scholarly journals Detection of Tumor Cells in Brain using Cellular Automata with Image Segmentation and Edge Detection

2019 ◽  
Vol 9 (4) ◽  
pp. 11-13
Author(s):  
Dr Kumaravel A. ◽  
◽  
Jasmeena Tariq ◽  
Keyword(s):  
Image Segmentation ◽  
Brain Tumor ◽  
Cellular Automata ◽  
Edge Detection ◽  
Human Life ◽  
Computation Time ◽  
Mri Scan ◽  
Magnetic Resonance Imaging Mri ◽  
Medical Examinations ◽  
Cancerous Cells

Tumor growth or, growth of cancerous cells is a big challenge in today’s medical word. When dealing with human life, the detection of tumors through computers has to be highly accurate. Thus we require the assistance of computer in medical examinations, so that we will get very low rate of false cases. Brain tumor, in today’s world, is seen as most threatening and life taking disease. In order to detect brain tumor more accurately in lesser time, many techniques have already been proposed using image segmentation and edge detection. In our paper we propose a technique which is more efficient to detect brain tumor where edge detection through cellular automata have been used from Magnetic Resonance Imaging (MRI) scan images. It processes these images, and determines the area affected by using segmentation and edge detection with cellular automata. Simulated work is completed with the help of Simulink in MATLAB. Regarding this particular topic there are many studies, however our proposal of combination of both segmentation and edge detection through cellular automata shows better results as compared to combining segmentation with classical edge detection in term of computation time and clarity. This will help in efficiency of detecting brain tumor and later in its removal.

scholarly journals Detection of Tumor Cells in Brain using Cellular Automata with Image Segmentation and Edge Detection.

2019 ◽  
Vol 9 (4) ◽  
pp. 11-13
Keyword(s):  
Image Segmentation ◽  
Brain Tumor ◽  
Cellular Automata ◽  
Edge Detection ◽  
Human Life ◽  
Computation Time ◽  
Mri Scan ◽  
Magnetic Resonance Imaging Mri ◽  
Medical Examinations ◽  
Cancerous Cells

Tumor growth or, growth of cancerous cells is a big challenge in today’s medical word. When dealing with human life, the detection of tumors through computers has to be highly accurate. Thus we require the assistance of computer in medical examinations, so that we will get very low rate of false cases. Brain tumor, in today’s world, is seen as most threatening and life taking disease. In order to detect brain tumor more accurately in lesser time, many techniques have already been proposed using image segmentation and edge detection. In our paper we propose a technique which is more efficient to detect brain tumor where edge detection through cellular automata have been used from Magnetic Resonance Imaging (MRI) scan images. It processes these images, and determines the area affected by using segmentation and edge detection with cellular automata. Simulated work is completed with the help of Simulink in MATLAB. Regarding this particular topic there are many studies, however our proposal of combination of both segmentation and edge detection through cellular automata shows better results as compared to combining segmentation with classical edge detection in term of computation time and clarity. This will help in efficiency of detecting brain tumor and later in its removal.

An Improved Method for Edge Detection and Image Segmentation Using Fuzzy Cellular Automata

2016 ◽  
Vol 47 (3) ◽  
pp. 161-179 ◽  
Author(s):  
Reza Shahverdi ◽  
Madjid Tavana ◽  
Ali Ebrahimnejad ◽  
Khadijeh Zahedi ◽  
Hesam Omranpour
Keyword(s):  
Image Segmentation ◽  
Cellular Automata ◽  
Edge Detection ◽  
Improved Method ◽  
Fuzzy Cellular Automata

scholarly journals Using Semi-Supervised Learning in Cellular Automata for Edge Detection of Image Segmentation

2018 ◽  
Vol 7 (02) ◽  
pp. 23613-23619
Author(s):  
Draiya A. Alaswad ◽  
Yasser F. Hassan
Keyword(s):  
Image Segmentation ◽  
Cellular Automata ◽  
Edge Detection ◽  
Supervised Learning ◽  
Unlabeled Data ◽  
Machine Learning Techniques ◽  
Natural Image ◽  
Clustering Method ◽  
Learning Techniques ◽  
Better Than

Semi-Supervised Learning is an area of increasing importance in Machine Learning techniques that make use of both labeled and unlabeled data. The goal of using both labeled and unlabeled data is to build better learners instead of using each one alone. Semi-supervised learning investigates how to use the information of both labeled and unlabeled examples to perform better than supervised learning. In this paper we present a new method for edge detection of image segmentation using cellular automata with modification for game of life rules and K-means algorithm. We use the semi-supervised clustering method, which can jointly learn to fusion by making use of the unlabeled data. The learning aim consists in distinguishing between edge and no edge for each pixel in image. We have applied the semi-supervised method for finding edge detection in natural image and measured its performance using the Berkeley Segmentation Dataset and Benchmark dataset. The results and experiments showed the accuracy and efficiency of the proposed method.

scholarly journals Overview of Multi-Modal Brain Tumor MR Image Segmentation

Healthcare ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1051
Author(s):  
Wenyin Zhang ◽  
Yong Wu ◽  
Bo Yang ◽  
Shunbo Hu ◽  
Liang Wu ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Diseases ◽  
Learning Methods ◽  
Brain Images ◽  
Advantages And Disadvantages ◽  
Segmentation Methods ◽  
Magnetic Resonance Imaging Mri ◽  
Tumor Mri

The precise segmentation of brain tumor images is a vital step towards accurate diagnosis and effective treatment of brain tumors. Magnetic Resonance Imaging (MRI) can generate brain images without tissue damage or skull artifacts, providing important discriminant information for clinicians in the study of brain tumors and other brain diseases. In this paper, we survey the field of brain tumor MRI images segmentation. Firstly, we present the commonly used databases. Then, we summarize multi-modal brain tumor MRI image segmentation methods, which are divided into three categories: conventional segmentation methods, segmentation methods based on classical machine learning methods, and segmentation methods based on deep learning methods. The principles, structures, advantages and disadvantages of typical algorithms in each method are summarized. Finally, we analyze the challenges, and suggest a prospect for future development trends.

scholarly journals Oral Cancer Detection: Hybrid Method of KFCM Clustering

2020 ◽  
Vol 8 (5) ◽  
pp. 2287-2292
Keyword(s):  
Hybrid Approach ◽  
Computation Time ◽  
Medical Intervention ◽  
Mri Scan ◽  
Risk Of Death ◽  
Segmentation Accuracy ◽  
The World ◽  
Fcm Clustering ◽  
Magnetic Resonance Imaging Mri ◽  
Oral Malignancy

Oral neoplasm is one of the complex diseases in the world. The risk of death is increasing all over the world due to the rapid swelling of abnormal tissues. Early diagnosis of malignancy is necessary to avoid the risk of death. The tumor detected from magnetic resonance imaging (MRI) images is new innovative analysis topic in medical intervention. Normally the internal structure of the mouth can be examined using the MRI scan or CT scan. MRI scan is an advantageous and adequate technique for detection of the oral malignancy. It is non-invasive because it does not use any radiation. In this study, the hybrid approach KFCM is proposed for the segmentation and compared with conventional K-Means & Fuzzy C-Means(FCM).The main objective of merging these two algorithms is to reduce the total iterations generated by initializing an exact cluster to the FCM clustering with less computation time. The developed system concentrated on image enhancement using anisotropic diffusion to improve the quality of image and segmentation technique using KFCM clustering to reduce computation time &improve the segmentation accuracy. It exactly segments the lesion region and evaluates the lesion area.

TUNNELING PHASE LOGIC CELLULAR NONLINEAR NETWORKS

2001 ◽  
Vol 11 (12) ◽  
pp. 2895-2911 ◽  
Author(s):  
TAO YANG ◽  
RICHARD A. KIEHL ◽  
LEON O. CHUA
Keyword(s):  
Image Segmentation ◽  
Cellular Automata ◽  
Edge Detection ◽  
Voltage Source ◽  
Sinusoidal Voltage ◽  
Simple Circuit ◽  
Dc Voltage ◽  
Nonlinear Networks ◽  
2D Arrays ◽  
Image Computation

Based on a simple circuit model of a tunneling phase logic (TPL) element that is driven by a sinusoidal voltage source and biased by a DC voltage source, we present simulations of operations in cellular nonlinear networks (CNN) that could potentially be used to perform general computations in 2D arrays of simple, locally connected nanoscale devices. Some examples are presented to demonstrate the image computation capability of TPL–CNN. In particular, we use a simple 2D TPL–CNN structure to perform edge detection, image enhancement and image segmentation. Some cellular automata (CA)-like behaviors of our 2D TPL-CNN are also presented.

Neuro-Radiosurgery Treatments: MRI Brain Tumor Seeded Image Segmentation Based on a Cellular Automata Model

2016 ◽  
pp. 323-333 ◽  
Author(s):  
Leonardo Rundo ◽  
Carmelo Militello ◽  
Giorgio Russo ◽  
Pietro Pisciotta ◽  
Lucia Maria Valastro ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Brain Tumor ◽  
Cellular Automata ◽  
Cellular Automata Model ◽  
Mri Brain

scholarly journals An Efficient Fuzzy Technique for Detection of Brain Tumor

2013 ◽  
Vol 8 (2) ◽  
pp. 813-818 ◽  
Author(s):  
P.G.K. Sirisha ◽  
C. Naga Raju ◽  
R. Pradeep Kumar Reddy
Keyword(s):  
Image Segmentation ◽  
Brain Tumor ◽  
Medical Image ◽  
Research Field ◽  
Medical Image Segmentation ◽  
Mri Scan ◽  
Innovative Method ◽  
Otsu Method ◽  
The Brain

   In this epoch Medical Image segmentation is one of the most challenging problems in the research field of MRI scan image classification and analysis. The importance of image segmentation is to identify various features of the image that are used for analyzing, interpreting and understanding of images. Image segmentation for MRI of brain is highly essential due to accurate detection of brain tumor. This paper presents an efficient image segmentation technique that can be used for detection of tumor in the Brain. This innovative method consists of three steps. First is Image enhancement to improve the quality of the tumor image by eliminating noise and to normalize the image. Second is fuzzy logic which produce optimal threshold to avoid the fuzziness in the image and makes good regions regarding Image and tumor part of the Image. Third is novel OTSU technique applied for separating the tumor regions in the MRI. This method has produced better results than traditional extended OTSU method.

scholarly journals Segmentation Techniques for Brain Tumor from MRI – A Survey

2020 ◽  
Author(s):  
Jeevitha R ◽  
Selvaraj D
Keyword(s):  
Brain Tumor ◽  
Medical Science ◽  
Radio Waves ◽  
Mri Scan ◽  
Generative Adversarial Network ◽  
Usual Procedure ◽  
Adversarial Network ◽  
Contrast Enhanced ◽  
Pros And Cons ◽  
Magnetic Resonance Imaging Mri

In the medical science, Biomedical images are the core. Generally, Magnetic Resonance Imaging(MRI) scan is the most usual procedure followed. Radio waves and strong magnetic flux were used to determine comprehensive images of tissues and organs inside the body. The enhancement in MRI scan has become a large milestone in the medical world. Generally, the brain is segmented into White and gray matter, and cerebrospinal fluid(CSF). Various segmentation techniques have been proposed with promising results. Still, they all have their own pros and cons. Deep neural networks(DNN) have established good performance in segmentation and classification task via Deep Wavelet Autoencoder(DWA). In this study, by using a pairwise Generative Adversarial Network(GAN) model, it addresses the problems in brain tumor detection using MRI from various scanner modalities T1 weighted, T2 weighted, T1 weighted with contrast-enhanced and FLAIR images.

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