scholarly journals Segmentation of MR Brain Image using Multi Atlas Matching Technique

2019 ◽  
Vol 8 (6S) ◽  
pp. 513-517
Keyword(s):  
Medical Image ◽  
Medical Image Processing ◽  
Histogram Equalization ◽  
Experimental Result ◽  
Tumor Segmentation ◽  
Brain Image ◽  
Adaptive Histogram Equalization ◽  
Matching Technique ◽  
Tumor Region ◽  
Mr Brain

Detection of brain tumor from Magnetic Resonance Image (MRI) image has become one of the most active researches in the field of medical image processing. Segmentation and Detection of tumor play a major role in biomedical imaging. In this research, tumor segmentation process is done with MR brain image. The proposed method contain image pre processing, image enhancement using Contrast Limited Adaptive Histogram Equalization (CLAHE) and segmentation with multi atlas matching and detection of tumor. The proposed work segment the tumor region precisely from the MR brain image. The experimental result gives an average of 0.85 Dice Similarity Co efficient (DSC), which indicates that the proposed method is efficient in segmentation and detection of the tumor region from the MR brain image

scholarly journals WAVELET SHRINKAGE ADAPTIVE HISTOGRAM EQUALIZATION FOR MEDICAL IMAGES

2014 ◽  
pp. 191-196
Author(s):  
Anbu Megelin star ◽  
Perumal Subburaj
Keyword(s):  
Medical Image ◽  
Medical Image Processing ◽  
Histogram Equalization ◽  
Wavelet Shrinkage ◽  
Image Brightness ◽  
Adaptive Histogram Equalization ◽  
Approximation Coefficients ◽  
Medical Image Enhancement ◽  
Novel Algorithm

Enhancement techniques play a major role in medical image processing, to improve the quality of raw images. This paper proposes a novel algorithm namely wavelet shrinkage adaptive histogram equalization (WSAHE) for medical image enhancement. This algorithm consists of four stages namely, decomposition of images using wavelet transform, application of adaptive histogram equalization on the approximation coefficients, application of shrinkage on the detailed coefficients and the reconstruction of image. Experiments show that the proposed method enhances the image brightness while preserving edges.

scholarly journals Enhancement of MRI Prostate Image Using Histogram Equalization Techniques

2019 ◽  
Vol 9 (1S) ◽  
pp. 96-101
Keyword(s):  
Image Enhancement ◽  
Medical Image ◽  
Medical Technology ◽  
Medical Image Processing ◽  
Histogram Equalization ◽  
Resonance Imaging ◽  
Adaptive Histogram Equalization ◽  
Equalization Technique ◽  
Image Modality ◽  
Mri Image

In this cutting edge world, Medical image processing in computerized field needs a compelling MRI image modality with less commotion and improved contrast of image. This is conceivable by utilizing image enhancement methodology. Image enhancement is referenced as a system of changing or altering image so as to make it progressively sensible for explicit applications and is utilized to enhance or improve contrast proportion, splendor of image, expel clamor from image and make it less hard to perceive. The purpose behind inclining toward Medical Resonance Imaging (MRI) is that it is a mind boggling medical technology which gives more useful information regarding malignancy, stroke and various another ailments. It helps the doctors to distinguish the diseases more adequately. MRI has exceptionally low difference proportion. To improve the contrast of MRI image, we utilized Histogram equalization technique. In which, Histogram Equalization, Local Histogram Equalization, Adaptive Histogram Equalization and Contrast Limited Adaptive Histogram Equalization techniques were used and it is pondered.

Brain Tumor Segmentation Based on Region of Interest-Aided Localization and Segmentation U-Net

2021 ◽  
Author(s):  
Shidong Li ◽  
Jianwei Liu ◽  
Zhanjie Song
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Brain Tumor ◽  
Medical Image ◽  
Brain Mri ◽  
Medical Image Processing ◽  
Tumor Segmentation ◽  
Dice Similarity Coefficient ◽  
Brain Tumor Segmentation ◽  
The Impact

Abstract Since magnetic resonance imaging (MRI) has superior soft tissue contrast, contouring (brain) tumor accurately by MRI images is essential in medical image processing. Segmenting tumor accurately is immensely challenging, since tumor and normal tissues are often inextricably intertwined in the brain. It is also extremely time consuming manually. Late deep learning techniques start to show reasonable success in brain tumor segmentation automatically. The purpose of this study is to develop a new region-ofinterest-aided (ROI-aided) deep learning technique for automatic brain tumor MRI segmentation. The method consists of two major steps. Step one is to use a 2D network with U-Net architecture to localize the tumor ROI, which is to reduce the impact of normal tissue’s disturbance. Then a 3D U-Net is performed in step 2 for tumor segmentation within identified ROI. The proposed method is validated on MICCAI BraTS 2015 Challenge with 220 high Gliomas grade (HGG) and 54 low Gliomas grade (LGG) patients’ data. The Dice similarity coefficient and the Hausdorff distance between the manual tumor contour and that segmented by the proposed method are 0.876 ±0.068 and 3.594±1.347 mm, respectively. These numbers are indications that our proposed method is an effective ROI-aided deep learning strategy for brain MRI tumor segmentation, and a valid and useful tool in medical image processing.

scholarly journals Image Defogging Framework Using Segmentation and the Dark Channel Prior

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 285
Author(s):  
Sabiha Anan ◽  
Mohammad Ibrahim Khan ◽  
Mir Md Saki Kowsar ◽  
Kaushik Deb ◽  
Pranab Kumar Dhar ◽  
...  
Keyword(s):  
Histogram Equalization ◽  
Homogeneous Region ◽  
Experimental Result ◽  
Dark Channel Prior ◽  
Color Information ◽  
Visual Effects ◽  
Low Contrast ◽  
Adaptive Histogram Equalization ◽  
Processing Step ◽  
Dark Channel

Foggy images suffer from low contrast and poor visibility problem along with little color information of the scene. It is imperative to remove fog from images as a pre-processing step in computer vision. The Dark Channel Prior (DCP) technique is a very promising defogging technique due to excellent restoring results for images containing no homogeneous region. However, having a large homogeneous region such as sky region, the restored images suffer from color distortion and block effects. Thus, to overcome the limitation of DCP method, we introduce a framework which is based on sky and non-sky region segmentation and restoring sky and non-sky parts separately. Here, isolation of the sky and non-sky part is done by using a binary mask formulated by floodfill algorithm. The foggy sky part is restored by using Contrast Limited Adaptive Histogram Equalization (CLAHE) and non-sky part by modified DCP. The restored parts are blended together for the resultant image. The proposed method is evaluated using both synthetic and real world foggy images against state of the art techniques. The experimental result shows that our proposed method provides better entropy value than other stated techniques along with have better natural visual effects while consuming much lower processing time.

scholarly journals An Efficient MRI Brain Image Registration and Wavelet Based Fusion

2019 ◽  
Vol 8 (4) ◽  
pp. 10209-10218
Keyword(s):  
Affine Transformation ◽  
Medical Image ◽  
Medical Image Processing ◽  
Reference Image ◽  
Data Sets ◽  
B Spline ◽  
Brain Image ◽  
Mri Brain ◽  
Medical Analysis ◽  
2D Images

Over last few decades, 3D reconstruction of medical images becomes advance technique in medical image processing. Reconstruction of 2D images of such data sets into 3D volumes, via registration of 2D sections had become a most interesting topic. In current years, MRI has been used for many medical analysis applications. The proposed system considered MRI images are taken from the same view, different times or acquired by different imaging modalities to increase the information. T1, T2 and PD MRI are taken as an input; T2 image is registered with a reference of T1 image using affine transformation, the registered T2 image is fused with T1 using DWT. The Fused T1T2 image is taken as reference image to register PD image using B-Spline transformation. DT-CWT technique is used to fuse the T1T2 image with registered PD image. The performance of the system shows that the proposed system gives more information by fusing T1T2PD images.

scholarly journals Image Segmentation using Various Approaches

2013 ◽  
pp. 86-95
Author(s):  
Snehal B. Ranit ◽  
Dr. Nileshsingh V. Thakur
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Medical Image ◽  
Euclidean Distance ◽  
Cluster Formation ◽  
Medical Image Processing ◽  
Experimental Result ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
Segmentation Image

This paper addresses the issue of image segmentation. Image segmentation process is the main basic process or technique used in various image processing problem domains, for example, content based image retrieval; pattern recognition; object recognition; face recognition; medical image processing; fault detection in product industries; etc. Scope of improvement exists in the following areas: Image partitioning; color based feature; texture based feature, searching mechanism for similarity; cluster formation logic; pixel connectivity criterion; intelligent decision making for clustering; processing time; etc. This paper presents the image segmentation mechanism which addresses few of the identified areas where the scope of contribution exists. Presented work basically deals with the development of the mechanism which is divided into three parts first part focuses on the color based image segmentation using k-means clustering methodology. Second part deals with region properties based segmentation. Later, deals with the boundary based segmentation. In all these three approaches, finally the Steiner tree is created to identify the class of the region. For this purpose the Euclidean distance is used. Experimental result justifies the application of the developed mechanism for the image segmentation.

scholarly journals Diabetic Retinopathy Detection using Image Processing

2019 ◽  
Vol 8 (6S) ◽  
pp. 937-941
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Medical Image ◽  
Histogram Equalization ◽  
Fundus Image ◽  
Adaptive Histogram Equalization ◽  
Retinal Fundus Image ◽  
Equalization Algorithm ◽  
Processing Techniques ◽  
Retinal Fundus

The main objective of this method is to detect DR (Diabetic Retinopathy) eye disease using Image Processing techniques. The tool used in this method is MATLAB (R2010a) and it is widely used in image processing. This paper proposes a method for Extraction of Blood Vessels from the medical image of human eye-retinal fundus image that can be used in ophthalmology for detecting DR. This method utilizes an approach of Adaptive Histogram Equalization using CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm with open CV (Computer Vision) framework implementation. The result shows that affected DR is detected in fundus image and the DR is not detected in the healthy fundus image and 98% of Accuracy can be achieved in the detection of DR.

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