Brain tumor detection based on segmentation using MATLAB

Hazra, Animesh; Dey, Ankit; Gupta, Sujit Kumar; Ansari, Md. Abid

doi:10.1109/icecds.2017.8390202

Cited by 34 publications

(11 citation statements)

References 3 publications

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“…From Table 5, the proposed tumor segmentation system achieves 97.9% of sensitivity, 98.6% of specificity, 99.1% of accuracy, 0.09% of error rate, 96.8% of F1‐score, 97.9% of ROC index and 98.5% of Geometric Mean. Table 6 shows the comparisons of the proposed method with other state of the art methods 17,4,18,22 . From Table 5, the results provided in this article is well compared with other state of the art methods.…”

Section: Resultsmentioning

confidence: 75%

“…The authors have detected tumor regions with a sensitivity of 92.9%, 96.5% of specificity and 96.1% region of tumor pixels accuracy index. Hazra et al 18 has used the Support Vector Machine (SVM) classification algorithm to classify brain images as either normal or abnormal. This SVM algorithm has been operated in two different methods as linear and nonlinear models.…”

Section: Introductionmentioning

confidence: 99%

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A method for detecting and classifying the tumor regions in brain MRI images using vector index filtering and ANFIS classification process

Surampudi¹

2021

Int J Imaging Syst Tech

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In this paper, the tumor affected images are detected and classified from non‐tumor affected brain magnetic resonance imaging (MRI) using Adaptive Neuro Fuzzy Inference System (ANFIS) classification process. The proposed work for brain tumor detection consists of a noise reduction module, decomposition module, feature extraction module, classification module, and segmentation module. The noisy brain images are filtered by the proposed Vector Index Filtering algorithm and the filtered images are further decomposed using Fast Wavelet Transform. Later, the index features are computed from each decomposed sub‐band image and these computed index features are classified either as normal or abnormal brain images using the ANFIS classification process. Further, the tumor regions are segmented in classified abnormal brain images using morphological functions. This paper uses the Brain Tumor Image Segmentation Challenge—BRATS 2016 dataset for analyzing the performance of the proposed brain tumor detection and segmentation system in terms of sensitivity (97.9%), specificity (98.6%), accuracy (99.1%), error rate (0.09%), F1‐score (96.8%), and Geometrics mean (98.5%). Comparisons are made between the proposed method's experimental results and other state of the art methods. The proposed methods using machine learning approach, stated in this paper are well suited for detecting tumor regions in brain images.

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Section: Resultsmentioning

confidence: 75%

Section: Introductionmentioning

confidence: 99%

A method for detecting and classifying the tumor regions in brain MRI images using vector index filtering and ANFIS classification process

Surampudi¹

2021

Int J Imaging Syst Tech

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“…Hanuman et al developed a technique for brain tumor segmentation, which includes anisotropic di usion, k-means clustering, morphological operations, temporal smoothing, and volumetric measurement [16]. A brain tumor detection technique proposed by Hazra et al is comprised of three stages: noise removal, edge detection, and k-means clustering [17]. Kharrat et al proposed an ecient technique for the detection of brain tumors that includes morphological operations to enhance the image contrast followed by wavelet transformation for segmentation and k-means clustering for extracting the tumor [18].…”

Section: Related Workmentioning

confidence: 99%

Detection and Localization of Early-Stage Multiple Brain Tumors Using a Hybrid Technique of Patch-Based Processing, k-means Clustering and Object Counting

Nasor

Obaid

2020

International Journal of Biomedical Imaging

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Brain tumors are a major health problem that a ect the lives of many people. ese tumors are classi ed as benign or cancerous. e latter can be fatal if not properly diagnosed and treated. erefore, the diagnosis of brain tumors at the early stages of their development can signi cantly improve the chances of patient's full recovery a er treatment. In addition to laboratory analyses, clinicians and surgeons extract information from medical images, recorded by various systems such as magnetic resonance imaging (MRI), X-ray, and computed tomography (CT). e extracted information is used to identify the essential characteristics of brain tumors (location, size, and type) in order to achieve an accurate diagnosis to determine the most appropriate treatment protocol. In this paper, we present an automated machine vision technique for the detection and localization of brain tumors in MRI images at their very early stages using a combination of k-means clustering, patch-based image processing, object counting, and tumor evaluation. e technique was tested on twenty real MRI images and was found to be capable of detecting multiple tumors in MRI images regardless of their intensity level variations, size, and location including those with very small sizes. In addition to its use for diagnosis, the technique can be integrated into automated treatment instruments and robotic surgery systems.

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“…This system used patient-specific training and compared classification of normal and abnormal using SVM classifier. It used the standard 2-class method and the more recent 1-class method [8]. The SVM method has the advantage of generalization and working in high dimensional feature space.…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptive Neuro Fuzzy Inference System Used to classify the Five Major Types of Brain Tumors

Bandyopadhyay¹,

Dutta²

2020

Preprint

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Life threatening diseases in both male and female are Brain tumor, stroke, hemorrhage and multiple sclerosis (MS). The most common and widespread disease among these brain diseases is Brain tumor. Early and accurate diagnosis of brain lesion is vital for determining accurate treatment and prognosis. However, the diagnosis is a very challenging task and can only be performed by specialists in neuroradiology. In this paper, initially MRI image is taken as input and is normalized. The second stage includes extraction of feature vectors from the image which results in reducing redundancy of data to serve as the input to the classifier. The classifier extracted vector as features to produce classified output. The methodology performed very efficiently and accurately. Proposed work exhibits the application of Fuzzy Inference System (FIS) based classifier known as Adaptive Neuro Fuzzy Inference System (ANFIS) to successfully classify the five major types of brain tumors.

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Brain tumor detection based on segmentation using MATLAB

Cited by 34 publications

References 3 publications

A method for detecting and classifying the tumor regions in brain MRI images using vector index filtering and ANFIS classification process

A method for detecting and classifying the tumor regions in brain MRI images using vector index filtering and ANFIS classification process

Detection and Localization of Early-Stage Multiple Brain Tumors Using a Hybrid Technique of Patch-Based Processing, k-means Clustering and Object Counting

Adaptive Neuro Fuzzy Inference System Used to classify the Five Major Types of Brain Tumors

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