Quantitative analysis of spatial and temporal concurrent responses of multiple markers in 3-dimensional cell cultures is hampered by the routine mode of sequential image acquisition, measurement and analysis of specific targets. A system was developed for detailed analysis of multi-dimensional, time-sequence responses and in order to relate features in novel and meaningful ways that will further our understanding of basic biology. Optical sectioning of the 3-dimensional structures is achieved with structured light illumination using the Wilson grating as described by Lanni. The automated microscopy system can image multicellular structures and track dynamic events, and is equipped for simultaneous/ sequential imaging of multiple fluorescent markers. Computer-controlled perfusion of external stimuli into the culture system allows (i) real-time observations of multiple cellular responses and (ii) automatic and intelligent adjustment of experimental parameters. This creates a feedback loop in real-time that directs desired responses in a given experiment. On-line image analysis routines provide cell-by-cell measurement results through segmentation and feature extraction (i.e. intensity, localization, etc.), and quantitation of meta-features such as dynamic responses of cells or correlations between different cells. Off-line image and data analysis is used to derive models of the processes involved, which will deepen the understanding of the basic biology.
The authors present a Hough transform based image segmentation algorithm for automated detection, counting, and measurement of particles in two- and three-dimensional microscopic digital image data sets. The algorithm has proven to be both sensitive and specific for the particles of interest, even in the presence of noise and blurring. We apply the algorithm to the problem of automated compilation of population statistics on the size and mass of zymogen granules in pancreatic acinar cells. We present results describing the performance of the algorithm on digital phantoms, and image data from conventional fluorescence and confocal microscopes.
Insect infestation increases the probability of aflatoxin contamination in pistachio nuts. A non- destructive test is currently not available to determine the insect content of pistachio nuts. This paper uses film X-ray images of various types of pistachio nuts to assess the possibility of machine recognition of insect infested nuts. Histogram parameters of four derived images are used in discriminant functions to select insect infested nuts from specific processing streams.
Pinta is a system for segmentation and visualization of anatomical structures obtained from serial sections reconstructed from Magnetic Resonance Imaging. The system approaches the segmentation problem by assigning each volumetric region to an anatomical structure. This is accomplished by satisfying constraints at the pixel level, slice level, and volumetric level. Each slice is represented by an attributed graph, where nodes correspond to regions and links correspond to the relations between regions. Next, the slice level attributed graphs are coerced to form a volumetric attributed graph, where volumetric consistency can be verified. The main novelty of our approach is in the use of the volumetric graph to ensure consistency from symbolic representations obtained from individual slices. In this fashion, the system allows errors to be made at the slice level, yet removes them when the volumetric consistency can not be verified. Once the segmentation is complete, surfaces of the 3D brain structures can be constructed and visualized. We present results obtained from real data and examine the performance of our system.
Conference Committee Involvement (7)
Digital and Computational Pathology
15 February 2021 | Online Only, California, United States
19 February 2020 | Houston, Texas, United States
20 February 2019 | San Diego, California, United States
11 February 2018 | Houston, Texas, United States
12 February 2017 | Orlando, Florida, United States
Digital Pathology Posters
12 February 2017 | Orlando, FL, United States
2 March 2016 | San Diego, California, United States