Open access peer-reviewed chapter

Fractal Dimension Estimation Methods for Biomedical Images

By Antonio Napolitano, Sara Ungania and Vittorio Cannata

Submitted: December 28th 2011Reviewed: May 4th 2012Published: September 26th 2012

DOI: 10.5772/48760

Downloaded: 7423

1. Introduction

The current evolution of both texture analysis algorithms and computer technology made boosted development of new algorithms to quantify the textural properties of an image and for medical imaging in recent years. Promising results have shown the ability of texture analysis methods to extract diagnostically meaningful information from medical images that were obtained with various imaging modalities such as positron emission tomography (PET) and magnetic resonance imaging (MRI). Among the texture analysis techniques, fractal geometry has become a tool in medical image analysis. In fact, the concept of fractal dimension can be used in a large number of applications, such as shape analysis[1] and image segmentation[2]. Interestingly, even though the fact that self-similarity can hardly be verified in biological objects imaged with a finite resolution, certain similarities at different spatial scales are quite evident. Precisely, the fractal dimension offers the ability to describe and to characterize the complexity of the images or more precisely of their texture composition.

2. Fractals

2.1. Fractal geometry

A fractal is a geometrical object characterized by two fundamental properties: Self-similarity and Hausdorff Besicovich dimension. A self-similar object is exactly or approximately similar to a part of itself and that can be continuously subdivided in parts each of which is (at least approximately) a reduced-scale copy of the whole. Furthermore, a fractal generally shows irregular shapes that cannot be simply described by Euclidian dimension, but, fractal dimension (fd) has to be introduced to extend the concept of dimension to these objects. However, unlike topological dimensions the fd can take non-integer values, meaning that the way a fractal set fills its space is qualitatively and quantitatively different from how an ordinary geometrical set does.

Nature presents a large variety of fractal forms, including trees, rocks, mountains, clouds, biological structures, water courses, coast lines, galaxies[3]. Moreover, it is possible to construct mathematical objects which satisfy the condition of self-similarity and that present fd (Figure 1).

Figure 1.

Sierpinski triangle: starting with a simple initial configuration of units or with a geometrical object then the simple seed configuration is repeatedly added to itself in such way that the seed configuration is regarded as a unit and in the new structure these units are arranged with respect to each other according to the same symmetry as the original units in the seed configuration. And so on.

The objects in Figure 1 are self-similar since a part of the object is similar to the whole and the fractal dimension can be calculated by the equation:

D=logNlogSE1

where Nis the number of the auto-similar parts in which an object can be subdivided and Sis the scaling, that is, the factor needed to observe Nauto-similar parts. According to the Eq.1, the following values are obtained for the Koch fractal and the Sierpinski triangle:

DKoch=log4log31.26DSierpinski=log3log31.58E2

In mathematics, no universal definition of fd exists and the several definitions of fd may lead to different results for the same object. Among the wide variety of fd definitions that have been introduced, the Hausdorff dimension DHis surely the most important and the most widely used[4]. Such definition can be theoretically applied to every fractal set but has the disadvantage it cannot always be easily determined by computational methods.

2.2. Hausdorff dimension DH

Hausdorff dimension DHwas introduced in 1918 by mathematical Felix Hausdorff [3]. Since many of the technical developments used to compute the Hausdorff dimension for highly irregular sets were obtained by Abram Samoilovitch Besicovitch, DHis sometimes called Hausdorff-Besicovitch dimension.

Hausdorff formulation[3] is based on the construction of a particular measure, HδD, representing the uniform density of the fractal object.

Intuitively we can sum up the construction as follows: let be Aa fractal and C(r,A)={B1....Bk}a complete coverage of Aconsisting of spheres of diameter smaller than a given r that approximateA, soδi=δi(Bi)<r.

We define the Hausdorff measure as the function HδDthat identifies the smallest of all the covering spheres for Awithδ<r:

HδD(A)=ωDlimr0{infiδiD}E3

with ωDvolume of the unit sphere in RDfor integerD.

We obtain an approximate measurement ofA, the so-called course-grained volume[4].

In the one-dimensional case (D=1), HδDsupplies the length of set Ameasured with a ruler of lengthr. The shorter the ruler, the longer the length measured, a paradox known as the coastline paradox[3].

Hence, when r0the effective length of Ais well approximated. Limit for small rcalculated for other values ofD, however, lead to a degenerateHδD:

HδD0    and    HδDE4

Therefore, DHcan be defined as the transition point for the function HδDmonotonically decreasing withD:

DH(A)=infD>0{HδD(A)=0}E5

with HδDthe D-dimensional Hausdorff measure given by Eq. 3.

The course-grained volume defined by Eq. 3 normally presents a scaling like:

HδD~r(D-DH)E6

that provides a method to estimate the dimensionDH.

In the uni-dimensional case D=1we can easily obtain:

LδD~r(1-DH)   with   LδD=measured  lengthE7

from which we deriveDH.

3. Methods

Although the definition of Hausdorff dimension is particularly useful to operatively define the fd, that presents difficulties when implementing it. In fact, determining the lower bound value of all coverings, as defined in Eq. 5, can be quite complex. For example, let’s consider the uni-dimensional case, in which we want to compute the fd of a coastline (Koch Curve). According to Eq. 3 in the case of D=1the coastline length is measured by a ruler of lengthr. Accuracy of the measure increase with decreasingr. For r0the coastline will have infinite length. Similar arguments can be applied toD=2; for r0the measure ofHδD0.

This discussion implies that our coastline (ex. Koch Curve) will have a fd value more than one-dimensional and less than two- dimensional. For this reason, the fd is considered as the transition point (the lower bound value in Eq. 5) between HδD0andHδD.

Several computational approaches have been developed to avoid the need of defining the lower bound at issue. Therefore many strategies accomplished the fd computation by retrieving it from the scaling of the object’s bulk with its size. In fact, object’s bulk and its size have a linear relationship in a logarithmic scale so that the slope of the best fitting line may provide an accurate estimation of this relationship. By using this log-log graph, called Richardson’s plot, the requirement of knowing the infimum over all coverings is relaxed.

Several approaches have been developed to estimate fractal dimension of images. In particular, this section will introduce two fractal analysis strategies: the Box Counting Method and the Hand and Dividers Method.

These methods overcome the problem by choosing as covering a simple rectangle fixed grid in order to obtain an upper bound onDH.

Five algorithms for a practical fd calculation based on these methods will also be presented.

3.1. Box counting method

The most popular method using the best fitting procedure is the so-called Box Counting Method[5][6]. Given a fractal structure Aembedded in a d-dimensional volume the box-counting method basically consists of partitioning the structure space with a d-dimensional fixed-grid of square boxes of equal sizer.

The number N(r)of nonempty boxes of size rneeded to cover the fractal structure depends onr:

N(r)~r-DE8

The box counting algorithm hence counts the number N(r)for different values of rand plot the log of the number N(r)versus the log of the actual box sizer. The value of the box-counting dimension Dis estimated from the Richardson’s plot best fitting curve slope.

-D=limr0logN(r)logrE9

Figure 2 shows the Box counting method for the Koch Curve.

Figure 2.

The Box-counting method applied to the Koch Curve with box size r = 0.4 (a); r = 1 (b); r = 1.4 (c); r= 2 (d)

Several algorithms[7][8][9] based on box counting method have been developed and widely used for fd estimation, as it can be applied to sets with or without self-similarity. However, in computing fd with this method, one either counts or does not count a box according to whether there are no points or some points in the box. No provision is made for weighting the box according to the number of points belonging to the fractal and inside the current box.

3.2. Hand and dividers method

Useful features and information can be deducted from the contours of structures belonging to an image and there is a number of techniques that can be used when estimating the boundary fractal dimension.

The most popular methods are all based on the Hand and Divers Method which was originally introduced by Richardson[10] and successively developed by Mandelbrot[11].

The Richardson method employs the so-called walking technique consisting of "walking" around the boundary of the structure with a given step length.

The actual structure boundary is so approximated by a polygon whose length is equal to:

l(ε)=εn(ε)E10

In a nutshell, it corresponds to the length of the single step multiplied by the number of steps needed to complete the walk.

The process is then reiterated for different step lengths:

Pi=l(εi)=εin(εi)E11

With Pithe perimeter calculated with steps of lengthεi.

The object’s boundary fd Dis finally estimate from:

D=1-mE12

where mis the slope of the Richardson’s plot.

The perimeter length of the boundary depends on the step length used so that a large step provides a rough estimation of the perimeter whereas a smaller step can take into account finer details of the contour.

Consequently, if the step length εdecreases the perimeter Pincreases.

In practice, the perimeter length is obtained by constructing a generally irregular polygon which approximate the border. Let δBbe the set of coordinates of object boundary and let be εa fixed step length. Given a starting point, an arbitrary contour point(xs,ys), the next point on the boundary (xs2,ys2)in a fixed direction (e.g. clockwise) is the point that has a distance

di=(xs-xs2)2+(ys-ys2)2E13

as close as possible toε.

The reached point then becomes the new starting point and is used to locate the next point on the boundary that satisfies the previous condition. This process is repeated until the initial starting point is reached.

The sum of all distances djcorresponds to the irregular polygon perimeter (Figure3).

A number of different perimeters for each polygon at each fixed step length are used to build the Richardson’s plot and the slope of its best linear fit is exploited to estimate the fd.

Figure 3.

Walking technique applied to a coastline with different step lengths.

4. Algorithms

All Hand and dividers techniques rely on the same identical principle that attempt to approximate the border perimeter with a different polygons. However, since the point coordinates belonging to border set are discrete, all the implemented methods differ in the choice of which point in the set has a distance that better approximate the step length.

The following two methods are the implementations of two different choices about how to overcome this particular issue.

4.1. HYBRID algorithm

The HYBRID algorithm is a computer implementation of Hand and Dividers method developed by Clark[12]. Let δBbe the boundary of the object whose fd we wish to compute. The main part of the method focuses on the perimeter estimation and the corresponding Richardson’s plot is then attained by reiterating this hard core part at different step size. Figure 4 shows the flow chart of the method.

Figure 4.

Perimeter estimation by HYBRID method flowchart: an arbitrary starting point S(xS,yS) on the boundary line is chosen and copied in a new variable, which is called current point C(xC(i),yC(i)). The index i runs through the total number of coordinate points and is iteratively increased defining the running point Rwith coordinates(xR,yR). The distance d between Sand Ris calculated and a check on dwhen smaller than a fixed step length εis done. The process is repeated until a boundary point whose distance from (xS,yS) is larger than the step εis reached. The next pivot point on the boundary line is determined by choosing between the two points the closest to the step length. The distance is then stored and this point becomes the new starting point in order to calculate the next pivot point and so on, until the initial starting point Sis reached.

Given an arbitrary starting point Sand its coordinates (xS,yS)on the boundary, the algorithm searches for the next pivot point. In particular the starting point is copied into a current point, C(xC,yC), which identifies all points having a mutual distance of aboutε. The actual point running through the entire border is indicated as running point R(xR,yR).

Therefore the program searches for a specific running point having a distance from Cas near as possible to the stepε. In particular, in the HYBRID method the real step may be chosen to be longer or shorter than the fixed step depending on the minimum deviation from it. Similarly once the running point hits a contour point having a distance from the actual current one bigger than the size step, the choice is made between that point and the preceding one.

Afterward, the computed distance between these two points Rand Cis stored and the running point becomes the new current point.

The procedure continues until the initial starting point is reached. Obviously it is likely that after a complete walking the starting point Smay be reached before having hit the following current pointC. In other words, there may not be a multiple of step size εso that the final incomplete step length ris added to the others stored distances, whose sum represent the boundary’s perimeter. Since the fixed step length is adapted every time during the perimeter computation, its averaged value is then computed and used in the Richardson’s plot.

4.2. EXACT algorithm

The EXACT algorithm was proposed for the first time by Clark in 1986[12]. As it will be shown, this method requires a longer computational time by providing a simpler solution to the choice of the best current points.

Similarly to the previous method the entire perimeter estimation is displayed in the flow chart of Figure 5.

The procedure is very similar to the one used for the previous method. As before (see Figure 5), the end of the step may not coincide with the digitized coordinates of the boundary.

The way the EXACT method attempts to overcome this problem relies on the assumption of piecewise linearity, meaning that all the points on the contour can be joined by a series of straight line[13, 14] (see Figure 6 (a)).

The location of the next current point Con the boundary from the one previously determined is schematically illustrated in Figure 6 (b).

The procedure starts from an arbitrary starting point (xS,yS)and the algorithm searches for the next pivot point. In particular the starting point is copied into a current point, C(xC,yC), which identifies all points having a mutual distance of aboutε. The actual point running through the entire border is indicated as running point R(xR,yR).

The distance from the current point to each point on the contour line is then calculated until the step length εfalls between two consecutive boundary points, (xR,yR)and (xR+1,yR+1)for which:

(xR-xC)2+(yR-yC)2<ε<(xR+1-xC)2+(yR+1-yC)2E14

The exact position of the point Nwith coordinates (x,y)is deduced by a process of geometric interpolation between the two consecutive running points (xR,yR)and(xR+1,yR+1). This point then becomes the new current point and is used to calculate the next boundary point and so on.

The process is stopped when we come back to the initial starting point (xS,yS)in order to obtain a polygon as is shown in Figure 8.

Figure 5.

Perimeter estimation by EXACT method flowchart: an arbitrary starting point S(xs,ys) on the boundary line is chosen and copied in a new variable, which is called current point C(xc(i),yc(i)). The index i runs through the total number of coordinate points and is iteratively increased defining the running point Rwith coordinates(xR,yR). The distance d between Sand Ris calculated and a check on dwhen smaller than a fixed step length εis done. The process is repeated until a boundary point whose distance from (xs,ys) is larger than the step εis reached. The exact position of the next pivot point (x,y) on the boundary line is determined by interpolating the two consecutivepoints (xR,yR) and(xR+1,yR+1).

The point (x,y)becomes the new starting point in order to calculate the next pivot point and so on, until the initial starting point Sis reached.

Figure 6.

a) The piece-wise linear assumption (a) (b) and the EXACT algorithm (c); b)Geometric EXACT interpolation scheme, with Sstarting point given by (xC,yC) coordinates, Rand R'two consecutive boundary running points respectly given by (xR,yR) and (xR+1,yR+1) coordinates, Nnew current point obtained by the interpolation between Rand R'and εa fixed step length.

Figure 7.

MRI image of an aneurismatic bone cyst (a), (b). Walking technique applied to an aneurysmatic bone cyst boundary (c).

The perimeter length of the polygon is found by adding the final incomplete step length to the sum of the other step lengths needed to entirely cover the boundary.

The procedure is then repeated for different step lengths[15].

The results, i.e., perimeter lengths versus step lengths, are plotted on a log-log Richardson’s Plot. From the slope of the fitting line on the Richardson’s plot we obtain the fd of the examined boundary[17, 12, 16, 18, 19, 1, 20, 21, 4]

4.3. Box-counting algorithm

The Box-counting algorithm implementation of box-counting method relies on the basic idea of covering a given digital binary image with a set of measuring boxes of sizes Sand then to count the number of boxes which actually contain the image.

Figure 8.

The Box-counting algorithm flowchart: given an imageI, its size, S0, is set as the maximum size from which the computer program starts to calculate the others decreasing box-sizes according toS=S0/2N. The Svalue has minimum value which is equal to the pixel size. The number pindicates the total number of box sizeS. The next step is a check on whether at least one pixel is in the box: if the box is non-empty, the check is stopped when one pixel is found. The procedure continues until the maximum index p(s)is reached. Then the number Nboxes(S)for a given size Sis stored and the process restarts with a different box size. When the minimum box size is reached the program stops and gives the output variables of Nboxesand the size value. Using the Eq. 8 the fractal dimension Dcan be estimate, from the least square linear fit.

Figure 8 shows the flow chart for box-counting fd estimation and for different box sizes. Moreover, since the procedure of size scaling (S=S0/2Nwith Nnumber of iterations) may be not always applicable to any image matrix size, image padding with background pixels is performed.

Therefore the final image Ihas a dimension that is a power of 2. This can be easily implemented by using padarray matlab function.

4.4. Differential Box-counting algorithm (DBC)

The box counting method is an extremely powerful tool for fd computation; in fact, it is easy to implement as well as flexible and robust.

However, a major limitation lies on the fact that the counting process of nonempty boxes implies its use only for binary images rather than gray scale ones. An extension of the standard approach to gray scale images is called the Differential Box Counting (DBC) and has been proposed in 1994 by N. Sarkar and Chaudhuri[8].

In the DBC method, a gray level image Iis considered as a 3-D spatial surface with (x,y)denoting the pixels spatial coordinates and the third axis zthe pixels gray level.

As for the standard box counting, the M×Mimage matrix is partitioned into non-overlapping s×s-sized boxes, where sis an integer falling in the interval[M/2,1].

Then, the scale of each block isr=s. On each block there is a column of boxes of sizes×s×s', with s'denoting the height of a single box. Named Gthe total number of gray levels inI, hence s'is defined by the relationship G/s'=M/s[7].

Let numbers1,2,3... be assigned to the boxes so to group the gray levels. Let the minimum and the maximum gray level of the image in the (i,j)thgrid fall in box number kandl, respectively.

The number of boxes covering this block is calculated as:

nr(i,j)=l-k+1E15

In Figure 9 for examples=s'=3, hencenr=3-1+1.

Figure 9.

Example of DBC method application for determining the number of boxes of sizes×s×s, whens=3.

Extending to the contribution from all blocks:

Nr=i,jnr(i,j)E16

The Eq. 16 is computed for different box size s(so for differentr) and the values of Nrare plotted versus the values of rin a log-log plot.

A matlab implementation of DBC can make use of functions such as blockprocor colfiltin order to make the box partitioning and apply the Eq. 15.

The DBC procedure has some weak points in the method used to select an appropriate box height[7], since the values of sis limited to the image size and s'is limited by the number of blocks of size s×sin which the image is divided.

Secondly, the box number calculation may lead to overestimate the number of boxes needed to cover the surface. Let Aand Bbe the pixels associated with the minimum and the maximum gray level of the block respectively, as is illustrated in Figure 10.

Figure 10.

Example of DBC method application with boxes ofs×s×s, whens=3. The two pixels AandB, denoting the maximum and the minimum gray levels of the block, are assigned in two differents boxes, having distance in eight direction smaller than the box sizes=3.

According to DBC procedure, the two pixels are assigned in boxes 2 and boxes 3. The distance between Aand Bis smaller than 3, which is the size of the box.

Hence, when calculating Eq. 15, the block can be covered by a single box but its pixels with minimum and maximum gray levels fall into two different boxes.

To solve the aforementioned problems some modifications was proposed by J. Li, Q. Du and C. Sun[7]. Given a digital image Iof sizeM×M, a new scale ris defined instead ofr, i.e. r'=r/cwhere câĄ1is a positive real number.

In particular, let μand σbe the mean and the standard deviation of Irespectively. Hence, if the greater part of image pixels fall into the interval of gray level within[μ-aσ,μ+aσ], where ais a positive integer, the height of the boxes is given by:

r'=r1+2aσE17
If dzis the height of the boxes in the direction ofz, the number of the column of boxes on a single image block correspond to the integer part of (dz/r'+1)instead of (dz/r+1)as in the original DBC method. Thus, sincer'<r, the residual part of dz/r'is smaller than that ofdz/r.

As a result, the errors introduced using r'are smaller than in the original DBC method. A box with smaller height is chosen when a higher intensity variation is present on the image surface. So the improved method uses, in general, finer scales to count[7].

Moreover, the use of dzinstead of zto count the number of boxes leads to the following modification of Eq. 15:

nr=ceil(l-kr'),1,   l=k   E18

with ceil(. ) denoting the function rounds the elements of the quantity into (. ) to the nearest integers greater or equal to it.

Eq. 18 relies a new way to count the number of boxes that cover the (i,j)thblock surface in which the boxes are assigned to the minimum gray level to the block rather than gray level 0[7].

As an example, suppose that the (i,j)thblock is covered by a column boxes with the size3x3x3. If the pixels Aand Brepresent the maximum and the minimum gray levels of the block, the two pixels will be assigned as in Figure 10.

According to Eq. 18 the number of counted boxes isnr=1, which is exactly the number of boxes covering the block.

As in standard box counting method, after having determined the number nr(i,j) for each block, the total number of boxes Nrcovering the full image surface is computed for different scalesr. Plotting the linear fit of log Nrversus the log r(Richardson’s plot) the fd is finally estimated.

5. Applications and discussion

Each described method has been implemented in Matlab 2010a and applied to either well-known fractals or biomedical images.

The results on the hand and dividers methods are shown in the table 1. The computed values are also compared to the theoretical fd values. The computational time for a 2.50 GHz 5i CPU is also shown.

The value ranges for the step size are not displayed but they were automatically chosen based upon the computation of the structure’s maximum caliber diameter which is defined as the major axis of an ellipse in which the structure can be embedded. The range was then running from the 40%of the maximum caliber diameter to the minimum step defined as the maximum distance between any two contiguous border points.

In practice, both EXACT and HYBRID methods computed the different step sizes by scaling each time the maximum step by akwith kthe number of the iteration. The chosen value of a=1.2is a compromise between a sufficient number of fitting points and the need to avoid too small variations of the step size so to duplicate perimeter estimation. The latter usually occurs in HYBRID method for it hits the same current points if the step does not vary enough in two consecutive iterations.

The parameter’s estimation uncertainty is also shown in the table 1; that is calculated from the fitting accuracy based upon standard linear regression.

The number of data points used in the Richardson’s plot was about 60 and two examples of that computation using EXACT and HYBRID are shown in Figure 12.

On the table 2 the computation results for the box counting method are also shown. The type of the displayed values are similar to the previous ones with the exception of Box counting uncertainty. In fact, the way an image can be partitioned into several boxes may affect the final computation of the number of nonempty boxes.

To investigate the variability of the fd for different box partitioning layouts, random box subdivisions have been applied. Therefore, the results on the table 2 show the standard deviation of the different computed fds and the mean values for each fractal at issue. In general, that variability is more pronounced in images having rougher resolution.

FractalfdtheofdexpTime(sec)BCerror
Apollonian Gasket1.30571.4081.50.0012000Image2000
Sierpinski1.58491.5870.30.0051000size1000
Dragon2.00001.7477.20.0063670×3978
Hexaflake1.77191.6401.60.0111050×1050

Table 1.

Tabular of results for box counting method application.

××Fractalfdtheo(sec)fdexpTime
Twin Dragon Hybrid1.52361.4668.60.006117005
Twin Dragon Exact1.52361.46511.50.006117005
Dragon Hybrid1.52361.47411.10.005115665
Dragon Exact1.52361.46212.80.004115665
Koch Hybrid1.26191.27631.20.004786433
Koch Exact1.26191.260154.90.003786433
Gosper Hybrid1.12921.1333.80.00123280
Gosper Exact1.12921.1284.70.00123280

Table 2.

Tabular of results for walking-based methods application.

Figure 11.

EXACT method apllied to the twin dragon fractal: Richardson’s Plot.

Figure 12.

HYBRID method apllied to the twin dragon fractal: Richardson’s Plot.

In general, the EXACT and the HYBRID methods appeared to be more precise than the box counting method but on the other hand they have a less wide range of applicability. However, this is also the reason of the fortune of the box counting methods compared to the others. Also, HYBRID technique is computationally less expensive than EXACT especially when the number of border points is quite large. The use of a variable step length which can be shorter or longer than the fixed step size leads to a larger variability and so to a Richardoson’s plot having a less accurate fitting. That has effects on the uncertainties of the parameter to estimate. Because of that, a more careful choice of the step size range is needed in the case of HYBRID method.

Importantly, it is quite clear that the choice of the starting point may also affect the perimeter value as the following currents points will depend upon this. A test on 80 random starting points for the Gosper Island fractal revealed that the fd computation performed with the HYBRID method appeared to be more stable than the one with EXACT.

As for walking method, in box counting the process of scaling from the maximum box size is limited by the pixel size so in principle a gross resolution might be the reason of a bad estimate of fd. It is noteworthy that the tests performed do not show any correlation between resolution and fd accuracy; that may be also caused by the fact that some fractals such as dragon does not reproduce the real fractal at small scales.

An application of the DBC method on a x-ray image is also shown in Figure 13 where breast cancer mammography image has been processed. The method uses a sliding technique as implemented in BCor errormatlab functions so to produce an image rather than a single fd value as previously described.

The second DBC method shows higher contrast in the area of the cancer and consequently lower fd values. Due to the enormous amount of linear fitting performed for an image size of 3450 Vector3100 the computational time reached 15 minutes.

Figure 13.

High resolution mammography image (a); fd recostruction image by standard Differential Box Counting (DBC) (b); fd reconstruction image by modified DBC (c).

6. Conclusions

In this chapter some of the most widely used and robust methods for fractal dimension estimation as well as their performances have been described. For few of them a detailed description of the algorithm has been also reported to make much easier for a beginner to start and implement his own Matlab code. Computational time is not excessively long to necessitate compiled functions such as C-mex files but that can be an advantage when using very high resolution images. The use of the described algorithms is obviously not restricted to the sole field of the image processing but it can be applied with some changes to any data analysis.

© 2012 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Antonio Napolitano, Sara Ungania and Vittorio Cannata (September 26th 2012). Fractal Dimension Estimation Methods for Biomedical Images, MATLAB - A Fundamental Tool for Scientific Computing and Engineering Applications - Volume 3, Vasilios N. Katsikis, IntechOpen, DOI: 10.5772/48760. Available from:

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