identification of tumor region in T1, T2, and FLAIR brain images using optimization and clustering techniques

Using Probabilistic Classification Technique and Statistical Features for Brain Magnetic Resonance Imaging (MRI) Classification: An Application of AI Technique in Bio-Science

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