Due to the optical principle and some imperfections of the conversion process, phase contrast microscopy images contain artifacts such as the bright halo surrounding the specimen and shade-off (the intensity profile of a large specimen gradually increases from the edges to the center, and even approaches the intensity of the surrounding background medium). Over time, biologists have learned how to overcome or even exploit those artifacts for interpreting phase contrast images. When computer-based microscopy image analysis began to relieve humans from tedious manual labelling, those artifacts presented significant challenges to automated image processing. In particular, they hinder the process of segmenting images into cells and background, which is the most critical step in almost all cell image analysis applications.
A user-friendly opensource software tool for tracking cells imaged with various imaging modalities, including fluorescent, phase contrast, and differential interference contrast (DIC) techniques. The main goals of the software tool are: (i) automated image quality enhancement using vignetting and alignment correction, (ii) detection and tracking of cells, (iii) editing cell paths and statistical analysis of the cell motion.
Increases the local contrast of an image. CLAHE uses the contrast limited adaptive histogram equalization to process. The contrast amplification in the vicinity of a given pixel value is delivered by the slope of the transformation function. This ImageJ plugin has three main parameters: block size, histogram and max slope.
Serves for effective segmentation of multidimensional datasets. MIB can recognize several number of imaging formats and offers a variety of image processing tools. It also simplifies utilization and quantification of acquired data. It permits users to segment large datasets, to realize 3D visualization, and to quantify images and models. Its parameters enable users to insert plugin s to customize the program for specific needs.
Serves for iterative image deblurring. PID allows users to deconvolve a color image by splitting the channels and deblur each channel separately. It contains a graphical user interface (GUI) permitting users to specify several types of details such as stopping tolerance, threshold, or log convergence.
Balances jittery image stacks. Image Stabilizer is based on the Lucas-Kanade algorithm. It can be applied to grayscale and color images. This tool can determine the geometrical transformation needed to best align each of the other slices with the "template".
Allows to visualize and present your images in several dimensions. The functionality of this imaging toolbox expands constantly with a wide range of different modules that are tailored to specific applications or microscope accessories. AxioVision offers countless functions for applications in the field of biological and medical routine research.
Finds dynamic changes in pixel intensity between image frames. MUSCLEMOTION expresses the output as a relative measure of movement during muscle contraction and relaxation. It can be employed for multiparameter recording conditions and experimental settings using transmitted light microscopy, fluorescent membrane labeling, fluorescent beads embedded in soft substrates or patch clamp video recordings.