scholarly journals Effective Super-Resolution Methods for Paired Electron Microscopic Images

2020 ◽  
Vol 29 ◽  
pp. 7317-7330
Author(s):  
Yanjun Qian ◽  
Jiaxi Xu ◽  
Lawrence F. Drummy ◽  
Yu Ding
Keyword(s):  
Super Resolution ◽  
Electron Microscopic ◽  
Microscopic Images ◽  
Paired Electron

An Efficient Adaptive Algorithm for Electron Microscopic Image Enhancement and Feature Extraction

2019 ◽  
Vol 9 (1) ◽  
pp. 1-16
Author(s):  
Vivek Arya ◽  
Vipul Sharma ◽  
Garima Arya
Keyword(s):  
Feature Extraction ◽  
Contrast Enhancement ◽  
Block Size ◽  
Sigmoid Function ◽  
Microscopic Image ◽  
Electron Microscopic ◽  
Microscopic Images ◽  
Electron Microscopic Image ◽  
Block Based

In this article, a block-based adaptive contrast enhancement algorithm has been proposed, which uses a modified sigmoid function for the enhancement and features extraction of electron microscopic images. The algorithm is based on a modified sigmoid function that adapts according to the input microscopic image statistics. For feature extraction, the contrast of the image is very important and authentic property by which this article enhances the visual quality of the image. In this work, for better contrast enhancement of image, a block based on input value, combined with a modified sigmoid function that is used as contrast enhancer provides better EMF values for a smaller block size. It provides localized contrast enhancement effects adaptively which is not possible using other existing techniques. Simulation and experimental results demonstrate that the proposed technique gives better results compared to other existing techniques when applied to electron microscopic images. After the enhancement of microscopic images of actinomycetes, various important features are shown, like coil or spiral, long filament, spore and rod shape structures. The proposed algorithm works efficiently for different dark and bright microscopic images.

scholarly journals Monitoring and Determining Mitochondrial Network Parameters in Live Lung Cancer Cells

2019 ◽  
Vol 8 (10) ◽  
pp. 1723
Author(s):  
Tamara Mirzapoiazova ◽  
Haiqing Li ◽  
Anusha Nathan ◽  
Saumya Srivstava ◽  
Mohd W. Nasser ◽  
...  
Keyword(s):  
Lung Cancer ◽  
3D Reconstruction ◽  
Cellular Response ◽  
Mitochondrial Dynamics ◽  
Confocal Imaging ◽  
Live Cells ◽  
Mitochondrial Morphology ◽  
Electron Microscopic ◽  
Microscopic Images ◽  
Network Parameters

Mitochondria are dynamic organelles that constantly fuse and divide, forming dynamic tubular networks. Abnormalities in mitochondrial dynamics and morphology are linked to diverse pathological states, including cancer. Thus, alterations in mitochondrial parameters could indicate early events of disease manifestation or progression. However, finding reliable and quantitative tools for monitoring mitochondria and determining the network parameters, particularly in live cells, has proven challenging. Here, we present a 2D confocal imaging-based approach that combines automatic mitochondrial morphology and dynamics analysis with fractal analysis in live small cell lung cancer (SCLC) cells. We chose SCLC cells as a test case since they typically have very little cytoplasm, but an abundance of smaller mitochondria compared to many of the commonly used cell types. The 2D confocal images provide a robust approach to quantitatively measure mitochondrial dynamics and morphology in live cells. Furthermore, we performed 3D reconstruction of electron microscopic images and show that the 3D reconstruction of the electron microscopic images complements this approach to yield better resolution. The data also suggest that the parameters of mitochondrial dynamics and fractal dimensions are sensitive indicators of cellular response to subtle perturbations, and hence, may serve as potential markers of drug response in lung cancer.

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