scholarly journals Efficient computer aided diagnosis of abnormal parts detection in magnetic resonance images using hybrid abnormality detection algorithm

2013 ◽  
Vol 3 (3) ◽  
Author(s):  
C. Lakshmi Devasena ◽  
M. Hemalatha
Keyword(s):  
Magnetic Resonance ◽  
Medical Diagnosis ◽  
Research Work ◽  
Detection Algorithm ◽  
Computer Aided Diagnosis ◽  
Magnetic Resonance Images ◽  
Feature Reduction ◽  
Computer Aided ◽  
Decision Making System ◽  
Aided Diagnosis

AbstractIn Medical Diagnosis, Magnetic Resonance Image (MRI) plays a momentous role. MRI is based on the physical and chemical principles of Nuclear Magnetic Resonance (NMR), a technique used to gain information about the nature of molecules. Retrieving a high quality MR Image for a medical diagnosis is critical. So denoising of Magnetic Resonance (MR) images and making them easy for human understanding form is a challenge. This research work presents an efficient Hybrid Abnormal Detection Algorithm (HADA) to detect the abnormalities in any part of the human body by MRIs. The proposed technique includes five stages: Noise Reduction, Smoothing, Feature Extraction, Feature Reduction and Classification. The proposed algorithm has been implemented and Classification accuracy of 98.80% has been achieved. The result shows that the proposed technique is robust and effective compared to other recent works. The system developed using the proposed algorithm will be a good computer aided diagnosis and decision making system in healthcare.

scholarly journals Computer-Aided Diagnosis Systems for Brain Diseases in Magnetic Resonance Images

Algorithms ◽  
2009 ◽  
Vol 2 (3) ◽  
pp. 925-952 ◽  
Author(s):  
Hidetaka Arimura ◽  
Taiki Magome ◽  
Yasuo Yamashita ◽  
Daisuke Yamamoto
Keyword(s):  
Magnetic Resonance ◽  
Computer Aided Diagnosis ◽  
Magnetic Resonance Images ◽  
Brain Diseases ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals PBTNet: A New Computer-Aided Diagnosis System for Detecting Primary Brain Tumors

2021 ◽  
Vol 9 ◽  
Author(s):  
Si-Yuan Lu ◽  
Suresh Chandra Satapathy ◽  
Shui-Hua Wang ◽  
Yu-Dong Zhang
Keyword(s):  
Magnetic Resonance ◽  
Brain Tumors ◽  
Computer Aided Diagnosis ◽  
Magnetic Resonance Images ◽  
Brain Diseases ◽  
Functional Link ◽  
Diagnosis System ◽  
Primary Brain Tumors ◽  
Computer Aided ◽  
Aided Diagnosis

Brain tumors are among the leading human killers. There are over 120 different types of brain tumors, but they mainly fall into two groups: primary brain tumors and metastatic brain tumors. Primary brain tumors develop from normal brain cells. Early and accurate detection of primary brain tumors is vital for the treatment of this disease. Magnetic resonance imaging is the most common method to diagnose brain diseases, but the manual interpretation of the images suffers from high inter-observer variance. In this paper, we presented a new computer-aided diagnosis system named PBTNet for detecting primary brain tumors in magnetic resonance images. A pre-trained ResNet-18 was selected as the backbone model in our PBTNet, but it was fine-tuned only for feature extraction. Then, three randomized neural networks, Schmidt neural network, random vector functional-link, and extreme learning machine served as the classifiers in the PBTNet, which were trained with the features and their labels. The final predictions of the PBTNet were generated by the ensemble of the outputs from the three classifiers. 5-fold cross-validation was employed to evaluate the classification performance of the PBTNet, and experimental results demonstrated that the proposed PBTNet was an effective tool for the diagnosis of primary brain tumors.

scholarly journals Optimized Resolution-Oriented Many-to-One Intensity Standardization Method for Magnetic Resonance Images

2019 ◽  
Vol 9 (24) ◽  
pp. 5531
Author(s):  
Yuan Gao ◽  
Yuanyuan Wang ◽  
Jinhua Yu
Keyword(s):  
Magnetic Resonance ◽  
Visual Information ◽  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Computer Aided Diagnosis ◽  
Magnetic Resonance Images ◽  
Image Resolution ◽  
Mr Images ◽  
Computer Aided ◽  
Aided Diagnosis

With the development of big data, Radiomics and deep-learning methods based on magnetic resonance (MR) images, it is necessary to conduct large databases containing MR images from multiple centers. Having huge intensity distribution differences among images reduced or even eliminated, robust computer-aided diagnosis models could be established. Therefore, an optimized intensity standardization model is proposed. The network structure, loss function, and data input strategy were optimized to better avoid the image resolution loss during transformation. The experimental dataset was obtained from five MR scanners located in four hospitals and was divided into nine groups based on the imaging parameters, during which 9152 MR images from 499 participants were collected. Experiments show the superiority of the proposed method to the previously proposed unified model in resolution metrics including the peak signal-to-noise ratio, structural similarity, visual information fidelity, universal quality index, and image fidelity criterion. Another experiment further shows the advantage of the proposed method in increasing the effectiveness of following computer-aided diagnosis models by better preservation of MR image details. Moreover, the advantage over conventional standardization methods are also shown. Thus, MR images from different centers can be standardized using the proposed method, which will facilitate numerous data-driven medical imaging studies.

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