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A Review of Automatic Methods Based on Image Processing Techniques for Tuberculosis Detection from Microscopic Sputum Smear Images.

, , , and . J. Medical Syst., 40 (1): 17:1-17:13 (2016)

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Magnetic resonance image denoising using nonlocal maximum likelihood paradigm in DCT-framework., , , , , , and . Int. J. Imaging Systems and Technology, 25 (3): 256-264 (2015)Automated segmentation of intra-retinal cysts from optical coherence tomography scans using marker controlled watershed transform., , and . EMBC, page 1292-1295. IEEE, (2016)Medical Image Segmentation using 3D Convolutional Neural Networks: A Review., , , and . CoRR, (2021)Segmentation of focal cortical dysplasia lesions from magnetic resonance images using 3D convolutional neural networks., , , , , , and . Biomed. Signal Process. Control., (2021)Automatic detection and localization of Focal Cortical Dysplasia lesions in MRI using fully convolutional neural network., , , , , and . Biomed. Signal Process. Control., (2019)A new non-local maximum likelihood estimation method for Rician noise reduction in magnetic resonance images using the Kolmogorov-Smirnov test., , and . Signal Process., (2014)An empirical study of the impact of masks on face recognition., , , and . Pattern Recognit., (2022)Non-Local Means Image Denoising Using Shapiro-Wilk Similarity Measure., , , and . IEEE Access, (2018)Multi-Res-Attention UNet: A CNN Model for the Segmentation of Focal Cortical Dysplasia Lesions from Magnetic Resonance Images., , , , , , , and . IEEE J. Biomed. Health Informatics, 25 (5): 1724-1734 (2021)Cross Task Temporal Consistency for Semi-supervised Medical Image Segmentation., , and . MLMI@MICCAI, volume 13583 of Lecture Notes in Computer Science, page 140-150. Springer, (2022)