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Segmentation
- 1. 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
- 2. What is Image Segmentation Image segmentation reduces pixel data to region-based information segmentation of an image which classifies voxels/pixels into objects or groups Form of segmenting the foreground from background simpliest case: thresholding gray-scale pixel values
- 3. 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.
- 4. 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.
- 5. 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
- 6. 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 identify CONS: 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
- 7. 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.
- 8. 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 R i stop when all regions are homogeneous: P(R i ) = TRUE ) obtain quadtree structure Bottom-up at each level, merge adjacent regions R i and R j if P(R i [ R j ) = TRUE Iterate until no further splitting/merging is possible
- 9. EXAMPLE
- 10. The Split-and-Merge Algorithm Sample image First split 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 0 3 1 4 9 9 8 1 0 1 1 8 8 8 4 1 0 1 1 6 6 6 3 1 0 1 1 5 6 6 3 1 0 1 1 5 6 6 2 1 0 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 0 3 1 4 9 9 8 1 0 1 1 8 8 8 4 1 0 1 1 6 6 6 3 1 0 1 1 5 6 6 3 1 0 1 1 5 6 6 2 1 0 1 1 1 1 1 1 0 0
- 11. Second split Third split 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 0 3 1 4 9 9 8 1 0 1 1 8 8 8 4 1 0 1 1 6 6 6 3 1 0 1 1 5 6 6 3 1 0 1 1 5 6 6 2 1 0 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 0 3 1 4 9 9 8 1 0 1 1 8 8 8 4 1 0 1 1 6 6 6 3 1 0 1 1 5 6 6 3 1 0 1 1 5 6 6 2 1 0 1 1 1 1 1 1 0 0
- 12. Merge Final result 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 0 3 1 4 9 9 8 1 0 1 1 8 8 8 4 1 0 1 1 6 6 6 3 1 0 1 1 5 6 6 3 1 0 1 1 5 6 6 2 1 0 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 0 3 1 4 9 9 8 1 0 1 1 8 8 8 4 1 0 1 1 6 6 6 3 1 0 1 1 5 6 6 3 1 0 1 1 5 6 6 2 1 0 1 1 1 1 1 1 0 0
- 13. CONTOUR TRACING It is a technique that is applied to digital images in order to extract their boundary To trace the contour of a given pattern
- 14. CONTOUR TRACING TECHNIQUE Palvidi’s algorithm
- 16. Resources: http://www.pages.drexel.edu/~weg22/edge.html http://iria.pku.edu.cn/~jiangm/courses/dip/html/node138.html http://en.wikipedia.org/wiki/Segmentation_(image_processing)
Editor's Notes
- The result of image segmentation is a set of regions that collectively cover the entire image, or a set of contours extracted from the image (see edge detection ). 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(s)