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 Medical Image Processing: Detection and Prediction of PCOS – A Systematic Literature Review

2021 ◽  
pp. 80-98
Author(s):  
Siji Jose Pulluparambil ◽  
Subrahmanya Bhat
Keyword(s):  
Image Processing ◽  
Literature Review ◽  
Medical Image ◽  
Polycystic Ovary ◽  
Detection System ◽  
Medical Image Processing ◽  
Data Sets ◽  
Ultrasound Images ◽  
Effective Diagnosis ◽  
Reproductive Aged Women

Purpose: Considered as the most common hormonal disorder among women, polycystic ovary syndrome or PCOS affects 1 in 10 reproductive aged women (18 - 44 years). Ultrasonography is applied for assessing the ovaries to detect PCOS. The patients affected by PCOS consist of 10-12 cysts present in the ovary, but more than 10 cysts are more enough to diagnose the disorder from the ultrasound images. Then, by examining the ultrasound the presence of follicles will be determined. Therefore, the image processing approaches have assisted to identify the characteristics like follicle size, number of follicles and structure to minimize the workload and time of doctors. PCOS do not have better treatment and effective diagnosis. This paper includes reviewing a summary of some of the researches that have been going in area of medical diagnosis. Based on the review, research gap, research agendas to carry out further research are identified. Approach: A detailed study on the algorithms used in medical image processing and classification. Findings: The study indicated that most of the classification of polycystic ovarian syndrome is done merely on the clinical data sets. The new hybrid methodology proposed will be more precise as both images and lifestyle are analysed. Originality: The type of data required for detection system are studied and the architecture and schematic diagram of a proposed system are included. Paper Type: Literature Review.

scholarly journals The IoT and registration of MRI brain diagnosis based on genetic algorithm and convolutional neural network

2022 ◽  
Vol 25 (1) ◽  
pp. 273
Author(s):  
Ahmed Shihab Ahmed ◽  
Hussein Ali Salah
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Image Registration ◽  
Brain Diseases ◽  
Reference Image ◽  
City Hospital ◽  
Brain Image ◽  
Mri Brain ◽  
Proposed Model ◽  
Mri Image

The technology <span>of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combined with phase convolution neural network (CNN) to classify the registration of brain tumors. In this work apply the genetic algorithm and CNN clasifcation in registration of magnetic resonance imaging (MRI) image. This approach follows eight steps, reading the source of MRI brain image and loading the reference image, enhencment all MRI images by bilateral filter, transforming DWT image by applying the DWT2, evaluating (fitness function) each MRI image by using entropy, applying the genetic algorithm, by selecting the two images based on rollout wheel and crossover of the two images, the CNN classify the result of subtraction to normal or abnormal, “in the eighth one,” the Arduino and global system for mobile (GSM) 8080 are applied to send the message to patient. The proposed model is tested on MRI Medical City Hospital in Baghdad database consist 550 normal and 350 abnormal and split to 80% training and 20 testing, the proposed model result achieves the 98.8% </span>accuracy.

Addendum: B-spline interpolation in medical image processing

2001 ◽  
Vol 20 (7) ◽  
pp. 660-665 ◽  
Author(s):  
T.M. Lehmann ◽  
C. Gonner ◽  
K. Spitzer
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Spline Interpolation ◽  
Medical Image Processing ◽  
B Spline

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

Establishing an International Reference Image Database for Research and Development in Medical Image Processing

2004 ◽  
Vol 43 (04) ◽  
pp. 409-412 ◽  
Author(s):  
M. Prinz ◽  
S. Schneider ◽  
O. Sipilä ◽  
K. Spinnler ◽  
J.-P. Vallée ◽  
...  
Keyword(s):  
Image Processing ◽  
Research And Development ◽  
Medical Image ◽  
Medical Image Processing ◽  
Image Database ◽  
Reference Image ◽  
European Federation ◽  
Medical Problems ◽  
Comprehensive Reference

Summary Introduction: The lack of comparability of evaluation results is one of the major obstacles of research and development in Medical Image Processing (MIP). The main reason for that is the usage of different image datasets with different quality, size and Gold standard. Objectives: Therefore, one of the goals of the Working Group on Medical Image Processing of the European Federation for Medical Informatics (EFMI WG MIP) is to develop first parts of a Reference Image Database. Methods: Kernel of the concept is to identify highly relevant medical problems with significant potential for improvement by MIP, and then to provide respective reference datasets. The EFMI WG MIP has primarily the role of a specifying group and an information broker, while the provider user relationships are defined by bilateral co-operation or license agreements. Results: An explorative database prototype has been implemented using the MySQL database software on the Web. Templates for provider user agreements have been worked out and already applied for own ‘pre-RID-MIP’ co-operations of the authors. Discussion and Conclusion: First steps towards a comprehensive reference image database have been done. Issues like funding, motivation, management, provision of Gold standards and evaluation guidelines are to be solved. Due to the interest from research groups and industry the efforts will be continued.

A Non-Rigid Image Registration Algorithm Based on NURBS

2012 ◽  
Vol 170-173 ◽  
pp. 3521-3524
Author(s):  
Jing Jing Wang ◽  
Hong Jun Wang
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Research Work ◽  
Medical Image Processing ◽  
Free Form ◽  
Uniform Grid ◽  
B Spline ◽  
Registration Algorithm ◽  
Image Registration Algorithm ◽  
Challenging Research

Non-rigid image registration is an interesting and challenging research work in medical image processing, computer vision and remote sensing fields. In this paper we present a free form deformable algorithm based on NURBS because NURBS (Non-uniform Rational B Spline ) with a non-uniform grid has a higher registration precision and a higher registration speed in comparison with B spline. In our experiment we compare the NURBS based FFD method with the B spline based FFD method quantitatively. The experiment result shows that the algorithm can improve highly the registration precision.

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