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Everything about Image segmentation
Partitioning a digital image into segments
In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Image segmentationA 5-minute orientation
- Skim a recent paper: TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT ImagesInfluential work since 2022
- Pick up a classic book: Beginner's Guide to Multi-Level Image ThresholdingThe most-reprinted book on the subject
- Read the landmark paper: DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFsCited 18,996 times
- Go sideways: Rigid motion segmentationA closely related topic
- Go sideways: Range segmentationA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
- Applications
- Classes of segmentation techniques
- Groups of image segmentation
- Thresholding
- Clustering methods
- Motion and interactive segmentation
- Compression-based methods
- Histogram-based methods
- Edge detection
- Isolated Point Detection
- Application of Isolated Point Detection in X-ray Image Processing
- Dual clustering method
🏆 Landmark papers (most cited)
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Normalized cuts and image segmentation
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
Efficient Graph-Based Image Segmentation
Contour Detection and Hierarchical Image Segmentation
🆕 Influential since 2022
TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images
TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers
UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery
Medical Image Segmentation Review: The Success of U-Net
Medical image segmentation using deep learning: A survey
📚 Books worth reading
- 📕Beginner's Guide to Multi-Level Image ThresholdingNadaradjane Sri Madhava Raja, Nilanjan Dey · 2020
Digital image processingBernd Jähne · 1991
Variational and level set methods in image segmentationAmar Mitiche · 2010
Video segmentation and its applicationsKing Ngi Ngan · 2011
Anterior segment optical coherence tomographyRoger Steinert, David Huang · 2008
Variational methods in image segmentationJean-Michel Morel, Sergio Solimini · 1994
Computational analysis of visual motionAmar Mitiche · 1994
Graph based representations in pattern recognitionJean-Michel Jolion · 1998
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Rigid motion segmentation In computer vision, rigid motion segmentation is the process of separating regions, features, or trajectories from a video sequence into coherent subsets…
- Range segmentation Range segmentation is the task of segmenting (dividing) a range image, an image containing depth information for each pixel, into segments (regions), so…
- Foreground detection Foreground detection is one of the major tasks in the field of computer vision and image processing whose aim is to detect changes in image sequences.…
- Graph cuts in computer vision and artificial intelligence As applied in the field of computer vision, graph cut optimization can be employed to efficiently solve a wide variety of low-level computer vision problems…
- Minimum spanning tree-based segmentation Image segmentation strives to partition a digital image into regions of pixels with similar properties, e.g. homogeneity. The higher-level region representation…
- Watershed (image processing) In the study of image processing, a watershed is a transformation defined on a grayscale image. The name refers metaphorically to a geological watershed…
- Edge detection Edge detection includes a variety of mathematical methods that aim at identifying edges, defined as curves in a digital image at which the image brightness…
- Random walker algorithm The random walker algorithm is an algorithm for image segmentation. In the first description of the algorithm, a user interactively labels a small number…