Tumour detection in brain MRI using improved segmentation algorithm

Kumar, E. Praveen; Kumar, V. Manoj; Sumithra, M. G.

doi:10.1109/icccnt.2013.6726575

Cited by 6 publications

(5 citation statements)

References 4 publications

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“…In addition, in a study [ 98 ] on a brain tumor detection method, the method involved the combination of steps such as preprocessing, segmentation through neural network, and image analysis that was performed though Gabor filter and Bayesian neural network classifier. In addition to the aforementioned studies, another study [ 2 ] revealed that the MRI was the widely preferred and used method of medical imaging. In this study, the statistical evaluation consists of the mean, median, mode, variance, and standard deviation.…”

Section: Segmentationmentioning

confidence: 99%

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Review on Hybrid Segmentation Methods for Identification of Brain Tumor in MRI

Ejaz

Rahim

Arif

et al. 2022

Contrast Media & Molecular Imaging

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Modalities like MRI give information about organs and highlight diseases. Organ information is visualized in intensities. The segmentation method plays an important role in the identification of the region of interest (ROI). The ROI can be segmented from the image using clustering, features, and region extraction. Segmentation can be performed in steps; firstly, the region is extracted from the image. Secondly, feature extraction performed, and better features are selected. They can be shape, texture, or intensity. Thirdly, clustering segments the shape of tumor, tumor has specified shape, and shape is detected by feature. Clustering consists of FCM, K-means, FKM, and their hybrid. To support the segmentation, we conducted three studies (region extraction, feature, and clustering) which are discussed in the first line of this review paper. All these studies are targeting MRI as a modality. MRI visualization proved to be more accurate for the identification of diseases compared with other modalities. Information of the modality is compromised due to low pass image. In MRI Images, the tumor intensities are variable in tumor areas as well as in tumor boundaries.

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Section: Segmentationmentioning

confidence: 99%

“…Hence, segmentation techniques are helpful for the identification of the region of interest (ROI). The studies [ 2 , 3 ] revealed that MRI faced numerous challenges for the identification of diseases.…”

Section: Introductionmentioning

confidence: 99%

Review on Hybrid Segmentation Methods for Identification of Brain Tumor in MRI

Ejaz

Rahim

Arif

et al. 2022

Contrast Media & Molecular Imaging

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“…MRI imaging is compromising due to noise, complexity of detection of brain tumor disease [21]. Patient declares normal or abnormal with analysis of MR imaging.…”

Section: Related Workmentioning

confidence: 99%

“…The segmentation of brain tumor has been performed in continuous regions as well as in discontinuous regions. Brain tumor is as a feature and has been detected in MRI when applies quad tree for detection of region of interest (ROI) in continuous region [21]. Quad tree active contour level set are compromising in discontinuous regions.…”

Section: Various Novel System For Detection Of Brain Tumor Has Usedmentioning

confidence: 99%

Segmentation Method for Pathological Brain Tumor and Accurate Detection using MRI

Ejaz¹,

Rahim²,

Rehman³

et al. 2018

ijacsa

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Image segmentation is challenging task in field of medical image processing. Magnetic resonance imaging is helpful to doctor for detection of human brain tumor within three sources of images (axil, corneal, sagittal). MR images are nosier and detection of brain tumor location as feature is more complicated. Level set methods have been applied but due to human interaction they are affected so appropriate contour has been generated in discontinuous regions and pathological human brain tumor portion highlighted after applying binarization, removing unessential objects; therefore contour has been generated. Then to classify tumor for segmentation hybrid Fuzzy K Mean-Self Organization Mapping (FKM-SOM) for variation of intensities is used. For improved segmented accuracy, classification has been performed, mainly features are extracted using Discrete Wavelet Transformation (DWT) then reduced using Principal Component Analysis (PCA). Thirteen features from every image of dataset have been classified for accuracy using Support Vector Machine (SVM) kernel classification (RBF, linear, polygon) so results have been achieved using evaluation parameters like Fscore, Precision, accuracy, specificity and recall.

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“…At the end of the procedure the tumor is removed from the MR picture and its precise position and the design also determined. The stage of the tumor is exhibited on the basis of the level of region determined from the cluster.Kumar et al [14] examine about an improved segmentation algorithm for various defects detection in brain MRI (Tumor) and have compared the efficiency of this process with main-stream method. N. Albert Singh [15] presented an algorithm which features measures for preprocessing, function removal and classification using neural system techniques.…”

Section: Literature Surveymentioning

confidence: 99%

Comparison of F-Measure, BER and PSNR of Tumor Detection using Hybridization of Fuzzy and Region Growing

Arora¹,

Singh²,

Banga³

2015

IJCA

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This paper has dedicated to brain tumor detection algorithm. The majority of the existing work with tumor detection has neglected the using object-based segmentation. Thus this paper has planned an effective brain tumor detection using the feature detection and roundness metric. To boost the tumor detection rate further we've incorporated the proposed hybridization of fuzzy C-means and region growing segmentation based tumor detection with the use of trilateral filter in its preprocessing stage. The planned method has the capability to generate efficient results even in the event of large occurrence of the noise. The experimental results have obviously shown that the planned method outperforms over the available techniques.

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Tumour detection in brain MRI using improved segmentation algorithm

Cited by 6 publications

References 4 publications

Review on Hybrid Segmentation Methods for Identification of Brain Tumor in MRI

Review on Hybrid Segmentation Methods for Identification of Brain Tumor in MRI

Segmentation Method for Pathological Brain Tumor and Accurate Detection using MRI

Comparison of F-Measure, BER and PSNR of Tumor Detection using Hybridization of Fuzzy and Region Growing

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