Brain tumor detection using self-adaptive K-means clustering

Kaur, Navpreet; Sharma, Manvinder

doi:10.1109/icecds.2017.8389771

Cited by 32 publications

(10 citation statements)

References 9 publications

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“…Clustering techniques, which are an unsupervised learning method, have been widely investigated in medical image segmentation. However, in this survey work some of the most popular clustering methods, such as k-means and its varieties [ 38 , 39 , 40 , 41 , 42 , 43 , 44 ], fuzzy c-means [ 38 , 39 , 41 , 45 ], subtractive clustering (SC), and hybrid techniques [ 46 , 47 , 48 ].…”

Section: Brain Tumor Segmentation Methodsmentioning

confidence: 99%

“…The minimal computational requirement [ 37 , 48 ], simplicity to implement on large dataset [ 49 ], adaptation to new examples, and guaranteed convergence are some of the advantages that makes K-means popular segmentation algorithm. However, k-means suffers with incomplete delineation of the tumor region [ 49 ], selection of the initial centroid is not optimum [ 37 , 43 ], and it is sensitive to outliers [ 48 , 50 ]. Due to these limitations a number of solutions have been proposed, including, evenly spreading the initial cluster centers (k-means++), hybridizing k-means with other clustering techniques [ 49 ], adaptively initializing cluster centers, such as adaptive k-means [ 43 ], modified adaptive k-means (MAKM), and histogram based k-means.…”

Section: Brain Tumor Segmentation Methodsmentioning

confidence: 99%

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A Survey of Brain Tumor Segmentation and Classification Algorithms

Biratu

Schwenker

Ayano³

et al. 2021

J. Imaging

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A brain Magnetic resonance imaging (MRI) scan of a single individual consists of several slices across the 3D anatomical view. Therefore, manual segmentation of brain tumors from magnetic resonance (MR) images is a challenging and time-consuming task. In addition, an automated brain tumor classification from an MRI scan is non-invasive so that it avoids biopsy and make the diagnosis process safer. Since the beginning of this millennia and late nineties, the effort of the research community to come-up with automatic brain tumor segmentation and classification method has been tremendous. As a result, there are ample literature on the area focusing on segmentation using region growing, traditional machine learning and deep learning methods. Similarly, a number of tasks have been performed in the area of brain tumor classification into their respective histological type, and an impressive performance results have been obtained. Considering state of-the-art methods and their performance, the purpose of this paper is to provide a comprehensive survey of three, recently proposed, major brain tumor segmentation and classification model techniques, namely, region growing, shallow machine learning and deep learning. The established works included in this survey also covers technical aspects such as the strengths and weaknesses of different approaches, pre- and post-processing techniques, feature extraction, datasets, and models’ performance evaluation metrics.

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Section: Brain Tumor Segmentation Methodsmentioning

confidence: 99%

Section: Brain Tumor Segmentation Methodsmentioning

confidence: 99%

A Survey of Brain Tumor Segmentation and Classification Algorithms

Biratu

Schwenker

Ayano³

et al. 2021

J. Imaging

View full text Add to dashboard Cite

show abstract

“…In particular, more advanced architectures like convolutional neural networks (NN) [11], probabilistic NN [12,13], deep NN [14] and U-Nets [15] have been successfully applied to the task of image segmentation and brain tumor classification. On the other hand, many unsupervised methods exist as well; some are based on clustering techniques, [16][17][18][19][20][21]; others are based on automatic thresholding methods and morphological transformations, [22][23][24][25][26]. Some techniques apply a suitable projection in lower dimensional spaces in order to get rid of unwanted redundancies, and so classical matrix decompositions like the SVD or the non-negative matrix factorization are applied, see e.g., [27].…”

Section: Introductionmentioning

confidence: 99%

Z2-$\gamma$: an Application of Zienkiewicz-Zhu Error Estimator to Brain Tumor Detection in MR Images

Falini¹

2022

Preprint

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Brain tumors are abnormal cells growth in the brain tissues that can be cancerous, or not. In any case, they could be a very aggressive disease that should be detected as early as possible. Usually, magnetic resonance imaging (MRI) is the main tool commonly adopted by neurologists and radiologists to identify and classify any possible anomalies present in the brain anatomy. In the present work, an automatic unsupervised method called Z2-$\gamma$, based on the use of adaptive finite-elements and suitable pre-processing and post-processing techniques, is introduced. In particular, the proposed methodology is able to automatically classify whether a given MR image represents a healthy or a diseased brain and in this latter case, is able to locate the tumor area.

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“…In this technique, input MRI images are converted into 2D images and segmentation is done using thresholding technique. Mind Tumor Location Utilizing Self Versatile K-Means Grouping is developed by Navpreet Kaur [5].…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Detection Using Gray Level Co-Occurrence Matrix Feature Extraction Technique

SheelaS¹,

Prema²,

Ramya³

et al. 2021

Advances in Parallel Computing

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From each and every passing year, the world has always witnessed a rise in the number of cases of brain tumor. Brain tumor classification and detection is that the most critical and strenuous task within the field of medical image processing while human aided manual detection leads to imperfect divination and diagnosing. Brain tumors have high heterogeneity in appearance and there is a same feature between tumor and non-tumor tissues and thus the extraction of tumor regions from MRI scan images becomes unyielding. A Gray Level Co-occurrence Matrix(GLCM) is applied on MRI scan images to detect tumor and non-tumor regions in brain. The main aim of medical imaging is to extract meaningful information accurately from the images. The method of detecting brain tumor from an MRI scan images are often classified into four categories: Pre-Processing, Skull Stripping, Segmentation and have Feature Extraction.

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Brain tumor detection using self-adaptive K-means clustering

Cited by 32 publications

References 9 publications

A Survey of Brain Tumor Segmentation and Classification Algorithms

A Survey of Brain Tumor Segmentation and Classification Algorithms

Z2-$\gamma$: an Application of Zienkiewicz-Zhu Error Estimator to Brain Tumor Detection in MR Images

Brain Tumor Detection Using Gray Level Co-Occurrence Matrix Feature Extraction Technique

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