Analysis of MRI based brain tumor identification using segmentation technique

Bhima, K.; Jagan, A

doi:10.1109/iccsp.2016.7754551

Cited by 35 publications

(15 citation statements)

References 9 publications

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“…The watershed method [1][27] [2][3][10] [11] has the disadvantage that it is highly sensitive to local minima, since at each minima, a watershed is created. If an image with noise, this will influence the segmentation.…”

Section: Survey Discussionmentioning

confidence: 99%

“…Fuzzy C-means (FCM) [ [11]. Both method provides better results, but due to training and testing phase neural network will comes up with some potential overheads i.e.…”

Section: Survey Discussionmentioning

confidence: 99%

“…This method uses atlas based marker detection and achieved the accuracy of 93.13 and 88.64 of Dice and Tanimoto coefficient values respectively. K. Bhima and A. Jagan [11] worked on "Analysis of MRI based Brain Tumor Identification using Segmentation Technique" In which analysis has been performed on brain MRI to detect brain anomalous regions with marker based watershed segmentation algorithm and accomplished average accuracy is 97.34%.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Review on Performance Evaluation of Image Processing Technique for Brain Tumor Detection

2017

IJMTER

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Brain tumor is an uncontrolled growth of tissues in any part of the body. Tumors are of different types and characteristics and have different treatments. Detection of tumor in the earlier stages makes the treatment easier. Presently many medical imaging techniques such as positron emission tomography (PET), X-ray, computed tomography (CT), magnetic resonance imaging (MRI) for tumor detection are in use among which MRI imaging technique is more attractive because of its higher resolution and therefore most preferred technique for diagnosing tumor. MRI scan is safer than any other scan as it does not involve any radiations and gives high resolution of image. Paper gives a brief review of different segmentation and classification methods used for detection of tumor from Magnetic Resonance Images (MRI).

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Section: Survey Discussionmentioning

confidence: 99%

“…Fuzzy C-means (FCM) [ [11]. Both method provides better results, but due to training and testing phase neural network will comes up with some potential overheads i.e.…”

Section: Survey Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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A Review on Performance Evaluation of Image Processing Technique for Brain Tumor Detection

2017

IJMTER

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“…This process reduces the noise and reconstructs the medical image to improve the image features for additional processing. The pre processing operations [16] are required before the data analysis and extraction of information from the image. The Low pass filter is applied on the medical image to improve the image value by decreasing the background noise and conserve the edge points of the image [12].…”

Section: Pre Processingmentioning

confidence: 99%

“…A particular MRI image is assigned to a class based on image classification process. C* = arg max c p(cd) (15) The fundamental model can be evolved on the basis of the bayes theorem using the conditional probability as   (16) The choice of p(d) is purely based on C*. To calculate the expression p(d |c), it is break down by the Naïve base as assuming the fi's are conditionally independent given d's class as in the subsequent equations.…”

Section: A Naïve Bayes Classificationmentioning

confidence: 99%

Identification And Classification Of Brain Tumor Images Using Efficient Classifier

kumar*¹

2019

IJEAT

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The main objective of this study is to propose a model for finding brain tumor. Failing to detect the tumor in its prior stage will increases the chance of losing a life. So the identification and treatment for the tumor in its prior stages become vital to save the life of a human being. This work uses the Magnetic Resonance Image (MRI) images to identify and classify the benign and malign type brain tumors. Low pass filter is applied to preprocess the MRI image that removes the unwanted background structures at the same time keeps the important portions sharpened. Watershed segmentation method is used for segmenting the tumor affected area independently. The statistical feature extraction method Gray Level Co-occurrence Matrix (GLCM) is applied to take out the imperative features from the segmented tumor. The feature selection is performed using Recursive Feature Elimination- Particle Swarm Optimization (RFE-PSO) method. Ensemble Support Vector Machine (SVM) is applied to classify the tumors into harmless and harmful from the medical image.

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