Some trends in microscope image processing

Spatial distribution of relaxation behavior on the surface of a ferroelectric relaxor in the ergodic phase

“…[18][19][20] The spectroscopic image of N ϫ M pixels formed by spectra containing P points is represented as a superposition of the eigenvectors w j ,…”

A Guided Tour of Selected Image Processing and Analysis Methods for Fluorescence and Electron Microscopy

“…In recent years, image analysis in microscopy has gained importance and the number of image processing and image analysis papers submitted to dedicated journals [2] in this area but also to more generalist magazines, workshops and conferences including in microscopy and bioinformatics, increased significantly in the last decade. The IEEE International Symposium on Biomedical Imaging (ISBI) focuses typically on microscopy image processing and analysis methods and cutting-edge algorithms.…”

“…Moreover, related books [4], [5] and special issues [6]- [9] include a few tutorial-style overview articles covering progress in recent years for a large variety of topics (e.g., tracking in fluorescence bioimaging [10]- [12], sub-diffraction limited imaging and single molecule localization [13], [14], parametric active contour-based image segmentation [15] ). Finally, several authors presented independently state of the art methods for specific and important topics including cell-shape analysis [16], neuron tracing [17], co-localization (percentage of co-detection of interacting protein types at the same location) [18], [19], 3D image deconvolution [20], spot detection [21] in fluorescence microscopy Even if it is generally a difficult task to present a broad view of activities in bio-image processing and analysis [2], several authors [22]- [25] already explained successfully how computer vision, image analysis and visualization algorithms combined in workflows, will play a significant role in image-based studies of cell biology.…”

Double-Layer Mediated Electromechanical Response of Amyloid Fibrils in Liquid Environment

“…As is usual for imaging in liquids, the response in the 50–300 kHz range contains multiple response peaks related to both intrinsic materials responses and non-idealities in the cantilever and holder transfer functions. To avoid the uncertainty in data interpretation and establish the veracity of the fitting procedure, we analyze spectroscopic BE data using principal component analysis (PCA) 56,57,58. The spectroscopic image of N × M pixels formed by spectra containing P points is represented as a superposition of the eigenvectors w j , $${\text{italicPR}}_{i}true({\omega }_{j}true)=a_{\text{italicik}}{w}_{k}true({\omega }_{j}true),$$ where a ik ≡ a k ( x , y ) are position-dependent expansion coefficients, PR i ( t j ) ≡ PR ( x , y ,ω j ) is the image at a selected time, and ω j are the discrete frequencies at which response is measured.…”