Morphological Image Processing The word morphology refers to the scientific branch that deals the forms and structures of animals/plants. Morphology in image processing is a tool for extracting image components that are useful in the representation and description of region shape, such as boundaries and skeletons. Furthermore, the morphological operations can be used for filtering, thinning and pruning. The language of the Morphology comes from the set theory, where image objects can be represented by sets. For example an image object containing black pixels can be considered a set of black pixels in 2D space of Z2.

Morphological Image Processing Set Theory Fundamentals Given that A is a set in Z2and a=(a1,a2), then a is an element in A: a is not an element in A: given sets A and B, A is said to be the subset of B: The union of A and B is denoted by: The intersection of A and B is denoted by: Two sets are disjoint/mutually exclusive if The complement of set A is the set of elements not contained in A, The difference of two sets:

Morphological Image Processing Set Theory Fundamentals Given 2 sets A and B

Morphological Image Processing Set Theory Fundamentals The reflection of set B is defined by: The translation of set A by point z=(z1,z2) is defined by: Translation of A by z. Reflection of B

Morphological Image Processing Logic operation involving Binary Images Given 1-bit binary images, A and B, the basic logical operations are illustrated: Note that the black indicates binary 1 and white indicates binary 0 here.

Morphological Image Processing Dilation and Erosion Dilation and erosion are the two fundamental operations used in morphological image processing. Almost all morphological algorithms depend on these two operations: Dilation: Given A and B sets in Z2, the dilation of A by B, is defined by: ^ The dilation of A and B is a set of all displacements, z , such that B and A overlap by at least one element. The definition can also be written as: Set B is referred to as the structuring element and used in dilation as well as in other morphological operations. Dilation expands/dilutes a given image.

Shaded area is the dilation of A by B Morphological Image Processing Dilation and Erosion Dilation: Given the structuring element B and set A. Shaded area is the dilation of A by B origin The structuring element B enlarges the size of A at its boundaries. Dilation simply expands a given image. The structuring element B enlarges the size of A at its boundaries, in relation to the distance from the origin of the structuring element .

Morphological Image Processing Dilation and Erosion Dilation: Given the following distorted text image where the maximum length of the broken characters are 2 pixels. The image can be enhanced by bridging the gaps by using the structuring element given below: 3x3 structuring element Note that the broken characters are joined.

Shaded area is the erosion of A by B Morphological Image Processing Dilation and Erosion Erosion: Given A and B sets in Z2, the erosion of A by structuring element B, is defined by: The erosion of A by structuring element B is the set of all points z, such that B, translated by z, is contained in A. Shaded area is the erosion of A by B structuring element Note that in erosion the structuring element B erodes the input image A at its boundaries. Erosion shrinks a given image.

Shaded line is what is left from the erosion of A by B Morphological Image Processing Dilation and Erosion Erosion: Given the structuring element B and set A. Shaded line is what is left from the erosion of A by B structuring element

Morphological Image Processing Dilation and Erosion Erosion: Given the following binary image with squares on size 1,3,5,7,9 and 15. You can get rid of all the squares less than size of 15 by erosion followed by dilation of a structuring element of 13x13. 13x13 structuring element Erosion of A by B

Morphological Image Processing Dilation and Erosion Dilation: Cont. from the previous slide. Note that erosion followed by dilation helps to perform filtering. 13x13 structuring element dilation by B

Morphological Image Processing Opening and closing Opening: The process of erosion followed by dilation is called opening. It has the effect of eliminating small and thin objects, breaking the objects at thin points and smoothing the boundaries/contours of the objects. Given set A and the structuring element B. Opening of A by structuring element B is defined by: Closing: The process of dilation followed by erosion is called closing. It has the effect of filling small and thin holes, connecting nearby objects and smoothing the boundaries/contours of the objects. Given set A and the structuring element B. Closing of A by structuring element B is defined by:

Morphological Image Processing Opening and closing Opening: The opening of A by the structuring element B is obtained by taking the union of all translates of B that fit into A. The opening operation can also be expressed by the following formula: Outer boundary of A Origin of B Circular structuring element Shaded area: complete opening Possible translations of B in A

Morphological Image Processing Opening and closing Closing: The closing has a similar geometric interpretation except that we roll B on the outside of the boundary. The opening operation can also be expressed by the following formula: Outer boundary of A Outer boundaries of closing Shaded area: complete closing Possible translations of B on the outer boundaries of A

Morphological Image Processing Opening and closing B circular structuring element result of erosion of A by B result of opening of A by B result of dilation of A by B result of closing of A by B

3x3 square structuring element result of opening followed by closing Morphological Image Processing Opening and closing Noise Filtering: The morphological operations can be used to remove the noise as in the following example: 3x3 square structuring element result of opening followed by closing Note that impulsive noise within the background and the fingerprints is removed. after opening

Morphological Image Processing Hit-or-Miss Transform (Template Matching) Hit-or-miss transform can be used for shape detection/ Template matching. Given the shape as the structuring element B1 the Hit-or-miss transform is defined by: * Where B2 =W-X and B1=X. W is the window enclosing B1. Windowing is used to isolate the structuring element/object. B2=W-X, used as the second structuring element. Complement of B1 Shape that we are searching for Used as the structuring element (B1=X)

The location of the matched object/shape, Morphological Image Processing Hit-or-Miss Transform * B2 B1 Complement of A Erosion of A by B1 Erosion of comp of A by B2 The location of the matched object/shape,

Morphological Image Processing Basic Morphological Algorithms Boundary Extraction: The boundaries/edges of a region/shape can be extracted by first applying erosion on A by B and subtracting the eroded A from A. Ex 1: 3x3 Square structuring element is used for boundary extraction. Ex 2: The same structuring element in Ex1 is used. Note that thicker boundaries can be obtained by increasing the size of structuring element.

Morphological Image Processing Basic Morphological Algorithms Region Filling: Region filling can be performed by using the following definition. Given a symmetric structuring element B, one of the non-boundary pixels (Xk) is consecutively diluted and its intersection with the complement of A is taken as follows: Following consecutive dilations and their intersection with the complement of A, finally resulting set is the filled inner boundary region and its union with A gives the filled region F(A).

This region is filled first. Filling of all the other regions Morphological Image Processing Basic Morphological Algorithms Region Filling: This region is filled first. Filling of all the other regions A non-boundary pixel Ex 1: X0=1 (Assume that the shaded boundary points are 1 and the white pixels are 0)

Morphological Image Processing Basic Morphological Algorithms Connected Component Extraction: The following iterative expression can be used to determine all the pixels in component Y which is in A. X0=1 corresponds to one of the pixels on the component Y. Note that one of the pixel locations on the component must be known. Consecutive dilations and their intersection with A, yields all elements of component Y. Result of first iteration Known pixel, p Result of second iteration Result of last iteration

Morphological Image Processing Basic Morphological Algorithms Connected Component Extraction: Input image (Chicken fillet) Thresholded image 15 connected components with different number of pixels After erosion by 5x5 square structuring element of 1’s

Morphological Image Processing Basic Morphological Algorithms Thinning: Thinning of A by the structuring element B is defined by: * hit-or-miss transform/template matching Note that we are only interested in pattern matching of B in A, so no background operation is required of the hit-miss-transform. The structuring element B consists of a sequence of structuring elements, where Bi is the rotated version of Bi-1. Each structuring elements helps thinning in one direction. If there are 4 structuring elements thinning is performed from 4 directions separated by 90o. If 8 structuring elements are used the thinning is performed in 8 directions separated by 45o. The process is to thin A by one pass with B1, then the result with one pass of B2, and continue until A is thinned with one pass of Bn.

... Morphological Image Processing Basic Morphological Algorithms Thinning: The following set of structuring elements are used for thinning operation. ... If there is no change any more. Declared to be the thinned object