Automatic Human Brain Tumor Detection in MRI Image Using Template-Based K Means and Improved Fuzzy C Means Clustering Algorithm

Alam, Shahariar; Rahman, M.; Hossain, Mohammad Amzad; Islam, Khairul; Ahmed, K.M.; Ahmed, Khandaker Takdir; Singh, Bikash Chandra; Miah, Md Sipon

doi:10.3390/bdcc3020027

Cited by 122 publications

(49 citation statements)

References 26 publications

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“…Alam et al (2019) proposed Fuzzy C means clustering algorithm for detecting and segmenting tumor. A new technique was proposed to extract the tumor tissues automatically in the MR brain images.…”

Section: Literature Surveymentioning

confidence: 99%

RETRACTED: An efficient approach for brain tumor detection and segmentation in MR brain images using random forest classifier

Thayumanavan¹,

Ramasamy²

2021

Concurrent Engineering

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Nowadays, the most demanding and time consuming task in medical image processing is Brain tumor segmentation and detection. Magnetic Resonance Imaging (MRI) is employed for creating a picture of any part in a body. MRI provides a competent quick manner for analyzing tumor in the brain. This proposed framework contains different stages for classifying tumor like Preprocessing, Feature extraction, Classification, and Segmentation. Initially, T1-weighted magnetic resonance brain images are considered as an input for computational purpose. Median filter is proposed to optimize the skull stripping in MRI images. Abnormal brain tissues are extracted in low contrast, in addition to meticulous location of edges of affected tissue can be detected. Then, Discrete Wavelet Transform (DWT) and Histogram of Oriented Gradients (HOG) are performing feature extraction process. HOG is used for extracting the features like texture and shape. Then, Classification is performed through Machine learning categorization techniques via Random Forest Classifier (RFC), Support Vector Machine (SVM), and Decision Tree (DT). These classifiers classify the brain image as either normal or abnormal and the performance is analyzed by various parameters such as sensitivity, specificity and accuracy.

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Section: Literature Surveymentioning

confidence: 99%

RETRACTED: An efficient approach for brain tumor detection and segmentation in MR brain images using random forest classifier

Thayumanavan¹,

Ramasamy²

2021

Concurrent Engineering

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“…The K-Means algorithm is one such semi-automated approach that segments the MR Image based on the pre-determined number of segments as stated by Alam et al (2019) and Chahal, Pandey & Goel (2020) . Thus, the proposed approach is one of the simple techniques for the segmentation of the images.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed model’s performance is assessed against the various existing algorithms like thresholding, seeded region growing, fuzzy C-means, and the artificial neural network models concerning the evaluation metrics like sensitivity, specificity, and accuracy presented by Alam et al (2019) . The comparative analysis of the approaches with obtained values is shown in Table 4 .…”

Section: Introductionmentioning

confidence: 99%

Self-Learning Network-based segmentation for real-time brain M.R. images through HARIS

Srinivasu

Balas

2021

PeerJ Computer Science

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In recent years in medical imaging technology, the advancement for medical diagnosis, the initial assessment of the ailment, and the abnormality have become challenging for radiologists. Magnetic resonance imaging is one such predominant technology used extensively for the initial evaluation of ailments. The primary goal is to mechanizean approach that can accurately assess the damaged region of the human brain throughan automated segmentation process that requires minimal training and can learn by itself from the previous experimental outcomes. It is computationally more efficient than other supervised learning strategies such as CNN deep learning models. As a result, the process of investigation and statistical analysis of the abnormality would be made much more comfortable and convenient. The proposed approach’s performance seems to be much better compared to its counterparts, with an accuracy of 77% with minimal training of the model. Furthermore, the performance of the proposed training model is evaluated through various performance evaluation metrics like sensitivity, specificity, the Jaccard Similarity Index, and the Matthews correlation coefficient, where the proposed model is productive with minimal training.

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“…Fuzzy c-means abled to separate different tissue types using a small number of the cluster [93], but K-means uses many clusters for different tissue types [120]. Fuzzy c-means detect abnormalities more accurately than k-means by keeping more information from the original image [122]. Even though computer-aided detection/diagnosis (CAD) can significantly impact automating the image processing and analysis process, it will never replace doctors and radiologists.…”

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confidence: 99%

Brain stroke computed tomography images analysis using image processing: A Review

Ali

Abdullah

Saad

et al. 2021

IJ-AI

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<span lang="EN-US">Stroke is the second-leading cause of death globally; therefore, it needs immediate treatment to prevent the brain from damage. Neuroimaging technique for stroke detection such as computed tomography (CT) has been widely used for emergency setting that can provide precise information on an obvious difference between white and gray matter. CT is the comprehensively utilized medical imaging technology for bone, soft tissue, and blood vessels imaging. A fully automatic segmentation became a significant contribution to help neuroradiologists achieve fast and accurate interpretation based on the region of interest (ROI). This review paper aims to identify, critically appraise, and summarize the evidence of the relevant studies needed by researchers. Systematic literature review (SLR) is the most efficient way to obtain reliable and valid conclusions as well as to reduce mistakes. Throughout the entire review process, it has been observed that the segmentation techniques such as fuzzy C-mean, thresholding, region growing, k-means, and watershed segmentation techniques were regularly used by researchers to segment CT scan images. This review is also impactful in identifying the best automated segmentation technique to evaluate brain stroke and is expected to contribute new information in the area of stroke research.</span>

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Automatic Human Brain Tumor Detection in MRI Image Using Template-Based K Means and Improved Fuzzy C Means Clustering Algorithm

Cited by 122 publications

References 26 publications

RETRACTED: An efficient approach for brain tumor detection and segmentation in MR brain images using random forest classifier

RETRACTED: An efficient approach for brain tumor detection and segmentation in MR brain images using random forest classifier

Self-Learning Network-based segmentation for real-time brain M.R. images through HARIS

Brain stroke computed tomography images analysis using image processing: A Review

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