[Show abstract][Hide abstract] ABSTRACT: This article is to inject insulin for type 1 diabetes patients by pumping the insulin. The blood glucose level has to be monitored with the help of glucose tolerance test. For efficient method to control type 1 diabetes we have used embedded linear parameter varying methodology controller. The joint element between glucose sensing and insulin delivering actions is an automatic algorithm-based decision. The insulin infusion rate during hyperglycemia, exercise and nocturnal hypoglycemia are known in order to mimic the insulin release pattern generated by controller and considered in the design. The inclusion of mathematical model of relations between glucose and chosen bio-signals in the control loop generates an adequate insulin infusion pattern to compensate blood glucose variations during each chemical processing occurring in living cells. This approach of automatic algorithm for decision shows good performance in controlling glycaemia in blood glucose, ensuring insulin distillation with a delivery form closer to that generated by a healthy pancreas. The biological perspective discussed in this article namely: Regulation in healthy system, modeling methodology, ARX (Auto regressive exogenous) technique. This paper aims to achieve a better blood glucose control profile by incorporating the time-dependent uncertainties in diabetic patient parameters into formulations of optimal control using a novel approach. The time-dependent uncertainties are represented using stochastic processes and the mathematical formulation for this problem is presented. This method holds a lot of promise in reducing the wide swings of blood glucose observed in diabetic patients and preventing complications of diabetes.
Full-text · Article · Feb 2015 · Journal of Medical Imaging and Health Informatics
[Show abstract][Hide abstract] ABSTRACT: Unstable nature in blood sugar of diabetic patients influences more innovative techniques to come for regular usage. Researcher proposes a new approach to control the glucose level of diabetic patients using various biological metrics of the patients. The proposed system is a fuzzy one which uses various rule sets to control the glucose level. Researcher uses the following biological metrics like body temperature, pressure, glucose, red blood cell count, white blood cell count and plasma cells. All the biological features are monitored periodically based on which the proposed system comes to a decision of injecting insulin and rate of medicine. Researcher used various control circuits and regulators and monitors to track the biological features dynamically. The proposed systems have more impact on society for the better control of diabetics.
[Show abstract][Hide abstract] ABSTRACT: The main objective of this study is to inject insulin for type 1 diabetes patient by pumping the insulin. The blood glucose level has to be monitored with the help of glucose tolerance test. For efficient method to control type 1 diabetes, we will use embedded linear parameter vaiying methodology controller. In this study, there are three steps, researchers need to focus. First is the sensor values read from the sensors has to be monitored, second is the lab information (patient's basic level of tests). Third the feedback after the compensation of the first and second steps. Depending on the patients test details the insulin has to be injected. For example, if the person sensor value is greater than the reference limit then he has to be provided with insulin for a longer period. So, the comparison of lab details with the patients current sensor values play a vital role in determining the insulin level for a patient. Finally, the feedback has to be obtained with the help of those comparisons and it has to be sent once again as a loop to the controller for later comparison and also for database information. Here, the controller is the key element for updating all the information about the patient and it will control all the parameters of the board. Here, researcherss are using EEPROM to save all the data on location basis. It is capable of holding 256 bytes at a time and each location can store one byte information at a time.
[Show abstract][Hide abstract] ABSTRACT: Spline-based approach is proposed to remove very high density salt-and-pepper noise in grayscale and color images. The algorithm consists of two stages, the first stage detects whether the pixel is noisy or noise-free. The second stage removes the noisy pixel by recursive spline interpolation filter. The proposed recursive spline interpolation filter is based on the neighborhood noise-free pixels and previous noise-free output pixel; hence, it is termed as recursive spline interpolation filter. The performance of the proposed algorithm is compared with the existing algorithms like standard median filter, decision-based filter, progressive switched median filter, and modified decision-based unsymmetric trimmed median filter at very high noise density. The proposed algorithm gives better peak signal-to-noise ratio, image enhancement factor, and correlation factor results than the existing algorithms.
No preview · Article · Jan 2014 · Signal Image and Video Processing
[Show abstract][Hide abstract] ABSTRACT: This paper proposes a new anisotropic diffusion approach to remove the impulse noise and retain the fine details. The proposed approach contains two stages, the first stage detects the impulse noise, and the second stage removes the noisy pixel and retains the fine details of the original image. The Laplacian operator is used to fine-tune the image quality of the restored image in the anisotropic diffusion filter. The proposed approach is tested with PSNR, IEF, correlation factor, and NSER for different test images and the results are compared against existing algorithms. The simulation results show that the proposed approach gives better results than the existing denoising algorithms.
No preview · Article · Jan 2013 · AEU - International Journal of Electronics and Communications
[Show abstract][Hide abstract] ABSTRACT: Mammography is the most efficient technique for detecting and diagnosing breast cancer. Clusters of microcalcifications have been mainly targeted as a reliable early sign of breast cancer and their earliest detection is essential to reduce the probability of mortality rate. Since the size of microcalcifications is very tiny and may be overlooked by the observing radiologist, we have developed a Computer Aided Diagnosis system for automatic and accurate cluster detection. A three-phased novel approach is presented in this paper. Firstly, regions of interest that corresponds to microcalcifications are identified. This can be achieved by analyzing the bandpass coefficients of the mammogram image. The suspicious regions are passed to the second phase, in which the nodular structured microcalcifications are detected based on eigenvalues of second order partial derivatives of the image and microcalcification pixels are segmented out by exploiting the foveal segmentation in multiscale analysis. Finally, by combining the responses coming out from the second order partial derivatives and the foveal method, potential microcalcifications are detected. The detection performance of the proposed method has been evaluated by using 370 mammograms. The detection method has a TP ratio of 97.76 % with 0.68 false positives per image. We have examined the performance of our computerized scheme using free-response operating characteristics curve.
Full-text · Article · May 2012 · Journal of Digital Imaging
[Show abstract][Hide abstract] ABSTRACT: In this paper, a new algorithm is introduced to remove the random valued impulse noise in images. This algorithm contains two stages. The first stage detects the noisy pixels in the image. In the second stage, the noisy pixel is replaced by the median value of the neighborhood noise free pixels. The absolute difference is used to detect the noisy pixel and trimmed median value replaces the noisy pixel. This proposed algorithm shows better results than the Progressive Switching Median Filter (PSM), Pixel-wise Median Absolute Difference (PWMAD), Tristate median filter (TSM), Efficient Procedure for removing Random Valued Impulse Noise (EPRIN) and Optimal Direction Based random valued impulse noise (ODRIN). The proposed algorithm is tested for different gray scale images and it gives better Peak Signal to Noise Ratio.
[Show abstract][Hide abstract] ABSTRACT: In this paper, a combined fuzzy logic and unsymmetric trimmed median filter approach is proposed to remove the high density salt and pepper noise in gray scale and colour images. This algorithm is a combination of decision based unsymmetrical trimmed median filter and fuzzy thresholding technique to preserve edges and fine details in an image. The decision based unsymmetric trimmed median filter fails if all the elements in the selected window are 0's or 255's. One of the possible solutions is to replace the processing pixel by the mean value of the elements in the window. This will lead to blurring of the edges and fine details in the image. To preserve the edges and fine details, the combined fuzzy logic and unsymmetric trimmed median filter approach is proposed in this paper. The better performance of the proposed algorithm is demonstrated on the basis of PSNR and IEF values.
Preview · Article · Jan 2012 · WSEAS Transactions on Signal Processing
[Show abstract][Hide abstract] ABSTRACT: Medical imaging is the technique and process used to create images of the human body for clinical purposes seeking to reveal, diagnose medical science. It is often perceived to designate the set of techniques that noninvasively produce images of the internal aspect of the body. The development of multimodality methodology based on nuclear medicine (NM), positron emission tomography (PET) imaging, magnetic resonance imaging (MRI), and optical imaging is the single biggest focus in many imaging and cancer centres worldwide and is bringing together researchers and engineers from the farranging fields of molecular pharmacology to nanotechnology engineering. This paper presents a new technique for registration of multimodal images (CT and MRI) using mutual information. The optimization of the images is done by using down sampling technique and also the same algorithm is tested by sub sampling. The speed and computation of both the sampling methods are compared and the results are plotted.
[Show abstract][Hide abstract] ABSTRACT: In this paper, we propose a modified switching bilateral filter to remove impulse noise and enhance the image details in an image. The proposed filter consists of noise detection stage and noise reduction stage. The noise detection is based on the gray level Lmin, Lmax]. The noise reduction is based on the global trimmed mean with modified switching bilateral filter. This modified switching bilateral filter effectively removes the salt and pepper noise at very high noise density. Simulation results show that our proposed filter achieves high peak signal to noise ratio, Image Enhancement factor and correlation factor. Even though the time complexity of proposed filter is greater than the other impulse noise filters, the performance of the proposed filter with respect to noise removal is better than the existing filters.
[Show abstract][Hide abstract] ABSTRACT: This paper proposes a new algorithm to remove salt and pepper noise in video. The adaptive decision algorithm first checks whether the selected pixel in the video sequence is noisy or noise free. Initially the window size is selected as 3 × 3. If the selected pixel within the window is 0's or 255's, and some of other pixels within the window are noise free, then the selected pixel value is replaced by trimmed median value. If the selected pixel is 0 or 255 and other pixel values in a selected window (3 × 3) all are 0's and 255's, then change the selected window size as 5 × 5, then the selected pixel value is replaced by trimmed median value. In the selected new window (5 × 5), all the elements are 0's or 255's then the processing pixel is replaced by the previous resultant pixel. Finally, the performance of the proposed algorithm is compared with the existing algorithms like Median Filter; Decision Based Filter and Progressive Switched Median Filter. The proposed algorithm gives better PSNR and IEF results than the existing algorithms.
[Show abstract][Hide abstract] ABSTRACT: Mammography is the most used diagnostic technique for breast cancer. Microcalcification clusters are the early sign of breast cancer and their early detection is a key to increase the survival rate of women. The appearance of microcalcification clusters in mammogram as small localized granular points, which is difficult to identify by radiologists because of its tiny size. An efficient method to improve diagnostic accuracy in digitized mammograms is the use of Computer Aided Diagnosis (CAD) system. This paper presents Multiresolution based foveal algorithm for microcalcification detection in mammograms. The detection of microcalcifications is achieved by decomposing the mammogram by wavelet transform without sampling operator into different sub-bands, suppressing the coarsest approximation subband, and finally reconstructing the mammogram from the subbands containing only significant detail information. The significant details are obtained by foveal concepts. Experimental results show that the proposed method is better in detecting the microcalcification clusters than other wavelet decomposition methods.
[Show abstract][Hide abstract] ABSTRACT: Microcalcifications in mammogram have been mainly targeted as a reliable earliest sign of breast cancer and their early detection is vital to improve its prognosis. Since their size is very small and may be easily overlooked by the examining radiologist, computer-based detection output can assist the radiologist to improve the diagnostic accuracy. In this paper, we have proposed an algorithm for detecting microcalcification in mammogram. The proposed microcalcification detection algorithm involves mammogram quality enhancement using multirresolution analysis based on the dyadic wavelet transform and microcalcification detection by fuzzy shell clustering. It may be possible to detect nodular components such as microcalcification accurately by introducing shape information. The effectiveness of the proposed algorithm for microcalcification detection is confirmed by experimental results.
[Show abstract][Hide abstract] ABSTRACT: The most efficient imaging techniques for the early detection and diagnosis of breast cancer in woman is mammography. Microcalcification is the earliest sign of breast carcinomas and their detection is one of the key issues for breast cancer control. Their small size makes their detection complex for the radiologist. This brings in the role of CAD (Computer Aided Diagnosis) which serves as an assistant to the radiologist. One of the most powerful computing methods is the use of multiresolution analysis of digitized mammogram images with wavelet transform as foundation tool. The proposed Microcalcification detection method involves image denoising using wavelet-based multiscale product thresholding, image enhancement by adaptive operator integrated in the wavelet domain and Microcalcification detection using neural network has been combined with wavelet. Preliminary results indicate that the possible Microcalcifications are detected precisely and efficiently.
[Show abstract][Hide abstract] ABSTRACT: Mammography is the most efficient method for breast cancer early detection. Clusters of microcalcifications are the sign of breast cancer and their early detection is the key to improve breast cancer prognosis. Microcalcifications appear in mammogram as tiny granular points, which are difficult to observe by radiologists due to their small size. An efficient method for automatic and accurate detection of clustered microcalcifications in digitized mammograms is the use of Computer Aided Diagnosis (CAD) systems. This paper presents a novel approach based on multiscale products of eigenvalues of Hessian matrix. The detection of microcalcifications is achieved by decomposing the mammograms by filter bank based on Hessian matrix into different frequency sub-bands, suppressing the low-frequency subband, and finally reconstructing the subbands containing only significant high frequencies features. The significant features are obtained by multiscale products. Preliminary results indicate that the proposed scheme is better in suppressing the background and detecting the microcalcification clusters than any other detection methods.