White Patch Detection in Brain MRI Image Using Evolutionary Clustering Algorithm

2016 ◽  
pp. 323-339
Author(s):  
Pradeep Kumar Mallick ◽  
Mihir Narayan Mohanty ◽  
S. Saravana Kumar
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Brain Mri ◽  
Medical Diagnostics ◽  
Vital Role ◽  
Evolutionary Clustering ◽  
Edge Detectors ◽  
Ct Brain ◽  
Edge Based ◽  
The Brain

Though image segmentation is a fundamental task in image analysis; it plays a vital role in the area of image processing. Its value increases in case of medical diagnostics through medical images like X-ray, PET, CT and MRI. In this paper, an attempt is taken to analyse a CT brain image. It has been segmented for a particular patch in the brain CT image that may be one of the tumours in the brain. The purpose of segmentation is to partition an image into meaningful regions with respect to a particular application. Image segmentation is a method of separating the image from the background, read the contents and isolating it.In this paper both the concept of clustering and thresholding technique with edge based segmentation methods like sobel, prewitt edge detectors is applied. Then the result is optimized using GA for efficient minimization of the objective function and for improved classification of clusters. Further the segmented result is passed through a Gaussian filter to obtain a smoothed image.

scholarly journals A Novel Brain MRI Image Segmentation Method Using an Improved Multi-View Fuzzy c-Means Clustering Algorithm

2021 ◽  
Vol 15 ◽  
Author(s):  
Lei Hua ◽  
Yi Gu ◽  
Xiaoqing Gu ◽  
Jing Xue ◽  
Tongguang Ni
Keyword(s):  
Image Segmentation ◽  
Brain Tissue ◽  
Adaptive Learning ◽  
Clustering Algorithm ◽  
Brain Mri ◽  
Segmentation Method ◽  
Learning Mechanism ◽  
Fcm Algorithm ◽  
The Brain ◽  
Mri Image

Background: The brain magnetic resonance imaging (MRI) image segmentation method mainly refers to the division of brain tissue, which can be divided into tissue parts such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The segmentation results can provide a basis for medical image registration, 3D reconstruction, and visualization. Generally, MRI images have defects such as partial volume effects, uneven grayscale, and noise. Therefore, in practical applications, the segmentation of brain MRI images has difficulty obtaining high accuracy.Materials and Methods: The fuzzy clustering algorithm establishes the expression of the uncertainty of the sample category and can describe the ambiguity brought by the partial volume effect to the brain MRI image, so it is very suitable for brain MRI image segmentation (B-MRI-IS). The classic fuzzy c-means (FCM) algorithm is extremely sensitive to noise and offset fields. If the algorithm is used directly to segment the brain MRI image, the ideal segmentation result cannot be obtained. Accordingly, considering the defects of MRI medical images, this study uses an improved multiview FCM clustering algorithm (IMV-FCM) to improve the algorithm’s segmentation accuracy of brain images. IMV-FCM uses a view weight adaptive learning mechanism so that each view obtains the optimal weight according to its cluster contribution. The final division result is obtained through the view ensemble method. Under the view weight adaptive learning mechanism, the coordination between various views is more flexible, and each view can be adaptively learned to achieve better clustering effects.Results: The segmentation results of a large number of brain MRI images show that IMV-FCM has better segmentation performance and can accurately segment brain tissue. Compared with several related clustering algorithms, the IMV-FCM algorithm has better adaptability and better clustering performance.

Image Segmentation Algorithm Combined with Regularized P-M De-Noising Model and Improved Watershed Algorithm

2020 ◽  
Vol 10 (2) ◽  
pp. 515-521 ◽  
Author(s):  
Guorui Chen
Keyword(s):  
Image Segmentation ◽  
Brain Mri ◽  
Small Region ◽  
Watershed Algorithm ◽  
Segmentation Method ◽  
Segmentation Algorithms ◽  
Segmentation Image ◽  
The Brain ◽  
Image Segmentation Algorithm ◽  
Mri Image

Aiming at the problems of noise sensitivity and unclear contour in existing MRI image segmentation algorithms, a segmentation method combining regularized P-M de-noising model and improved watershed algorithm is proposed. First, the brain MRI image is pre-processed to obtain a brain nuclear image. Then, the brain nuclear image is de-noised by a regularized P-M model. After that, the image is preliminarily segmented by the traditional watershed algorithm to extract the features of each small region. Finally, the small regions are merged by Fuzzy Clustering with Spatial Pattern (FCSP) to obtain the segmentation image with smooth edges. The experimental results show that the algorithm can accurately segment the gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) regions. The average AOM and ME of the segmentation results on the BrainWeb dataset reached 0.93 and 0.04, respectively.

scholarly journals Brain MRI Images Segmentation Based on U-Net Architecture

2021 ◽  
Vol 18 (1) ◽  
pp. 21-27
Author(s):  
Assalah Atiyah ◽  
Khawla Ali
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Performance Metrics ◽  
Brain Mri ◽  
Regular Function ◽  
Survival Rates ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Edge Based ◽  
The Brain

Brain tumors are collections of abnormal tissues within the brain. The regular function of the brain may be affected as it grows within the region of the skull. Brain tumors are critical for improving treatment options and patient survival rates to prevent and treat them. The diagnosis of cancer utilizing manual approaches for numerous magnetic resonance imaging (MRI) images is the most complex and time-consuming task. Brain tumor segmentation must be carried out automatically. A proposed strategy for brain tumor segmentation is developed in this paper. For this purpose, images are segmented based on region-based and edge-based. Brain tumor segmentation 2020 (BraTS2020) dataset is utilized in this study. A comparative analysis of the segmentation of images using the edge-based and region-based approach with U-Net with ResNet50 encoder, architecture is performed. The edge-based segmentation model performed better in all performance metrics compared to the region-based segmentation model and the edge-based model achieved the dice loss score of 0. 008768, IoU score of 0. 7542, f1 score of 0. 9870, the accuracy of 0. 9935, the precision of 0. 9852, recall of 0. 9888, and specificity of 0. 9951.

Review on 2D and 3D MRI Image Segmentation Techniques

2019 ◽  
Vol 15 (2) ◽  
pp. 150-160 ◽  
Author(s):  
S. Shirly ◽  
K. Ramesh
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Medical Image ◽  
Medical Diagnostics ◽  
Processing Technique ◽  
Image Features ◽  
Medical Image Segmentation ◽  
Image Processing Technique ◽  
Edge Based ◽  
Mri Image

Background: Magnetic Resonance Imaging is most widely used for early diagnosis of abnormalities in human organs. Due to the technical advancement in digital image processing, automatic computer aided medical image segmentation has been widely used in medical diagnostics. </P><P> Discussion: Image segmentation is an image processing technique which is used for extracting image features, searching and mining the medical image records for better and accurate medical diagnostics. Commonly used segmentation techniques are threshold based image segmentation, clustering based image segmentation, edge based image segmentation, region based image segmentation, atlas based image segmentation, and artificial neural network based image segmentation. Conclusion: This survey aims at providing an insight about different 2-Dimensional and 3- Dimensional MRI image segmentation techniques and to facilitate better understanding to the people who are new in this field. This comparative study summarizes the benefits and limitations of various segmentation techniques.

scholarly journals Combination of Color and Focus Segmentation for Medical Images with Low Depth-of-Field

2018 ◽  
Vol 4 (1) ◽  
pp. 345-349
Author(s):  
Tamara Wirth ◽  
Ady Naber ◽  
Werner Nahm
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Medical Images ◽  
Image Data ◽  
Image Understanding ◽  
Surgical Instruments ◽  
Second Step ◽  
Depth Of Field ◽  
Segmentation Methods ◽  
Edge Based

AbstractImage segmentation plays an increasingly important role in image processing. It allows for various applications including the analysis of an image for automatic image understanding and the integration of complementary data. During vascular surgeries, the blood flow in the vessels has to be checked constantly, which could be facilitated by a segmentation of the affected vessels. The segmentation of medical images is still done manually, which depends on the surgeon’s experience and is time-consuming. As a result, there is a growing need for automatic image segmentation methods. We propose an unsupervised method to detect the regions of no interest (RONI) in intraoperative images with low depth-of-field (DOF). The proposed method is divided into three steps. First, a color segmentation using a clustering algorithm is performed. In a second step, we assume that the regions of interest (ROI) are in focus whereas the RONI are unfocused. This allows us to segment the image using an edge-based focus measure. Finally, we combine the focused edges with the color RONI to determine the final segmentation result. When tested on different intraoperative images of aneurysm clipping surgeries, the algorithm is able to segment most of the RONI not belonging to the pulsating vessel of interest. Surgical instruments like the metallic clips can also be excluded. Although the image data for the validation of the proposed method is limited to one intraoperative video, a proof of concept is demonstrated.

scholarly journals MRI Segmentation of the Human Brain: Challenges, Methods, and Applications

2015 ◽  
Vol 2015 ◽  
pp. 1-23 ◽  
Author(s):  
Ivana Despotović ◽  
Bart Goossens ◽  
Wilfried Philips
Keyword(s):  
Image Segmentation ◽  
Medical Image Analysis ◽  
Brain Mri ◽  
Bias Field ◽  
Mri Segmentation ◽  
Anatomical Structures ◽  
Segmentation Methods ◽  
Brain Changes ◽  
The Brain ◽  
Segmentation Problem

Image segmentation is one of the most important tasks in medical image analysis and is often the first and the most critical step in many clinical applications. In brain MRI analysis, image segmentation is commonly used for measuring and visualizing the brain’s anatomical structures, for analyzing brain changes, for delineating pathological regions, and for surgical planning and image-guided interventions. In the last few decades, various segmentation techniques of different accuracy and degree of complexity have been developed and reported in the literature. In this paper we review the most popular methods commonly used for brain MRI segmentation. We highlight differences between them and discuss their capabilities, advantages, and limitations. To address the complexity and challenges of the brain MRI segmentation problem, we first introduce the basic concepts of image segmentation. Then, we explain different MRI preprocessing steps including image registration, bias field correction, and removal of nonbrain tissue. Finally, after reviewing different brain MRI segmentation methods, we discuss the validation problem in brain MRI segmentation.

Optimized Fuzzy Clustering Algorithms for Brain MRI Image Segmentation Based on Local Gaussian Probability and Anisotropic Weight Models

2018 ◽  
Vol 32 (09) ◽  
pp. 1857005 ◽  
Author(s):  
Ting Zhang
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clinical Medicine ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Segmentation Algorithm ◽  
Fuzzy C Means ◽  
Anisotropic Weight ◽  
Mri Image

Brain Magnetic Resonance Imaging (MRI) image segmentation is one of the critical technologies of clinical medicine, and is the basis of three-dimensional reconstruction and downstream analysis between normal tissues and diseased tissues. However, there are various limitations in brain MRI images, such as gray irregularities, noise, and low contrast, reducing the accuracy of the brain MRI images segmentation. In this paper, we propose two optimization solutions for the fuzzy clustering algorithm based on local Gaussian probability fuzzy C-means (LGP-FCM) model and anisotropic weight fuzzy C-means (AW-FCM) model and apply it in brain MRI image segmentation. An FCM clustering algorithm is proposed based on AW-FCM. By introducing the new neighborhood weight calculation method, each point has the weight of anisotropy, effectively overcomes the influence of noise on the image segmentation. In addition, the LGP model is introduced in the objective function of fuzzy clustering, and a fuzzy clustering segmentation algorithm based on LGP-FCM is proposed. A clustering segmentation algorithm of adaptive scale fuzzy LGP model is proposed. The neighborhood scale corresponding to each pixel in the image is automatically estimated, which improves the robustness of the model and achieves the purpose of precise segmentation. Extensive experimental results demonstrate that the proposed LGP-FCM algorithm outperforms comparison algorithms in terms of sensitivity, specificity and accuracy. LGP-FCM can effectively segment the target regions from brain MRI images.

scholarly journals Digital Medical Image Segmentation Using Fuzzy C-Means Clustering

2020 ◽  
Vol 4 (1) ◽  
pp. 51 ◽  
Author(s):  
Bakhtyar Ahmed Mohammed ◽  
Muzhir Shaban Al-Ani
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Brain Mri ◽  
Accurate Method ◽  
Medical Image Segmentation ◽  
Fuzzy C Means ◽  
Fcm Clustering ◽  
The Brain ◽  
Digital Medical Image

In the modern globe, digital medical image processing is a major branch to study in the fields of medical and information technology. Every medical field relies on digital medical imaging in diagnosis for most of their cases. One of the major components of medical image analysis is medical image segmentation. Medical image segmentation participates in the diagnosis process, and it aids the processes of other medical image components to increase the accuracy. In unsupervised methods, fuzzy c-means (FCM) clustering is the most accurate method for image segmentation, and it can be smooth and bear desirable outcomes. The intention of this study is to establish a strong systematic way to segment complicate medical image cases depend on the proposed method to share in the decision-making process. This study mentions medical image modalities and illustrates the steps of the FCM clustering method mathematically with example. It segments magnetic resonance imaging (MRI) of the brain to separate tumor inside the brain MRI according to four statuses.

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