Image Segmentation: • Segmentation refers to the process of partitioning a digital image into multiple regions (sets of pixels). • The goal of segmentation is to simplify 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 • Each of the pixels in a region are similar with respect to some characteristic or computed property, such as color, intensity, or texture. • Adjacent regions are significantly different with respect to the same characteristic • Some applications of image segmentation in medical field includes: – Locate tumors and other pathologies, – Measure tissue volumes, – Computer-guided surgery
Transcript
Image Segmentation:• Segmentation refers to the process of partitioning a digital image into
multiple regions (sets of pixels).• The goal of segmentation is to simplify 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• Each of the pixels in a region are similar with respect to some characteristic
or computed property, such as color, intensity, or texture. • Adjacent regions are significantly different with respect to the same
characteristic• Some applications of image segmentation in medical field includes:
– Locate tumors and other pathologies, – Measure tissue volumes, – Computer-guided surgery
Edge Detection:• Edges in images are areas with strong intensity contrasts –
a jump in intensity from one pixel to the next. • Edge detecting an image significantly reduces the amount
of data and filters out useless information, while preserving the important structural properties in an image.
• There are many ways to perform edge detection.– Gradient - The gradient method detects the edges by looking for
the maximum and minimum in the first derivative of the image. – Laplacian - The Laplacian method searches for zero crossings in
the second derivative of the image to find edges.
Determining Intensity Values for Threshold
Thresholding separate foreground pixels from background pixels and can be performed before or after applying a morphological operation to an image. While a threshold operation produces a binary image and rely upon the definition of an intensity value.
This intensity value is compared to each pixel value within the image and an output pixel is generated based upon the conditions stated within the threshold.
Intensity histograms provide a means of determining useful intensity values as well as determining whether or not an image is a good candidate for thresholding or stretching.
Intensity histogram based segmentation
REGION GROWING• Group pixels or sub-regions into
larger regions when homogeneity criterion is satisfied
• Region grows around the seed point based on similar properties (grey level, texture, color)
PROS:• Better in noisy image where
edges are hard to identifyCONS:• Seed point must be specified• Different seed point will give
different results
PIXEL AGGREGATION:
Homogeneity criteria:• The difference between 2 pixel
values is less than or equal to 5• Horizontal, vertical, diagonal
10 10 10 10 10 10 10
10 10 10 69 70 10 10
59 10 60 64 59 56 60
10 59 10 60 70 10 62
10 60 59 65 67 10 65
10 10 10 10 10 10 10
10 10 10 10 10 10 10
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Region-Oriented Segmentation Region Splitting
Region growing starts from a set of seed points. An alternative is to start with the whole image as a single region and
subdivide the regions that do not satisfy a condition of homogeneity. Region Merging
Region merging is the opposite of region splitting. Start with small regions (e.g. 2x2 or 4x4 regions) and merge the
regions that have similar characteristics (such as gray level, variance). Typically, splitting and merging approaches are used iteratively.
Split and Merge Approach:• This is a 2 step procedure:
– top-down: split image into homogeneous quadrant regions
– bottom-up: merge similar adjacent regions
• The algorithm includes:Top-down– successively subdivide image into
quadrant regions Ri
– stop when all regions are homogeneous: P(Ri ) = TRUE) obtain quadtree structure
Bottom-up– at each level, merge adjacent regions
Ri and Rj if P(Ri [ Rj ) = TRUE• Iterate until no further
splitting/merging is possible
CONTOUR TRACING• It is a technique that is
applied to digital images in order to extract their boundary
