Image segmentation

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Model of a segmented femur - journal.pone.0079004.g005

3D CT of thorax

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MRF neighborhood

Sample segmentation HMRF-EM

Image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels), often to simplify the representation of an image or to make it more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) 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.

Techniques

There are several techniques for image segmentation, including:

• Thresholding: This technique involves converting a grayscale image into a binary image based on a threshold value.
• Edge detection: This method detects the edges within an image, which are often indicative of object boundaries.
• Region growing: This technique starts with a seed point and grows regions by appending neighboring pixels that have similar properties.
• Clustering: Methods like k-means clustering and mean shift can be used to segment images by grouping pixels with similar features.
• Watershed algorithm: This technique treats the grayscale image as a topographic surface and finds the lines that separate different catchment basins.
• Graph-based segmentation: This method models the image as a graph and partitions the graph to segment the image.
• Deep learning: Techniques such as convolutional neural networks (CNNs) and fully convolutional networks (FCNs) are used for more advanced and accurate segmentation tasks.

Applications

Image segmentation has a wide range of applications, including:

• Medical imaging: Segmenting anatomical structures in MRI and CT images.
• Object detection: Identifying and locating objects within an image.
• Face recognition: Segmenting facial features for recognition systems.
• Autonomous vehicles: Segmenting road scenes to identify lanes, vehicles, and pedestrians.
• Satellite imagery: Analyzing land use and cover from satellite images.

Challenges

Some of the challenges in image segmentation include:

• Noise: Images often contain noise that can affect the accuracy of segmentation.
• Occlusion: Objects in images may be partially occluded, making segmentation difficult.
• Variability: Variability in object appearance, lighting conditions, and background can complicate segmentation tasks.
• Computational complexity: Some segmentation algorithms can be computationally intensive, especially for large images or real-time applications.

See also

• Image processing
• Computer vision
• Pattern recognition
• Machine learning
• Artificial intelligence

Related pages

• Thresholding (image processing)
• Edge detection
• Region growing
• Clustering
• Watershed algorithm
• Graph-based segmentation
• Deep learning
• Medical imaging
• Object detection
• Face recognition
• Autonomous vehicles
• Satellite imagery