Brain Tumor Detection Using Color-Based K-Means Clustering Segmentation

Wu, Ming-Ni; Lin, Chia-Chen; Chang, Chin‐Chen

doi:10.1109/iihmsp.2007.4457697

Cited by 121 publications

(59 citation statements)

References 4 publications

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“…Ming-Ni Wu [4] In this paper a color-based segmentation method which is based on using K-means clustering technique is proposed to detect tumor objects in magnetic resonance (MR) brain images. This work uses a color-based segmentation algorithm with K-means to convert a given gray-level MR image into a color space image.…”

Section: Related Workmentioning

confidence: 99%

Contrast Level Test-based Methodology for Speed-Up MRI Brain Tumor Detection and Localization Approach

Al-Azzawi¹

2016

IJIRCCE

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ABSTRACT:A tumor is a growth in the abnormal tissue which can be differentiated from the surrounding tissue by its structure.A tumor also may lead to cancer, which is a major leading cause of death and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in the world. Great knowledge and experience on radiology are required for accurate tumor detection in medical imaging. Automation of tumor detection is required because there might be a shortage of skilled radiologists at a time of great need. This paper reviews the processes and techniques used in detecting tumor based on medical imaging results such as mammograms, x-ray computed tomography (x-ray CT) and magnetic resonance imaging (MRI). MRI image which is the Magnetic Resonance Imaging is also could be used for diagnosisand discover which is the type of the human brain that has been tested is normal or abnormal. Moreover, the MRI images give an observation and useful information that will help the doctors and surgeries to avoid some mistakes that will happen during the testing and the diagnosis process. Also, MRI characteristics are used for avoiding human error in manual interpretation of medical content. One of the most popular and useful application that the medical system in need is the MRI brain image classification approach. In this paper, we proposed a new clustering algorithm which relies on the differences between the contrast level of the tumor in the MRI. We depend on new approach for image clustering which is based on the difference between the construct (which is the intensity level) between the tumor region and the whole MRI image. In contrast, other algorithms like k-means, Fuzzy c-means, or probabilistic c-means depend on typical approach which is based on the distance majority between each point (pixel) and its mean. Our work consists some preprocessing steps like smoothed and enhanced the MRI by using enhancement techniques such as Gaussian kernel, median filter, high-pass filter, and Morphological image operation. Mainly, the proposed clustering algorithm in this work uses for segmentation of the image to detect the suspicious region of the tumor in brain MRI image. In this paper feature our results have been compared with the traditional clustering algorithm such as k-means and watershed.

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Section: Related Workmentioning

confidence: 99%

Contrast Level Test-based Methodology for Speed-Up MRI Brain Tumor Detection and Localization Approach

Al-Azzawi¹

2016

IJIRCCE

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“…Binary morphology utilizes only set of membership and is unconcerned to the value, such as gray level or color of a pixel. Ming-Ni Wu, Chia-Chen Lin, and Chin-Chen Chang et al [8] has developed a color-based segmentation with the help of K-means clustering process to detect the tumor in MRI brain images. The main idea in this is color-based segmentation technique using Kmeans clustering is to convert a gray image into a color space image and then separate the position of tumor from MRI image by using K-means clustering and histogram-clustering technique.…”

Section: IImentioning

confidence: 99%

Automatic Detection of Brain Tumor Using K-Means Clustering

Singh¹

2017

IJRASET

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Brain tumor is an uncommon and uncontrolled growth of cell in brain. In medical image processing one of the most challenging tasks is study of Brain tumor. Recently, magnetic resonance imaging (MRI) is the most widely used imaging modality for identifying brain tumor and other discrepancies. From these MRI images, we are able to determine the detailed anatomical information to examine the development of the human brain and to diagnose the various diseases. The tumor detection becomes most complicated for the huge image database. So a software approach is required to help the accurate and faster clinical diagnosis. The proposed system identifies and segments the tumor portions of the image successfully using MATLAB. The aim is to introduce an algorithm employed for performing useful operations on the MRI images such as filtering, pre-processing, morphological operation, K-means clustering, feature Extraction, Decision making system, SVM Classifier & Naïve Bayes. This work helps to save the time of both pathologist and doctor. The experimental results show that the proposed method detects tumor areas efficiently in the image of the brain.

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“…[2,4] Let us consider an image m × n and the image has to be cluster into K number of cluster. Suppose a point f(x, y) which is an input pixels to be cluster and C k be the cluster center.…”

mentioning

confidence: 99%

Parametric Comparison of K-means and Adaptive K-means Clustering Performance on Different Images

Sahu¹,

Parvathi²,

Krishna³

2017

IJECE

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<p>Image segmentation takes a major role to analyzing the area of interest in image processing. Many researchers have used different types of techniques to analyzing the image. One of the widely used techniques is K-means clustering. In this paper we use two algorithms K-means and the advance of K-means is called as adaptive K-means clustering. Both the algorithms are using in different types of image and got a successful result. By comparing the Time period, PSNR and RMSE value from the result of both algorithms we prove that the Adaptive K-means clustering algorithm gives a best result as compard to K-means clustering in image segmentation. </p>

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Brain Tumor Detection Using Color-Based K-Means Clustering Segmentation

Cited by 121 publications

References 4 publications

Contrast Level Test-based Methodology for Speed-Up MRI Brain Tumor Detection and Localization Approach

Contrast Level Test-based Methodology for Speed-Up MRI Brain Tumor Detection and Localization Approach

Automatic Detection of Brain Tumor Using K-Means Clustering

Parametric Comparison of K-means and Adaptive K-means Clustering Performance on Different Images

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