Chapter 3 cont’d.

transcript

Chapter 3 cont’d.Binary Image Analysis

Binary image morphology

(nonlinear image processing)

CLOSING

Definition: closing BBABA

CC BABA

simply dilation followed by erosion when –B = B

comp((comp(A) opened by B))

APPLICATION TO MEDICAL IMAGES

APPLICATION TO GEAR INSPECTION (MISSING TEETH)

Extracting shape primitives

►translation invariance (E,D,O,C)

Morphological filter properties

xBABxA

►increasing (E,D,O,C)

BABAA 2121 A t h e n i f

►commutative (D)

►associative (D)

CBACBA

This property can lead to a more efficient implementation. How?

►antiextensive (O)

►extensive (C)

►idempotent (O,C)►“Idempotence is the property of certain

operations in mathematics and computer science, that they can be applied multiple times without changing the result beyond the initial application.” - wikipedia

►The origin is important for E and D, but not O and C.

BOUNDARIES

Boundary detection► Let B be a disk centered at the origin.

Then we can detect 3 types of boundaries:

1. External boundary

2. Internal boundary

3. Both (morphological gradient)

CHGHG :Recall

Boundary example

More examplesinput dilation

erosion external

internal morphologicalgradient

More examples

Dilation & distance maps/transforms

►Dilation can be used to create distance maps.

►Di is the set of all pixels at distance i from object.

►The operators denote the ith and i-1th applications of dilation.

BABAD iii 1

CONDITIONAL DILATION

Conditional dilation►(Two somewhat conflicting) problems:

1. Dilation may merge separate objects.

2. Erosion may remove parts of an otherwise complete object that we are trying to target.

Conditional dilation (Dougherty et al. method) addressing problem #1 (i.e., dilation may merge separate objects)► If B contains the origin, expands A

without bound. Recall dilation:

►So we define conditional dilation:

- the conditional dilation of A by B relative to C

AaCaBBA C

(hitting) | e)commutativ (since |

addition) (Minkowski |

AxBxAaaBBbbA

BABA C

Conditional dilation (Dougherty) cont’d.►But the previous definition may cause

dilation to join disconnected objects so another form may be used:

and | CaBAaaBBA

Conditional dilation (Dougherty) example

Conditional dilation (Shapiro and Stockman) addressing problem #2

►Recall problem #2: “We want the entire component(s), not just what remains of them after erosion.”

► “Given an original binary image B, a processed image C (e.g., C=B(-)V for some structuring element V), and a structuring element S, let

The conditional dilation of C by S w.r.t. B is defined bywhere the index m is the smallest index satisfying Cm = Cm-1.”

mB CSC |

. a nd 10 BSCCCC nn

Example of Shapiro and Stockman conditional dilation

Note: C is (typically) a processed version of B.

From previous slide:“Given an original binary image B, a processed image C, and a structuring element S, let

The conditional dilation of C by S w.r.t. B is defined by

where the index m is the smallest index satisfying Cm = Cm-1.”

Basically, we grow (dilate) object seeds in C until we completely recover the object in B corresponding to those seeds.

. a nd 10 BSCCCC nn

mB CSC |

Conditional dilation example► Below are the input image (left), C, and the result of

its dilation by an euclidean disk of diameter 7 (right), C0<i<m.

► Below are the conditional image (left), B, and the resultant image (right), Cm.

SKELETONIZATION

Skeletonization►a common thinning

procedure

►Medial Axis Transform (MAT)

►Connected sets can have disconnected skeletons!

Skeletonization

SkeletonizationDefinition: Given a Euclidean (continuous)

binary image, consider a point within an object. Next, consider that point to be the center of the largest disk centered at that point s.t. the edge of the disk touches the border of the object. Two possibilities:

1. This disk is contained in a larger disk (centered somewhere else)

or2. This disk is NOT contained in a larger disk.

If (2) holds, the disk is maximal and the center is on the medial axis of the object.

Skeletonization

Skeletonization►A and B are maximal; C is not.

Skeletonization

Skeletonizationexample

Note: I’m not sure why these three aren’t exactly the same!

Skeletonization example

Skeletonization►Define skeletal subset Skel(S; n) to be the

set of all pixels x in S s.t. x is the center of a maximal disk nB (the n-fold dilation) where n=0,1,… and

(n in nB is, in effect, the size.)►Define skeleton to be the union of all skeletal

subsets:

BBBnB ...

,...2,1,0,

;,...2,1,0),;(

nBnBSnBSSSkelBnBSnBSnSSkel

nnSSkelSSkel

Morphological operations on gray images

►Believe or not, these operations can be (have been) extended to gray image data!

►This ends our discussion of binary morphological operations.

► References:►E. Dougherty, “An Introduction to Morphological Image

Processing,” SPIE Press, 1992.►E. R. Dougherty and J. Astola, “An Introduction to Nonlinear

Image Processing,” SPIE Press, 1994.►C.R. Giardina and E.R. Dougherty, “Morphological Methods

in Image and Signal Processing,” Prentice Hall, 1988.►L.G. Shapiro and G.C. Stockman, “Computer Vision,”

Prentice Hall, 2001.

Chapter 3 cont’d.

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