摘要 :
Medical image processing covers various types of images such as tomography, mammography, radiography (X-Ray images), cardiogram, CT scan images etc. X-Ray is a type of image in which electronic radiation is passed in human body to...
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Medical image processing covers various types of images such as tomography, mammography, radiography (X-Ray images), cardiogram, CT scan images etc. X-Ray is a type of image in which electronic radiation is passed in human body to capture images of injured parts. Once the X-Ray image is captured, orthopaedics doctors to detect degenerative conditions, trauma, sports injury, tumors, congenital issues etc. manually diagnose it. In automated medical diagnosis system, image processing has to go through various stages such as image acquisition, enhancement, feature extraction, ROI detection, interpretation etc. Feature extraction is one of the important steps of image processing which mainly focus on detection of the region of interest from the image. It includes various mathematical, statistical and scientific algorithms to detect characteristics from the targeted image to narrow down the image. Many researchers have worked on feature extraction from X-Ray image to contribute into automated X-Ray image processing system. In this research paper, we have presented an extensive research review on "Feature Extraction" step of digital image processing based on X-Ray image of human being.
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摘要 :
Edge detection is the prior stage to object recognition and considered as a pillar for image processing task. It is a process to detect such locations from images in terms of pixels where their intensity changing is abruptly. Ther...
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Edge detection is the prior stage to object recognition and considered as a pillar for image processing task. It is a process to detect such locations from images in terms of pixels where their intensity changing is abruptly. There are many types of images such as medical images, satellite images, articular images, industrial images, general purpose images etc. X-Ray is a type of medical image in which electronic radiation is passed into the human body to capture image of inner parts for better disease diagnoses by orthopaedics or radiologist. In this research paper, we have proposed an improved method to detect edges from human being's X-Ray images based on Gaussian filter and statistical range. Gaussian filter is used for image preprocessing and enhancement. Whereas, Statistical range is used to calculate difference between maximum and minimum pixels from every 3X3 image matrix partition. These two can work to detect edges from X-Ray images. We have also presented a comprehensive comparison of our proposed method with four existing latest methods/algorithms of edge detection. Apart from X-Ray images, experiments have also been conducted on human X-Ray images to detect edges. Further, we have found that our proposed method is superior in terms of MSE, RMSE, PSNR and computation time to detect edges from X-Ray images of human being.
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摘要 :
Day-by-day smartphone network's structures are improving in an efficient manner; they are becoming ideal users to accessing the any web resources or a service, specifically, Services which are access by Internet. Web services that...
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Day-by-day smartphone network's structures are improving in an efficient manner; they are becoming ideal users to accessing the any web resources or a service, specifically, Services which are access by Internet. Web services that are used to provide changed kind of services for an app running on smart mobile users suitable and widespread used; still there are some limitations of the current smart phone clients in common manner, like as low processing speed, limited storage capacity, less band-width, latency, and in-adequate memory. This paper gears a platform free architecture for connecting mobile users to the existing Internet based Services. In this architecture includes a cross-platform design of smart mobile users based on client services and a middleware for acquisitive the communication between mobile users and Internet based Web Services. We have used the architecture for deployed services on cloud platforms, such as "Google App Engine" (GAE) and "CloudSim" to enhance the consistency and scalability and reached up to the end-users.
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摘要 :
Day-by-day smartphone network’s structures are improving in an efficient manner; they are becoming ideal users to accessing the any web resources or a service, specifically, Services which are access by Internet. Web services tha...
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Day-by-day smartphone network’s structures are improving in an efficient manner; they are becoming ideal users to accessing the any web resources or a service, specifically, Services which are access by Internet. Web services that are used to provide changed kind of services for an app running on smart mobile users suitable and widespread used; still there are some limitations of the current smart phone clients in common manner, like as low processing speed, limited storage capacity, less band-width, latency, and in-adequate memory. This paper gears a platform free architecture for connecting mobile users to the existing Internet based Services. In this architecture includes a cross-platform design of smart mobile users based on client services and a middle ware for acquisitive the communication between mobile users and Internet based Web Services. We have used the architecture for deployed services on cloud platforms, such as “Google App Engine” (GAE) and “Cloud Sim” to enhance the consistency and scalability and reached up to the end-users.
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摘要 :
Abstract Image segmentation is an essential phase of medical image processing. Orthopaedics practitioners suggest X-Ray imaging to detect bone related diseases of the patient. Due to increase in the number of bone cancers, bone tu...
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Abstract Image segmentation is an essential phase of medical image processing. Orthopaedics practitioners suggest X-Ray imaging to detect bone related diseases of the patient. Due to increase in the number of bone cancers, bone tumor detection and its segmentation from X-Ray image has become thirst area of research in the medical image analysis. In this research paper, an intelligent assistive algorithm has been proposed, which is called BTDBB to identify bone tumor from human being’s X-Ray images. The proposed algorithm accepts human arm X-Ray images, converts into grayscale, do Gaussian filtering to remove noise and enhancement, Segmentation to divide image into different parts based on threshold, detection of ROI using binary blob pattern analysis, crop image to retain ROI only, measurement of tumor size and finally, detection of bone tumor. The algorithm is implemented in Scilab 5.5.2 open source image processing software using 109 X-Ray images as dataset, out of which 82 images were bone tumor infected. We have compared our proposed algorithm with existing algorithms/methods for performance evaluation using different types of accuracies as evaluation metrics. Further, we have found that proposed algorithm yields an average accuracy of 99.12% and hence, it is superior in performance over existing selected algorithms/methods based on selected parameters.
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