scholarly journals A Distributed and Collaborative Intelligent System for Medical Diagnosis

2013 ◽  
Vol 2 (2) ◽  
pp. 1-16
Author(s):  
Naoufel Khayati ◽  
Wided Lejouad-Chaari
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Intelligent System ◽  
Distributed Environment ◽  
Web Based ◽  
Knowledge Based ◽  
Technological Advances ◽  
And Control ◽  
Control Complex

In this paper, we present a distributed collaborative system assisting physicians in diagnosis when processing medical images. This is a Web-based solution since the different participants and resources are on various sites. It is collaborative because these participants (physicians, radiologists, knowledgebasesdesigners, program developers for medical image processing, etc.) can work collaboratively to enhance the quality of programs and then the quality of the diagnosis results. It is intelligent since it is a knowledge-based system including, but not only, a knowledge base, an inference engine said supervision engine and ontologies. The current work deals with the osteoporosis detection in bone radiographies. We rely on program supervision techniques that aim to automatically plan and control complex software usage. Our main contribution is to allow physicians, who are not experts in computing, to benefit from technological advances made by experts in image processing, and then to efficiently use various osteoporosis detection programs in a distributed environment.

Computing the Medical Image Registration Using Meta-Heuristics

2014 ◽  
Vol 643 ◽  
pp. 237-242 ◽  
Author(s):  
Tahari Abdou El Karim ◽  
Bendakmousse Abdeslam ◽  
Ait Aoudia Samy
Keyword(s):  
Image Processing ◽  
Genetic Algorithms ◽  
Simulated Annealing ◽  
Image Registration ◽  
Cost Function ◽  
Population Genetic ◽  
Medical Image ◽  
Medical Image Registration ◽  
Anatomical Structures

The image registration is a very important task in image processing. In the field of medical imaging, it is used to compare the anatomical structures of two or more images taken at different time to track for example the evolution of a disease. Intensity-based techniques are widely used in the multi-modal registration. To have the best registration, a cost function expressing the similarity between these images is maximized. The registration problem is reduced to the optimization of a cost function. We propose to use neighborhood meta-heuristics (tabu search, simulated annealing) and a meta-heuristic population (genetic algorithms). An evaluation step is necessary to estimate the quality of registration obtained. In this paper we present some results of medical image registration

scholarly journals Intelligent Segmentation and Recognition Method of Breast Cancer Based on Digital Image Processing Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiaochen Tang ◽  
Yunbo An ◽  
Congshan Li
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Medical Image ◽  
Intelligent System ◽  
Recognition System ◽  
Processing Technology ◽  
Image Processing Technology ◽  
Pathological Image

With the development of digital image technology, judging diseases by medical image plays an important role in medical diagnosis. Mammography is the most effective imaging examination method for breast cancer at present. Intelligent segmentation and identification of breast cancer images and judging their size and classification by digital image processing technology can promote the development of clinical medicine. This paper introduces the preprocessing technology of breast cancer pathological image and medical image recognition technology of breast cancer. In order to improve the segmentation accuracy of image processing and optimize, the segmentation recognition ability in digital mammography was improved. Based on the technical basis of pathological image analysis of breast cancer, the architecture of intelligent segmentation and recognition system for breast cancer was constructed, and each functional module of intelligent system was introduced in detail. Based on digital image processing technology, filtering technology is used to reduce dryness and improve the clarity of the image. Public datasets INBreast and DDSM-BCRP were used to verify system’s performance, and it was tested on the breast cancer image test set. The experiment shows that the comprehensive performance of the intelligent segmentation and recognition system can realize the segmentation and recognition of breast cancer and has higher accuracy and interpretability, which is helpful to improve the diagnosis of doctors.

scholarly journals Echocardiography image denoising using fractal wavelet transform

2018 ◽  
Vol 5 ◽  
pp. 23-33
Author(s):  
Reena Manandhar ◽  
Sanjeeb Prashad Pandey
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Gaussian Noise ◽  
Medical Image ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Hard Thresholding ◽  
Echocardiography Image ◽  
Different Types

One of the most important areas in image processing is medical image processing where the quality of the images has become an important issue. Most of the medical images are corrupted with the visual noise, and one of the such images is echocardiography image where this effect is more. So, this research aims to denoise the echocardiography image with fractal wavelet transform and to compare its performance with other wavelet based algorithm like hard thresholding, soft thresholding and wiener filter. Initially, the image is corrupted by the Gaussian noise with varying noise variances and is denoised using above mentioned different wavelet based denoising techniques. On comparison of the obtained results, it is observed that the fractal wavelet transform is well suited for highly degraded echocardiography images in terms of Mean Square Error (MSE) and Peak Signal To Noise Ratio (PSNR) than other wavelet based denoising methods. Further, the work could be enhanced to denoise the echocardiography image corrupted by other different types of noise. This research is limited to denoise the echocardiography image corrupted with Gaussian noise only.

scholarly journals RayPlus: a Web-Based Platform for Medical Image Processing

2016 ◽  
Vol 30 (2) ◽  
pp. 197-203 ◽  
Author(s):  
Rong Yuan ◽  
Ming Luo ◽  
Zhi Sun ◽  
Shuyue Shi ◽  
Peng Xiao ◽  
...  
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Web Based

Web-based interactive 2D/3D medical image processing and visualization software

2010 ◽  
Vol 98 (2) ◽  
pp. 172-182 ◽  
Author(s):  
Seyyed Ehsan Mahmoudi ◽  
Alireza Akhondi-Asl ◽  
Roohollah Rahmani ◽  
Shahrooz Faghih-Roohi ◽  
Vahid Taimouri ◽  
...  
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Web Based ◽  
Visualization Software

Efficient Noise Removal Based Relevant Vector Machine to Detect Lung Disease

2019 ◽  
Vol 16 (8) ◽  
pp. 3372-3377
Author(s):  
P. Dhanalakshmi ◽  
G. Satyavathy
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Research Work ◽  
Multiple Scale ◽  
Noise Removal ◽  
Semantic Gap ◽  
Linear Filters ◽  
Processing Techniques

The quality of images is decreased by noises. There exist several chances for the noises to occur while capturing and transmission the image. Noise removal becomes a thrust area of research in image processing. The outcome of the noise removal shows the quality of digital image processing techniques. Noises in image lead to the semantic gap problem in medical image processing. Semantic gap problem becomes a serious issue in the classification of the medical image. With the aim to overcome this issue, this research work proposes an efficient noise removal method based on relevant vector machine. Instead of using unsuited linear filters to detect noises, this research work uses the nonlinear filters which suit well to detect noises in multiple scale layers. The proposed method is applied to ADL dataset for the detection of lung cancer. The results clearly show that the proposed noise removal based relevant vector machine performs better in terms of accuracy.

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