Detection of brain tumor in medical images

Kharrat, Ahmed; Benamrane, Nacéra; Messaoud, Mohamed Ben; Abid, Mohamed

doi:10.1109/icscs.2009.5412577

Cited by 81 publications

(39 citation statements)

References 11 publications

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“…The simulation results showed improved performance over conventional systems [2]. An efficient brain tumor detection was proposed in [3], which consisted of three steps: enhancement, segmentation and classification. The enhancement process was applied to enhance the image quality before the segmentation phase.…”

Section: Introductionmentioning

confidence: 93%

Performance analysis of classifier for brain tumor detection and diagnosis

Shanthakumar

Ganeshkumar²

2015

Computers & Electrical Engineering

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Section: Introductionmentioning

confidence: 93%

Performance analysis of classifier for brain tumor detection and diagnosis

Shanthakumar

Ganeshkumar²

2015

Computers & Electrical Engineering

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“…Kharrat et al [7] used the K-means algorithm to extract the tumor considering high intensity of brain tumor. Shape of the extracted tumor is distorted in their result by using K-means.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Grayscale Brain Tumor in Magnetic Resonance Image

Sharma¹,

Meghrajani²

2014

International Journal of Advanced Research in Computer and Comm

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Magnetic resonance imaging (MRI) has great contribution in diagnosis and treatment of brain tumor. Segmentation of grayscale tumor or low intensity tumor is the most important and challenging task. In this paper, we propose a method to extract grayscale tumor from MRI image. Due to low intensity profile of input image, global contrast enhancement method is applied as a pre-processing step. This enhanced grayscale image is converted into binary image. Further, algorithm segments largest connected region using mathematical morphological and segmentation methods. At last, tumor region is extracted using resultant image and input MRI image. Experimental results show the proposed method effectively extracts a grayscale brain tumor from MRI images.

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“…Several other approaches have proposed automatic detection of brain tumor based on using segmentation with other filtering processes [13][14][15][16][17][18][19], which are summarized in Table 2.…”

Section: Related Workmentioning

confidence: 99%

Accuracy Evaluation of Brain Tumor Detection using Entropy-based Image Thresholding

Alyahya¹,

Abu-Shareha²

2018

IJITCS

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Abstract-In this paper, the accuracy of the entropybased thresholding approaches in brain tumor detection framework is investigated. Entropies are information gain methods that have been used for image thresholding with various application and different image modalities. The accuracy of the existing entropies for image thresholding has been studied in general domain (e.g.: natural images) and were not compared thoroughly. Thus, a framework for brain tumor segmentation is proposed with the core process of the image thresholding, in order to evaluate the accuracy of the entropies. Five entropies, namely, Renyi, Maximum, Minimum, Tsallis and Kapur are evaluated. Moreover, the aggregation of entropies was implemented and evaluated. The results show that the maximum entropy is the best for brain tumor detection. Moreover, it was shown that aggregation of entropies output does not enhance the result, however, it works as automatic selection of the best result and produces the results with the highest accuracy.

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Detection of brain tumor in medical images

Cited by 81 publications

References 11 publications

Performance analysis of classifier for brain tumor detection and diagnosis

Performance analysis of classifier for brain tumor detection and diagnosis

Extraction of Grayscale Brain Tumor in Magnetic Resonance Image

Accuracy Evaluation of Brain Tumor Detection using Entropy-based Image Thresholding

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