[Ys Jagan Images Hd 1080p
Facunda Ganesh
In modern days, image processing methods are widely adopted in the medical field for enhancing the earlier detection of certain abnormalities, such as the breast cancer, lung cancer, brain cancer and so on. This paper mainly concentrates on the segmentation of lung cancer tumors from X-ray images, Computed Tomography (CT) images and MRI images. Image processing methods are adopted in segmenting the images. In the pre-processing stage mean and median filters are used. In the image segmentation stage, Otsu's thresholding and k-Means clustering segmentation approaches are used to segment the lung images and locate the tumors. To evaluate the performance of the methods used for segmentation, the performance evaluation parameters such as Signal to noise Ratio(SNR) ,Mean Square Error (MSE) and Peak Signal Noise to Ratio (PSNR)) are computed on the segmented images of the two different segmentation methods used for segmentation. Better results are obtained for the K-Means segmentation irrespective of the images.
Check out a fantastic collection of super clear and sharp images featuring YS Jagan Mohan Reddy, the leader of Andhra Pradesh. These pictures capture his moments at rallies and more. You can easily download and share these top-quality HD images to stay connected with the leader you admire.
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Arterial spin labelling is an emerging non-invasive magnetic resonance imaging technique for estimating the cerebral perfusion without the requirement for gadolinium-based intravenous contrast agents. Despite the wide range of applications in epilepsy, dementia, brain tumours, vascular malformations and stroke imaging, obtaining clinically useful arterial spin labelling data is technically challenging and prone to numerous artefacts. The objective of this review is to provide a comprehensive pictorial overview of the various artefacts associated with arterial spin labelling, particularly three-dimensional fast spin echo pseudocontinuous arterial spin labelling with spiral readout. These artefacts could be broadly classified as those occurring during the magnetic labelling, arterial transit or image acquisition. Arterial spin labelling artefacts of clinical diagnostic utility are also elaborated. A thorough knowledge of the basis of these artefacts will avoid diagnostic pitfalls while interpreting arterial spin labelling images. Important tips to reduce or overcome these artefacts are also discussed.
Diabetic retinopathy (DR) has become the major cause of blindness for diabetic patients. This is because the microvascular consequence of diabetes mellitus results in DR, and treatment is successful only at the early stages. So, timely identification of DR is very important to minimize the risk of permanent vision loss. However, identifying and analyzing DR takes a long time and requires skilled ophthalmologists and radiologists. An automatic DR detection technique is needed in real-time applications to limit potential human errors. This paper proposes a hybrid bi-stage feature selection model for DR grading using the fundus images. Initially, the deep ensemble model extracts the efficient retinal features from preprocessed fundus images. Then, the proposed bi-stage feature selection method selects an optimal set of features to classify DR. In the first stage, two-filterbased feature selection techniques, namely Minimum Redundancy Maximum Relevance and Chi-squares, select the Guided features. In the second stage, the whale optimization algorithm reduces the feature space and selects more relevant and optimal features. The final optimal feature set is used for DR classification using support vector machines. The performance of the proposed model has been evaluated on the three publicly available databases, IDRiD, MESSIDOR-2, and Kaggle, and obtained an accuracy of 98.92%, a sensitivity of 99%,specificity of 99.69%, a precision of 98.8%, and F1-score of 0.988 with optimal features, which are better than other methods.
Optical imaging has attracted recent attention as a non-invasive medical imaging method in biomedical and clinical applications. In optical imaging, a light beam is transmitted through an under-test tissue by using an optical source. The beams which are gone through the tissue and/or reflected from the tissue surfaces are received by an array sensor. Based on the light intensity of these received beams on the sensor, sub-tissue maps are generated to scan large tissue areas so that any further biopsy is not required. Although the large tissue areas in pathological images can be scanned by using various methods, nonlinear deformations occur. To overcome this problem, the reconstruction process is frequently used. In this study, we propose an application of biomedical imaging based on performing the reconstruction of a phantom image via an in-line digital holography technique. Hence, many different sub-tissues can be imaged at the same time without the storage problem of the reconstructed image. To neglect the biopsy process required in medical imaging, the phantom image is obtained by using a linear array transducer for this study. We present the performance evaluation of the simulation results for the proposed technique by calculating the error metrics such as mean squared error (MSE), mean absolute error (MAE), and peak signal-to-noise ratio (PSNR). The obtained results reveal that the reconstructed images are well-matched to the original images, which are desired to be displayed by the holography technique.
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