Browsing by Author "Mei, Jiangyuan"
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Item An automatic and cost-effective parasitemia identification framework for low-end microscopy imaging devices(IEEE, 2015-09-03) Maiseli, Baraka J.; Mei, Jiangyuan; Gao, Huijun; Yin, ShenIn the detection of Malarial parasites from a patient, it is usually necessary to carefully examine the corresponding blood-slide smear and distinguish the infected and healthy Red Blood Cells (RBCs). If this process is done manually, as evidenced in common traditional approaches, the following challenges may be encountered: inaccuracy of the lab results, which originates from normal human errors or lack of experience of a person conducting diagnosis, and large processing times. Consequently, doctors and specialists are likely to provide improper prescriptions to patients. With the improvement of the computational power of computers, however, the whole diagnosis process can be automated. Several methods in literature have been proposed for this purpose. Most of these methods demand the availability of high-end microscopy imaging systems to generate reliable and accurate results. Such costly advanced devices may not be afforded by developing countries with sluggish economic growth. In this paper, therefore, we have developed a cost-effective framework which can address the mentioned challenge. Our approach introduces a Super Resolution (SR) model into the existing framework to enhance the resolution of the input images before letting them subjected to the subsequent detection stages. This provides a possibility for applying the low-end microscopy devices capable of capturing Low Resolution (LR) blood smear images for identifying the degree of Malaria in a patient. In the proposed framework, the SR component uses the nonlinear Charbonnier diffusion model in the regularization part because of its good regularity characteristics. Experimental results demonstrate strong correlation of our method and the manual one.Item An Automatic and Cost-Effective Parasitemia Identification Framework for Low-End Microscopy Imaging Devices(IEEE, 2014) Maiseli, Baraka J.; Mei, Jiangyuan; Gao, Huijun; Yin, ShenIn the detection of Malarial parasites from a patient, it is usually necessary to carefully examine the corresponding blood-slide smear and distinguish the infected and healthy Red Blood Cells (RBCs). If this process is done manually, as evidenced in common traditional approaches, the following challenges may be encountered: inaccuracy of the lab results, which originates from normal human errors or lack of experience of a person conducting diagnosis, and large processing times. Consequently, doctors and specialists are likely to provide improper prescriptions to patients. With the improvement of the computational power of computers, however, the whole diagnosis process can be automated. Several methods in literature have been proposed for this purpose. Most of these methods demand the availability of high-end microscopy imaging systems to generate reliable and accurate results. Such costly advanced devices may not be afforded by developing countries with sluggish economic growth. In this paper, therefore, we have developed a cost-effective framework which can address the mentioned challenge. Our approach introduces a Super Resolution (SR) model into the existing framework to enhance the resolution of the input images before letting them subjected to the subsequent detection stages. This provides a possibility for applying the low-end microscopy devices capable of capturing Low Resolution (LR) blood smear images for identifying the degree of Malaria in a patient. In the proposed framework, the SR component uses the nonlinear Charbonnier diffusion model in the regularization part because of its good regularity characteristics. Experimental results demonstrate strong correlation of our method and the manual one.Item Edge Preservation Image Enlargement and Enhancement Method Based on the Adaptive Perona–Malik Non-Linear Diffusion Model(IEEE, 2014) Maiseli, Baraka J.; Elisha, Ogada Achieng; Mei, Jiangyuan; Gao, HuijunIn this study, the authors have proposed a new super resolution (SR) model based on the Perona–Malik regularisation scheme. The new model integrates into its regularisation component an adaptive exponential term which automatically adjusts itself depending on the local image features. This lends more sensitivity and adaptability to the proposed model, thereby making the reconstruction process much less punishing against semantically important features. Therefore, regularisation is stronger in homogeneous regions, and weaker in the neighbourhood of boundaries. The proposed method has a promising capability of supressing noise more effectively, while preserving important image features. The approach used differs significantly from the available methods, especially in the manner in which adaptability has been deployed. Noting that SR methods are less sensitive to the local image topography, a factor that causes the super-resolved images to be visually poor, the new method sensitively probes the local features of the image, and determines the necessary level of reconstruction and regularisation. Additionally, the formulation robustly introduces a backward diffusion, a phenomenon proved from literature to have a tendency of sharpening edges. The authors have included empirical reconstruction results to demonstrate that their model produces better images in comparison with other classical methods.Item Edge preservation image enlargement and enhancement method based on the adaptive Perona–Malik non-linear diffusion model(IET Image Processing, 2014-12) Maiseli, Baraka J.; Elisha, Ogada Achieng; Mei, Jiangyuan; Gao, JiangyuanIn this study, the authors have proposed a new super resolution (SR) model based on the Perona–Malik regularisation scheme. The new model integrates into its regularisation component an adaptive exponential term which automatically adjusts itself depending on the local image features. This lends more sensitivity and adaptability to the proposed model, thereby making the reconstruction process much less punishing against semantically important features. Therefore, regularisation is stronger in homogeneous regions, and weaker in the neighbourhood of boundaries. The proposed method has a promising capability of supressing noise more effectively, while preserving important image features. The approach used differs significantly from the available methods, especially in the manner in which adaptability has been deployed. Noting that SR methods are less sensitive to the local image topography, a factor that causes the super-resolved images to be visually poor, the new method sensitively probes the local features of the image, and determines the necessary level of reconstruction and regularisation. Additionally, the formulation robustly introduces a backward diffusion, a phenomenon proved from literature to have a tendency of sharpening edges. The authors have included empirical reconstruction results to demonstrate that their model produces better images in comparison with other classical methods.Item A robust super-resolution method with improved high-frequency components estimation and aliasing correction capabilities(Journal of the Franklin Institute, 2014-01) Maiseli, Baraka J.; Wu, Chuan; Mei, Jiangyuan; Liu, Qiang; Gao, HuijunIn this paper, we have proposed a robust super-resolution high-frequency component estimation (RS-HFCE) method, which can efficiently estimate lost high-frequency components and correct aliasing effects of low-frequency components of an image. The fundamental principle of operation of the proposed method is based on the idea that, when a baseband band-limited image signal of known bandwidth in a high-resolution lattice is iteratively low-pass filtered in the frequency domain, the unknown values in the lattice can be interpolated, thus correcting the aliasing for the low-frequency components. If this process is done along with adjusting the amplitudes of the known pixel values, some high-frequency components of an image are automatically extrapolated. In order to provide simultaneous edge preservation and noise removal capabilities of the super-resolved images, an improved version of an adaptive Perona–Malik (PM) model is incorporated into the process. One of the characteristics of the proposed method is its high level of tolerance capabilities to reconstruction errors and noise caused by an increase in the reconstruction scaling factors. High quality images of higher resolution are still appreciably reconstructed when greater magnification factors are used. From a couple of experiments on real images, and using both subjective and objective image quality assessment measures, it is demonstrated that the proposed method outperforms most of other classical methods.