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Unicity in discrete tomography Rob Tijdeman, Leiden, 12 February 2015 2013

Unicity in discrete tomography - Lorentz Center Tijdeman.pdf · 1. Introduction Discrete tomography if few materials which have lattice structure few grey values (few pixel values)

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Page 1: Unicity in discrete tomography - Lorentz Center Tijdeman.pdf · 1. Introduction Discrete tomography if few materials which have lattice structure few grey values (few pixel values)

Unicity in discrete tomography

Rob Tijdeman, Leiden, 12 February 2015 2013

Page 2: Unicity in discrete tomography - Lorentz Center Tijdeman.pdf · 1. Introduction Discrete tomography if few materials which have lattice structure few grey values (few pixel values)

1. Introduction Discrete tomography if

few materials which have lattice structure few grey values (few pixel values) Then 5 – 40 pictures may suffice

Advantages:

less damage of material smaller time range

For unicity: phenomena are clearer

Page 3: Unicity in discrete tomography - Lorentz Center Tijdeman.pdf · 1. Introduction Discrete tomography if few materials which have lattice structure few grey values (few pixel values)

Mathematical model

Simple model (binary case)

Page 4: Unicity in discrete tomography - Lorentz Center Tijdeman.pdf · 1. Introduction Discrete tomography if few materials which have lattice structure few grey values (few pixel values)

Horizontal line sums

Page 5: Unicity in discrete tomography - Lorentz Center Tijdeman.pdf · 1. Introduction Discrete tomography if few materials which have lattice structure few grey values (few pixel values)

Vertical line sums

Page 6: Unicity in discrete tomography - Lorentz Center Tijdeman.pdf · 1. Introduction Discrete tomography if few materials which have lattice structure few grey values (few pixel values)

Diagonal sums

Page 7: Unicity in discrete tomography - Lorentz Center Tijdeman.pdf · 1. Introduction Discrete tomography if few materials which have lattice structure few grey values (few pixel values)

Anti-diagonal sums

Page 8: Unicity in discrete tomography - Lorentz Center Tijdeman.pdf · 1. Introduction Discrete tomography if few materials which have lattice structure few grey values (few pixel values)

Questions 1.  Is the solution unique?

If not: 2. What can be said about other solutions? 3. Can we guess what was the original

solution?

Page 9: Unicity in discrete tomography - Lorentz Center Tijdeman.pdf · 1. Introduction Discrete tomography if few materials which have lattice structure few grey values (few pixel values)

Equal line sums

0 1 0 0

0 0 0 1

1 0 0 0

0 0 1 0

0 0 1 0

1 0 0 0

0 0 0 1

0 1 0 0

Subtract:

Page 10: Unicity in discrete tomography - Lorentz Center Tijdeman.pdf · 1. Introduction Discrete tomography if few materials which have lattice structure few grey values (few pixel values)

‘Switching component’ 0 1 -1 0

-1 0 0 1

1 0 0 -1

0 -1 1 0

Line sums are zero in the four directions. Any two tables with this difference have equal line sums.

Page 11: Unicity in discrete tomography - Lorentz Center Tijdeman.pdf · 1. Introduction Discrete tomography if few materials which have lattice structure few grey values (few pixel values)

Remarks

1.  Any two solutions have the same line sums, iff the difference has zero line sums.

2.  In general there are many more unknowns than line sums (linear equations). Therefore, in principle, there are many solutions.

3.  However, since we have only few grey values, the possibilities are restricted.

Page 12: Unicity in discrete tomography - Lorentz Center Tijdeman.pdf · 1. Introduction Discrete tomography if few materials which have lattice structure few grey values (few pixel values)

Unique solution

6

6

5

4

3

2

1

6 5 4 3 2 1

Page 13: Unicity in discrete tomography - Lorentz Center Tijdeman.pdf · 1. Introduction Discrete tomography if few materials which have lattice structure few grey values (few pixel values)

2. Algebraic structure Joint work with Lajos Hajdu (Debrecen, HU) J. reine angew. Math. 534 (2001), 119-128. Working over 0,1 is very difficult, but

working over the integers is not so hard. In this section we work with integer pixel

values.

Page 14: Unicity in discrete tomography - Lorentz Center Tijdeman.pdf · 1. Introduction Discrete tomography if few materials which have lattice structure few grey values (few pixel values)

Generating polynomial

3x2 - 2y + 7xy + 4y2 + 5xy2 + 6x2y2 – y3 + xy3

-1 1 0 0 4 5 6 0 -2 7 0 0 0 0 3 0

Page 15: Unicity in discrete tomography - Lorentz Center Tijdeman.pdf · 1. Introduction Discrete tomography if few materials which have lattice structure few grey values (few pixel values)

Directions

If vector between two consecutive pixels on a line is (a,b), then we write xayb-1 if b > 0, xa-1 if b=0, xa-y|b| if b < 0. (We assume a≥0.)

Thus: Row sum has (1,0) and therefore x-1, column sum has (0,1) and therefore y-1, diagonal sum has (1,1) and therefore xy-1, anti-diagonal sum has (1,-1)

and therefore x-y. Combination has (x-1)(y-1)(xy-1)((x-y).

Page 16: Unicity in discrete tomography - Lorentz Center Tijdeman.pdf · 1. Introduction Discrete tomography if few materials which have lattice structure few grey values (few pixel values)

Minimal switching component

(x-1)(y-1)(x-y)(xy-1) =

x3y2 - x3y + x2 - x2y3 + xy3 – x + y - y2.

Corresponding rectangle: Theory:This is the smallest configuration.

0 -1 1 0

1 0 0 -1

-1 0 0 1

0 1 -1 0

Page 17: Unicity in discrete tomography - Lorentz Center Tijdeman.pdf · 1. Introduction Discrete tomography if few materials which have lattice structure few grey values (few pixel values)

Properties

Size of minimal switching configuration is degree of x (sum over all ai) by degree of y (sum over all |bi|). Two tables with equal line sums cannot be very close to each other. All switching polynomials have a corresponding polynomial which is divisible by the minimal polynomial.

Page 18: Unicity in discrete tomography - Lorentz Center Tijdeman.pdf · 1. Introduction Discrete tomography if few materials which have lattice structure few grey values (few pixel values)

Example of switching component

-1 1

1 -1

-1 1-1 3 -3 1

1 -3 -1+1 -1 3

-1 3 1 -3

1 -1 -3 3

Directions (1,0), (0,1), (1,1), (1,-1).

Page 19: Unicity in discrete tomography - Lorentz Center Tijdeman.pdf · 1. Introduction Discrete tomography if few materials which have lattice structure few grey values (few pixel values)

Another example Consider directions (1,0), (2,1), (1,-3). Corresponding polynomial: (x-1)(x2y-1)(x-y3) = x4y -x3y –x2 +x -x3y4 +x2y4 +xy3 –y3. Minimal switching polynomial:

0 0 1 -1 0 -1 1 0 0 0 0 0 0 0 0 0 0 0 1 -1 0 1 -1 0 0

Page 20: Unicity in discrete tomography - Lorentz Center Tijdeman.pdf · 1. Introduction Discrete tomography if few materials which have lattice structure few grey values (few pixel values)

3. Geometric structure Suppose m by n block with integers. Then representation by vector of length mn

and entries in fixed order. If L line sums are given, then L linear

conditions on these entries. Hence solutions are on a linear manifold.

Line sums will be linear dependent, e.g.

sum of column sums = sum of row sums.

Page 21: Unicity in discrete tomography - Lorentz Center Tijdeman.pdf · 1. Introduction Discrete tomography if few materials which have lattice structure few grey values (few pixel values)

Solutions are on a hypersphere

We know the sums of the entries of a solution, as it is the sum of all the line sums in one direction, e.g. the sums of all row sums.

Suppose we have only entries 0 and 1. (‘binary’) Since 02=0 and 12=1, we then also know the sum

of the squares of the entries, and therefore the length R of the solution vector.

Hence all 0-1 solutions are on a hypersphere with radius R around the origin.

Page 22: Unicity in discrete tomography - Lorentz Center Tijdeman.pdf · 1. Introduction Discrete tomography if few materials which have lattice structure few grey values (few pixel values)

Theorem of Pythagoras Suppose 0-1 solutions. We know: -  all solutions are in some linear manifold - all solutions are on a hypersphere of radius R

around the origin. We can compute the orthogonal projection x* from

the origin on the linear manifold. By Pythagoras we can then compute the distance

from x* to the solution vector.

Page 23: Unicity in discrete tomography - Lorentz Center Tijdeman.pdf · 1. Introduction Discrete tomography if few materials which have lattice structure few grey values (few pixel values)

In a picture

-  Sol

All binary solutions are on the intersection of the sphere around the origin and on the linear manifold. Thus they are on the ‘circle’. We know the length of x and the length of x*, and therefore the length of x-x*.

Page 24: Unicity in discrete tomography - Lorentz Center Tijdeman.pdf · 1. Introduction Discrete tomography if few materials which have lattice structure few grey values (few pixel values)

Distances between solutions

≥: Difference between two solutions is multiple of minimal switching component.

≤: Distance between two solutions is at

most 2 √(|x|2 - |x*|2) where |x| and |x*| can be computed independent of solutions.

|x| is fixed. After adding a new direction the minimal switching component is larger

and upper bound probably smaller.

Page 25: Unicity in discrete tomography - Lorentz Center Tijdeman.pdf · 1. Introduction Discrete tomography if few materials which have lattice structure few grey values (few pixel values)

Conclusions

Instead of asking for binary solutions, one can more generally ask for the integer solutions with smallest vector length.

The Pythagorean argument gives an upper

bound on the distance between two tables with equal line sums.

The greater the distance between the origin

and its orthogonal projection, the better the approximation to the solutions.

Page 26: Unicity in discrete tomography - Lorentz Center Tijdeman.pdf · 1. Introduction Discrete tomography if few materials which have lattice structure few grey values (few pixel values)

Literature See: www.math.unideb.hu/~hajdul/ www.math.leidenuniv.nl/~tijdeman/ up to 2010, on arXiv from 2010 on.