IMAGE SEGMENTATION TECHNIQUES

IMAGE SEGMENTATION• Segmentation divides an image into its constituent regions or objects.

• Image segmentation means assigning a label to each pixel in the image such that pixels with same labels share common visual characteristics[1].

• It makes an image easier to analyze in the image processing tasks.

• Segmentation of images is a difficult task in image processing. Still under research.

• Segmentation allows to extract objects in images.

• Segmentation is unsupervised learning.

• Model based object extraction, e.g., template matching, is supervised learning.

SEGMENTATION TECHNIQUE, BASICALLY CONVERT THE COMPLEX IMAGE INTO THE SIMPLE IMAGE.

IMAGE(COMPLEX)

SEGMENTATION TECHNIQUE

SEGMENTATION TECHNIQUE(SIMPLE)

WHAT IT IS USEFUL FOR• After a successful segmenting the image, the contours of

objects can be extracted using edge detection and/or border following techniques.

• Shape of objects can be described.• Based on shape, texture, and color objects can be identified.• Image segmentation techniques are extensively used in

similarity searches.

CATEGORIES OF SEGMENTATION TECHNIQUES:

SEGMENTATON TECHNIQUES

EDGE-BASE SEGMENTATION REGION-BASED SEGMENTATION

Detecting Discontinuity It means to partition an image based on abrupt changes in intensity [1], this includes image segmentation algorithms like edge detection.

Detecting Similarity It means to partition an image into regions that are similar according to a set of predefined criterion this includes image segmentation algorithms like thresholding, region growing.

EDGE-BASED SEGMENTATIONSegmentation Methods based on Discontinuity find for abrupt changes in the intensity value. These methods are called as Edge or Boundary based methods. Edge detection is the problem of fundamental importance in image analysis. Edge detection techniques are generally used for finding discontinuities in gray level images. Edge detection is the most common approach for detecting meaningful discontinuities in the gray level. Image segmentation methods for detecting discontinuities are boundary based methods. Edge detection can be done using either of the following methods. Edges are local changes in the image intensity. Edges typically occur on the boundary between two regions[2]. Important features can be extracted from the edges of an image (e. g., corners, lines, curves). Edge detection is an important feature for image analysis.

REGION-BASED SEGMENTATION• Region based methods are based continuity. These techniques divide the

entire image into sub regions depending on some rules like all the pixels in one region must have the same gray level.

• Region-based techniques rely on common patterns in intensity values within a cluster of neighboring pixels. The cluster is referred to as the region, and the goal of the segmentation algorithm is to group the regions according to their anatomical or functional roles. Compared to edge detection method, segmentation algorithms based on region are relatively simple and more immune to noise [3, 4]. Edge based methods partition an image based on rapid changes in intensity near edges whereas region based methods, partition an image into regions that are similar according to a set of predefined criteria [5, 1].

SEGMENTATION ALGORITHMS BASED ON REGION MAINLY INCLUDE FOLLOWING METHODS:

Region GrowingRegion growing is a procedure that group’s pixels in whole image

into sub regions or larger regions based on predefined criterion [7]Region Splitting and Merging

Rather than choosing seed points, user can divide an image into a set of arbitrary unconnected regions and then merge the regions [2, 4] in an attempt to satisfy the conditions of reasonable image segmentation. Region splitting and merging is usually implemented with theory based on quad tree data.

WATERSHED TRANSFORMATION

Watershed Transformation belongs to the category of the region based similarities. Watershed model is a mathematical morphological approach and derives its analogy from a real life flood situation [8]. It transforms image into a gradient image. Then, image is seen as a topographical surface where grey values are deemed as elevation of the surface at that location. Then, flooding process starts in which water effuses out of the minimum grey value. When flooding across two minimum converges then a dam is built to identify the boundary across them. This method is essentially an edge based technique .

REFERENCES[1] Rafael C. Gonzalez, Richard E. Woods, “Digital Image Processing”, 2nd ed., Beijing: Publishing House of Electronics Industry, 2007.[2] Nikita Sharma, Mahendra Mishra, Manish Shrivastava, “ COLOUR IMAGE SEGMENTATION TECHNIQUES AND ISSUES: AN APPROACH”, International[3] Journal of Scientific & Tech. Research Volume 1, Issue 4, May 2012 ISSN 2277-8616.[4] W. X. Kang, Q. Q. Yang, R. R. Liang, “The Comparative Research on Image Segmentation Algorithms”, IEEE Conference on ETCS, pp. 703-707, 2009.[5] H. Zhang, J. E. Fritts, S. A. Goldman, “Image Segmentation Evaluation: A Survey of unsupervised methods”, computer vision and image understanding, pp. 260-280, 2008.[6] H. G. Kaganami, Z. Beij, “Region Based Detection versus Edge Detection”, IEEE Transactions on Intelligent information hiding and multimedia signal processing, pp. 1217- 1221, 2009.[7] K. K. Singh, A. Singh, “A Study of Image Segmentation Algorithms for Different Types of Images”, International Journal of Computer Science Issues, Vol. 7, Issue 5, 2010.[8] Y. Chang, X. Li, “Adaptive Image Region Growing”, IEEE Trans. On Image Processing, Vol. 3, No. 6, 1994.

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