Image Processing Analysis
Image Processing Analysis - Related Videos
11:24
High-throughput Imaging and Analysis Workflow for Evaluating Skin Cell Phenotypes and Proliferation States in Tissue Samples
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Cited by 1 •
2025
The combination of iterative-bleaching-extends-multiplexity (IBEX) and a commercial nucleotide labeling assay (Click-iT EdU) enables the detection and categorization of dividing cell types in highly dynamic processes in fixed frozen murine tissue sections. Furthermore, a novel open-source image processing pipeline provides high-throughput image acquisition and analysis.
10:09
EasyFiji: A Graphical Interface for User-Friendly Fluorescence Image Processing in Fiji
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2026
EasyFiji is a graphical user interface plugin for Fiji (ImageJ) that provides a curated suite of fluorescence image visualization and processing tools frequently utilized by life scientists.
11:41
Rapid Analysis and Exploration of Fluorescence Microscopy Images
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Cited by 3 •
2014
Here we describe a workflow for rapidly analyzing and exploring collections of fluorescence microscopy images using PhenoRipper, a recently developed image-analysis platform.
13:45
Hybrid µCT-FMT imaging and image analysis
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Cited by 34 •
2015
We describe a protocol for hybrid imaging, combining fluorescence-mediated tomography (FMT) with micro computed tomography (µCT). After fusion and reconstruction, we perform interactive organ segmentation to extract quantitative measurements of the fluorescence distribution.
09:47
Isolation, Processing and Analysis of Murine Gingival Cells
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Cited by 56 •
2013
This study describes an efficient technique to isolate and process gingival tissues from the mouse oral cavity in order to produce a single-cell culture. The resulting cells can be further used for flow cytometry analysis and molecular studies.
FAQs
Preprocessing improves subsequent measurements by reducing image noise before segmentation and feature extraction. Noise can obscure boundaries or distort intensity patterns, making tumor or cell regions harder to distinguish from surrounding tissue. In a cancer study, this ordering supports more consistent estimates of size, shape, intensity, and spatial organization across images and helps make large-dataset analysis more reproducible.
Segmentation determines which pixels or image regions are treated as tumors, cells, or surrounding tissue. That boundary matters because measurements taken afterward depend on what has been included or excluded. Reliable separation therefore allows researchers to compare tumor morphology and cellular behavior quantitatively, rather than interpreting measurements that mix the biological target with adjacent tissue.
Feature extraction converts segmented image content into specific measurements, including size, shape, intensity, and spatial organization. Different features describe different aspects of cancer-related structure: morphology captures form, intensity captures image signal, and spatial organization captures arrangement. Together, these measurements support analysis of tumor morphology, cellular behavior, treatment response, and disease progression.
Spatial organization adds information about how tumors or cells are arranged relative to one another or surrounding tissue. This complements measurements of size, shape, and intensity, which may not capture distribution alone. In cancer research, the resulting patterns can contribute to examining cellular behavior, tumor morphology, and changes associated with disease progression.
A typical workflow starts with preprocessing to reduce noise, continues with segmentation to isolate tumors or cells, and then applies feature extraction to quantify image characteristics. Researchers can analyze those measurements across microscopy, histopathology, or medical imaging datasets. Keeping these stages distinct clarifies how visual data become comparable numerical information for cancer studies.
The same analytical framework can be applied to microscopy, histopathology, and medical imaging, but the biological targets and image context differ. Microscopy may support examination of cells, histopathology may support tumor or tissue assessment, and medical imaging may support broader tumor evaluation. In each case, preprocessing, segmentation, and feature extraction organize visual information for measurement.
Quantitative image measurements can support biomarker development, disease classification, and evaluation of treatment response or disease progression. Their value comes from turning characteristics such as size, shape, intensity, and spatial organization into data that can be compared across images. In cancer research, this supports objective decisions and analysis of large datasets with improved reproducibility.