scholarly journals Enlarge Medical Image using Line-Column Interpolation (LCI) Method

2018 ◽  
Vol 8 (5) ◽  
pp. 3620
Author(s):  
Jufriadif Na'am ◽  
Julius Santony ◽  
Yuhandri Yuhandri ◽  
Sumijan Sumijan ◽  
Gunadi Widi Nurcahyo
Keyword(s):  
Medical Image ◽  
Nearest Neighbor ◽  
Medical Images ◽  
Interpolation Method ◽  
Clinical Analysis ◽  
X Ray ◽  
Chest X Ray ◽  
Brain Ct Scan ◽  
Image Pixels

Quality of medical image has an important role in constructing right medical diagnosis. This paper recommends a method to improve the quality of medical images by increasing the size of the image pixels. By increasing the size of pixels, the size of the objects contained therein is also greater, making it easier to observe. In this study medical images of Brain CT-Scan, Chest X-Ray and Panoramic X-Ray were processed using Line-Column Interpolation (LCI) Method. The results of the treatment are then compared to Nearest Neighbor Interpolation (NNI), Bilinear Interpolation (BLI) and Bicubic Interpolation (BCI) processing results. The experiment shows that Line-Column Interpolation Method produces a larger image with details of the objects in it are not blurred and has equal visual effects. Thus, this method is expected to be a reference material in enlarging the size of the medical image for ease in clinical analysis.<br /><br />

Medical Image Enhancement Using Edge Information-Based Methods

2017 ◽  
pp. 123-148
Author(s):  
S. Anand
Keyword(s):  
Image Enhancement ◽  
Medical Image ◽  
Hyperbolic Function ◽  
Edge Information ◽  
X Ray ◽  
Enhancement Method ◽  
Chest X Ray ◽  
Medical Image Enhancement

Medical image enhancement improves the quality and facilitates diagnosis. This chapter investigates three methods of medical image enhancement by exploiting useful edge information. Since edges have higher perceptual importance, the edge information based enhancement process is always of interest. But determination of edge information is not an easy job. The edge information is obtained from various approaches such as differential hyperbolic function, Haar filters and morphological functions. The effectively determined edge information is used for enhancement process. The retinal image enhancement method given in this chapter improves the visual quality of the vessels in the optic region. X-ray image enhancement method presented here is to increase the visibility of the bones. These algorithms are used to enhance the computer tomography, chest x-ray, retinal, and mammogram images. These images are obtained from standard datasets and experimented. The performance of these enhancement methods are quantitatively evaluated.

Medical Image Enhancement Using Edge Information-Based Methods

Biometrics ◽  
2017 ◽  
pp. 1701-1726
Author(s):  
S. Anand
Keyword(s):  
Image Enhancement ◽  
Medical Image ◽  
Hyperbolic Function ◽  
Edge Information ◽  
X Ray ◽  
Enhancement Method ◽  
Chest X Ray ◽  
Medical Image Enhancement

Medical image enhancement improves the quality and facilitates diagnosis. This chapter investigates three methods of medical image enhancement by exploiting useful edge information. Since edges have higher perceptual importance, the edge information based enhancement process is always of interest. But determination of edge information is not an easy job. The edge information is obtained from various approaches such as differential hyperbolic function, Haar filters and morphological functions. The effectively determined edge information is used for enhancement process. The retinal image enhancement method given in this chapter improves the visual quality of the vessels in the optic region. X-ray image enhancement method presented here is to increase the visibility of the bones. These algorithms are used to enhance the computer tomography, chest x-ray, retinal, and mammogram images. These images are obtained from standard datasets and experimented. The performance of these enhancement methods are quantitatively evaluated.

INTEGRATION OF MEDICAL ONTOLOGY CONCEPTS TO ANNOTATE MEDICAL IMAGES

2015 ◽  
Vol 77 (29) ◽  
Author(s):  
Mohd Nizam Saad ◽  
Zurina Muda ◽  
Noraidah Sahari ◽  
Hamzaini Abd Hamid
Keyword(s):  
Medical Image ◽  
Image Annotation ◽  
Medical Images ◽  
Regions Of Interest ◽  
Semantic Description ◽  
Image Description ◽  
X Ray ◽  
Lung Area ◽  
Chest X Ray ◽  
Enrichment Process

Medical ontology has become an important element to enrich semantic description in medical images. The enrichment process can be done with medical image annotation where label or keyword can be added into the image description. Therefore, in this paper, we have proposed an annotation model that contains two components i.e. image processing and annotation component in order to annotate chest X-ray images with relevant medical ontology concepts. The first component helps transforming the original medical image into six regions of interest for the lung area while the second component prepares medical ontology concepts to annotate the derived regions. By annotating the chest X-ray images with medical ontology concepts, we hope to obtain better and accurate image retrieval.

DAUBECHIES COMPLEX WAVELET TRANSFORM BASED TECHNIQUE FOR DENOISING OF MEDICAL IMAGES

2007 ◽  
Vol 07 (04) ◽  
pp. 663-687 ◽  
Author(s):  
ASHISH KHARE ◽  
UMA SHANKER TIWARY
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Medical Image ◽  
Medical Images ◽  
Universal Method ◽  
Complex Wavelet Transform ◽  
Shrinkage Function ◽  
Daubechies Complex Wavelet Transform ◽  
Complex Wavelet

Wavelet based denoising is an effective way to improve the quality of images. Various methods have been proposed for denoising using real-valued wavelet transform. Complex valued wavelets exist but are rarely used. The complex wavelet transform provides phase information and it is shift invariant in nature. In medical image denoising, both removal of phase incoherency as well as maintaining the phase coherency are needed. This paper is an attempt to explore and apply the complex Daubechies wavelet transform for medical image denoising. We have proposed a method to compute a complex threshold, which does not depend on any assumed model of noise. In this sense this is a "universal" method. The proposed complex-domain shrinkage function depends on mean, variance and median of wavelet coefficients. To test the effectiveness of the proposed method, we have computed the input and output SNR and PSNR of various types of medical images. The method gives an improvement for Gaussian additive, Speckle and Salt-&-Pepper noise as well as for the mixture of these noise types for a range of noisy images with 15 db to 30 db noise levels and outperforms other real-valued wavelet transform based methods. The application of the proposed method to Ultrasound, X-ray and MRI images is demonstrated in the experiments.

scholarly journals COVID-19 prediction using chest X-ray medical images and Deep Learning

2021 ◽  
Author(s):  
Nuria Pereira Espasandín ◽  
David Maseda Neira ◽  
Diana Marcela Noriega Cobo ◽  
Iago Iglesias Corrás ◽  
Alejandro Pazos ◽  
...  
Keyword(s):  
Deep Learning ◽  
Medical Images ◽  
X Ray ◽  
Chest X Ray

scholarly journals Feature Extraction Processing Method of Medical Image Fusion Based on Neural Network Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Tianming Song ◽  
Xiaoyang Yu ◽  
Shuang Yu ◽  
Zhe Ren ◽  
Yawei Qu
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Medical Image ◽  
Medical Images ◽  
Clinical Analysis ◽  
Image Features ◽  
Processing Method ◽  
The Body ◽  
Image Feature ◽  
The One

Medical image technology is becoming more and more important in the medical field. It not only provides important information about internal organs of the body for clinical analysis and medical treatment but also assists doctors in diagnosing and treating various diseases. However, in the process of medical image feature extraction, there are some problems, such as inconspicuous feature extraction and low feature preparation rate. Combined with the learning idea of convolution neural network, the image multifeature vectors are quantized in a deeper level, which makes the image features further abstract and not only makes up for the one-sidedness of single feature description but also improves the robustness of feature descriptors. This paper presents a medical image processing method based on multifeature fusion, which has high feature extraction effect on medical images of chest, lung, brain and liver, and can better express the feature relationship of medical images. Experimental results show that the accuracy of the proposed method is more than 5% higher than that of other methods, which shows that the performance of the proposed method is better.

scholarly journals OFCS: Optimized Framework of Compressive Sensing for Medical Images in Bottleneck Network Condition

2018 ◽  
Vol 8 (5) ◽  
pp. 2829
Author(s):  
Lakshminarayana M ◽  
Mrinal Sarvagya
Keyword(s):  
Compressive Sensing ◽  
Medical Image ◽  
Medical Images ◽  
Region Of Interest ◽  
Lossless Compression ◽  
Cost Effective ◽  
Signal Quality ◽  
Effective Solution ◽  
The Cost

Compressive sensing is one of teh cost effective solution towards performing compression of heavier form of signals. We reviewed the existing research contribution towards compressive sensing to find that existing system doesnt offer any form of optimization for which reason the signal are superiorly compressed but at the cost of enough resources. Therefore, we introduce a framework that optimizes the performance of the compressive sensing by introducing 4 sequential algorithms for performing Random Sampling, Lossless Compression for region-of-interest, Compressive Sensing using transform-based scheme, and optimization. The contribution of proposed paper is a good balance between computational efficiency and quality of reconstructed medical image when transmitted over network with low channel capacity. The study outcome shows that proposed system offers maximum signal quality and lower algorithm processing time in contrast to existing compression techniuqes on medical images.

scholarly journals Automated Systems for Detection of COVID-19 Using Chest X-ray Images and Lightweight Convolutional Neural Networks

2020 ◽  
Author(s):  
Ali Mohammad Alqudah ◽  
Shoroq Qazan ◽  
Amin Alqudah
Keyword(s):  
Early Diagnosis ◽  
Nearest Neighbor ◽  
Early Stage ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
X Ray ◽  
Softmax Classifier ◽  
Chest X Ray ◽  
Novel Coronavirus ◽  
Sensitivity Specificity

Abstract Since December 2019, the appearance of an outbreak of a novel coronavirus disease namely COVID-19 and which is previously known as 2019-nCoV. COVID-19 is a type of coronavirus that leads to the general destruction of respiratory systems and a severe respiratory symptom which are associated with highly Intensive Care Unit (ICU) admissions and death. Like any disease, the early diagnosis of coronavirus leads to limit its wide-spreading and increases the recovery rates of patients. The gold standard of COVID-19 detection is the real-time reverse transcription-polymerase chain reaction (RT-PCR) which has been used by the clinician to discover the presence or absence of this type of virus. The clinicians report that this technique has a low positive rate in the early stage of this disease. Based on this, the clinicians were forced to use another way to help in the early diagnosis of COVID-2019. So, the clinician's attention moved towards the medical imaging modalities especially the computed Tomography (CT) and X-ray chest images. Both modalities show that there is a change in the lungs in the case of COVID-19 that is different from any other type of pneumonic disease. Therefore, this research targeted toward employing different Artificial Intelligence (AI) techniques to propose a system for early detection of COVID-19 using chest X-ray images. These images are classified using different AI algorithms and a combination of them, then their performance was evaluated to recognize the best of them. These algorithms include a convolutional neural network (CNN), Softmax, support vector machine (SVM), Random Forest, and K nearest neighbor (KNN). Here CNN is into two scenarios, the first one to classify the X-ray images using a softmax classifier, and the second one to extract automated features from the images and pass these features to other classifiers (SVM, RFF, and KNN). According to the results, the performance of all classifiers is good and most of them record accuracy, sensitivity, specificity, and precision of more than 98%.

scholarly journals PET SFCM image segmentation for alzheimer’s disease

2018 ◽  
Vol 7 (3.3) ◽  
pp. 603
Author(s):  
Dr A. Meenakabilan ◽  
Agathiyan K
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Images ◽  
Computation Time ◽  
Clinical Analysis ◽  
Sample Analysis ◽  
Knowledge Based ◽  
Support Strategy ◽  
Huge Data ◽  
Selection Of

Medical images are known to capture the human body in both anatomical and functional view. These images are interpreted with expert domain for clinical analysis. Here, the selection of image sample plays a fundamental role. However, doctors need to manually obtain this process. But, in order to get similarity between the samples automation is definitely required as it reduces the computation time. So, the automation process should be knowledge based to get better results. This paper highlights the knowledge based automation of medical image sample analysis. It presents a significant assessment of PET – SFCM approach for the segmentation of functional medical images which is considered as the value of neighboring pixels in spatial correlation. Here, the proposed method is used to apply the decision support strategy to identify the effective samples from the huge data collection. The proposed algorithm is implemented in Matlab 7.0. The obtained results were analyzed and compared with other two clustering approaches known as K-Means and Fuzzy C-Means. The resultant images encourage the identification and an evaluation of treatment response in a set of oncological constraints.  

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