MRI Brain Image Segmentation based on Wavelet and FCM Algorithm

Khalifa, Iraky; Youssif, Aliaa A. A.; Youssry, Howida

doi:10.5120/7275-0446

Cited by 22 publications

(10 citation statements)

References 28 publications

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“…Khalifa et al [26] developed a system for segmenting MRI brain image based on wavelet and FCM (WFCM) algorithm. Their algorithm is robust and efficient approach to segmenting noisy medical images.…”

Section: Related Workmentioning

confidence: 99%

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Multi-resolution MRI Brain Image Segmentation Based on Morphological Pyramid and Fuzzy C-mean Clustering

et al. 2015

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Image segmentation is a vital step in many imaging applications, such as medical images and computer vision. Image segmentation is considered as a challenging problem, so we need to develop an efficient, fast technique for medical image segmentation. In this paper, we propose a new system for a multi-resolution MRI brain image segmentation, which is based on a morphological pyramid with fuzzy C-mean (FCM) clustering. In the first stage, we use a wavelet multi-resolution to maintain spatial context between pixels. Secondly, we use the morphological pyramid to fuse the resulting multi-resolution images with the original image to increase sharpness and decrease noise in the processed image. Finally, we use FCM technique to segment the processed images. We compared our proposed system with some state of the art segmentation techniques on two different brain data sets. Experimental results showed that the proposed system improves the accuracy of the MRI brain image segmentation.

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Section: Related Workmentioning

confidence: 99%

“…Soft segmentation methods, such as FCM, can keep much more information, but it cannot incorporate any information about spatial context. Therefore, we use discrete wavelet to solve this drawback of FCM [16,26].…”

Section: The Proposed Segmentation Systemmentioning

confidence: 99%

Multi-resolution MRI Brain Image Segmentation Based on Morphological Pyramid and Fuzzy C-mean Clustering

et al. 2015

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“…for a brain tumor image. Iraky khalifa, et.al [6], worked with hybrid method for the segmentation of MRI brain tumor images and they analyze its quality. Manoj kumarV et.al [9], has been discussed various segmentation algorithms for an MRI images and analyze the performance of those algorithms.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Approach Based Segmentation Technique for Brain Tumor in MRI Images

Anithadevi¹,

Perumal²

2016

SIPIJ

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“…The image segmentation plays an introduction and indispensable step in medical image processing that has proposed by Iraqi Khalifa et al [3]. Magnetic resonance (MR) fragmentation used for brain tissues extraction white matter (WM), gray matter (GM) and cerebro-spinal fluids (CSF).These tissues assist with lots of medical image segmentation applications such as radiotherapy planning, clinical diagnosis, treatment planning and Alzheimer disease.…”

Section: Introductionmentioning

confidence: 99%

Early diagnosis of primary tumor in brain MRI images using wavelet as the input of Ada-Boost classifier

Ajikumar

Jayachandran

2014

2014 International Conference on Contemporary Computing and Informatics (IC3I)

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In this paper we have developed a new approach for automatic classification of brain tumor in enhanced MRI images. The proposed method consists of four stages namely Preprocessing, feature extraction, feature reduction and classification. In the first stage wiener filter is applied for noise reduction and to make the image suitable for extracting the features. In the second stage, the seeded region growing segmentation is used for partitioning the image into meaningful regions. In the third stage, Discrete wavelet transformation is used to extract the wavelet coefficients from the segmented image. In the next stage PCA is used to reduce the dimensionality of the wavelet coefficients which results in a more efficient and accurate classification .Finally, In the classification stage, Ada-Boost classifier is used to classify the experimental images into normal and abnormal cases .Our proposed method is evaluated using the metrics sensitivity, specificity and accuracy. It produces better results compared to Linear and non-linear SVM .

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MRI Brain Image Segmentation based on Wavelet and FCM Algorithm

Cited by 22 publications

References 28 publications

Multi-resolution MRI Brain Image Segmentation Based on Morphological Pyramid and Fuzzy C-mean Clustering

Multi-resolution MRI Brain Image Segmentation Based on Morphological Pyramid and Fuzzy C-mean Clustering

A Hybrid Approach Based Segmentation Technique for Brain Tumor in MRI Images

Early diagnosis of primary tumor in brain MRI images using wavelet as the input of Ada-Boost classifier

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