New Bioinspired Filter of DICOM Images

Arata Andrade Saraiva

1

, N. M. Fonseca Ferreira

2,3

and Antonio Valente

4

1

UNICEUMA, UTAD University, Vila Real, Portugal

2

Institute of Engineering of Coimbra, Polytechnic Institute of Coimbra, Portugal

3

Knowledge Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development

(GECAD) of the Institute of Engineering, Polytechnic Institute of Porto, Portugal

4

INESC TEC (formerly INESC Porto), School of Science and Technology, UTAD University, Vila Real, Portugal

Keywords:

Filtering, Genetic Algorithm, Hybrid, DICOM.

Abstract:

The article refers to a new model of genetic algorithm. The method used has ﬁnality of optimize the ﬁltering

of artifacts in DICOM images in two-steps.The ﬁrst step is constituted by ﬁlterings with BM4D, 3d median

ﬁlter and ellipsoid ﬁlter. The second step is formed by the application of operators of simple mutations in

the previously recovered image, for that was used: intensity change, gaussian ﬁlter and mean ﬁlter. As a

result, a better performance ﬁlter was obtained and which provides an improvement in diagnosis, in diseases

assessment and in decisions making by the professional.

1 INTRODUCTION

Digital images have been used for various purposes,

from just storing remembrances until accurate exams

in medicine (James and Dasarathy, 2014). Over the

years, the use and popularization of the digital image

made it possible the great increase of the volume of

images, just like it’s over by to make available new

advances and challenges in its use. As example there

is an introduction of images processing solutions in

industrial environment for visual inspection in envi-

ronments at risk for a physical integrity of employees

(Gonalves et al., 2014).

Despite of various technological advances, during

the captation process and posteriorly the transmission

of the digital images can acquire artifacts in innume-

rable ways.

Each artifacts ﬁlter model adapts differently to

each noise, thus forming its advantages and disadvan-

tages in relation to a determined type of noise.

As an example, there is the image in ﬁgure 1, here

white (Gaussian) noise has been added. This type of

noise is quite common in communications.

The white noise it comes from the stirring of the

electrons in the metallic conductors. Its level is in

function of the temperature, being evenly distributed

in all the frequencies of the spectrum.

The challenge of suppressing or attenuating has

provided the search of enhancement of techniques

Figure 1: Image increased Gaussian noise.

to reduce imperfections, in way to preserve impor-

tant information of the image such as corners, bor-

ders and textures. In the literature, the BM3D (Dabov

et al., 2006) noises attenuation technique was inten-

sively researched and tested on real problems such as

suppress artifacts in images of ultrasonography (Gan

et al., 2015), however, there is no solution available to

completely solve the problem.

Genetic Algorithms (GA) are metaheuristics ba-

sed on the theory of evolution of the species of Char-

les Darwin, where by natural selection the ﬁttest in-

dividual tends to survive and reproduce descendants

(Barbosa, 2014). In this context, the individual is a

representation of the solution of the problem.

This work proposes and analyzes a 3D hybrid ge-

netic algorithm (HGA3D) for noise attenuation in DI-

COM medical images, integrates genetic algorithm to

258

Saraiva, A., Ferreira, N. and Valente, A.

New Bioinspired Filter of DICOM Images.

DOI: 10.5220/0006723802580265

In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 1: BIODEVICES, pages 258-265

ISBN: 978-989-758-277-6

Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

some literature attenuation methods: BM4D (Maggi-

oni et al., 2013), 3D median ﬁlter (Jiang and Crookes,

2006) and ellipsoid (Yang et al., 2008).

Each individual of the population corresponds to

an image initially restored by one of these three met-

hods and the others individuals of the population are

created through the application of different mutation

operators in the initial image. HGA3D evolves the

entire population during a determined amount of time

and at the end the best individual is returned as the

restored image.

The hypothesis of the work is that the proposed

genetic algorithm model be able to ﬁnd quality so-

lutions when compared to other methods present in

the literature for the smoothing of artifacts in DICOM

images.

Thus, the article starts with the state of the art, a

review of evaluation methods used, explains the met-

hodology, it is made to exhibition of results and a dis-

cussion of the results.

2 STATE OF ART

Different solution methods for the noise attenuation

problem were proposed. The BM4D (Maggioni et al.,

2013) method for example, use sliding voxels cubes in

a ﬁrst stage for the stacking of similar cubes, in a se-

cond phase each cube is ﬁltered by a Wiener type ﬁlter

(Gonzalez and Woods, 2006). At the end, the image

is reconstructed using adaptive weights for each cube

added in its original position.

The proposed BM4D algorithm proved to be ef-

fective for gaussians noise and its performance is re-

markable in PSNR statistics generated during the aut-

hor’s tests.

Approaches based on the 3D median ﬁlter (Jiang

and Crookes, 2006) were also suggested. Widely ap-

plied in images processing, this ﬁlter is known for

its edge conservation nature. The ﬁlter demonstrated

in this paper uses a median calculation of a window

with sliding mask size NxNxN voxels. Results de-

monstrate its efﬁciency for removal of splashes in 3D

medical images, in addition to having low computati-

onal cost.

In addition to the previously cited methods, there

is also the ellipsoidal ﬁlter (Yang et al., 2008). In this

paper the author proposes a three-dimensional me-

dian ﬁltering method and then an adaptive ellipsoidal

Gaussian ﬁltering method for local preservation of the

image characteristics. According to the research the

ﬁlter is ideal in the meaning it reduces the magnitude

spatial of the high frequency in an image.

There are also methods based on genetic algo-

rithm of great relevance currently, as the hybrid ge-

netic algorithm for noises suppression in images pro-

posed in Paiva’s thesis (Paiva, 2016). It is proposed

the combination of a genetic algorithm with various

algorithms for the removal of artifacts from images

found in the literature.

The HGA was tested on images corrupted by a

gaussian additive noise with different levels of stan-

dard deviation. At the end of the work, the effective-

ness of the proposed method is demonstrated by me-

ans of statistical and visual data, showing better re-

sults in several cases in relation to literature methods.

In addition to all the methods already mentioned

above, a search was made in the literature for other ap-

proaches of great current impact in the research area,

for this was considered the google scholar metrics op-

tion. Initially a work was used available in ’IEEE

Transactions on Image Processing’ whose index is

the same.

In the work proposed in (Moore and Lopes, 1999)

is proposed a general methodology to create and op-

timize a wide group of algorithms for the destruction

of a mixed artifact between poisson noise and gaus-

sian noise. To remove of the artifact is demonstrated,

an algorithm denominated PURE-LET where in par-

ticular the best results are obtained. With the tests in

images and posteriorly the comparison between this

proposed method and other competing methods it is

veriﬁed the effectiveness of the restoration of several

textures present in the image.

In (Danielyan et al., 2012) is proposed an analy-

sis and synthesis for the family of BM3D algorithms

aiming to develop new iterative algorithms of deblu-

ring. The BM3D is a non-local modeling technique

based on adaptive models, it is divided into three steps

where initially, similar image blocks are collected in

groups, then the obtained groups spectra are ﬁltered,

and lastly the ﬁltered spectra are inverted providing

estimates of blocks that were returned to their original

positions and ﬁnally occurs the image recostruction.

Based on the researchs carried out and described,

a genetic algorithm based on BM4D, 3D median and

ellipsoid was developed.

3 METRIC METHODS OF

EVALUATION

The image ﬁltering search aims to reduce the num-

ber of artifacts to represent an image, removing the

noises, as much as possible. The ideal is to get the

resulting image it’s close to the original image. One

of the ways to quantify is given by the measurement

New Bioinspired Filter of DICOM Images

259

of proximity with the Mean Square Error (MSE) can

be deﬁned mathematically by equation 1 (Talbi et al.,

2015).

MSE =

1

mn

m−1

∑

x=0

n−1

∑

y=0

(I(x, y) − K(x, y))

2

(1)

In this equation I represents the original image and

K the ﬁnal image to be compared. The x and y are two

matrices of size MxN, respectively representing the

original x-channel and the y-channel to be compared

(after ﬁltering).

Another way to compare the quality of the ima-

ges is the Peak Signal to Noise Ratio (PSNR) what is

usually a measure of image quality and can be repre-

sented by equation 2 (Fedorov and Rodyhin, 2016).

The PSNR ideal of comparison presents an optimum

value the higher its is your value.

PSNR = 10 log

MAX

2

MSE

= 20 log

MAX

MSE

1

2

(2)

In which, MAX represents the maximum possible

value of the pixel in the image and MSE is the value

resulting from equation 1.

The MSE may present problems when used to

compare similarity. The main from them is that large

distances between pixel intensities do not necessarily

mean that the content of the images be dramatically

different. It is important to note that a value of 0 for

MSE indicates perfect similarity. A value greater than

1 implies smaller similarity and will continue to grow

as the mean difference between pixel intensities in-

creases as well.

In order to remedy some of the problems asso-

ciated with MSE for image comparison, one has the

Structural Similarity Index (SSIM). The SSIM Is ob-

served by equation 3.

SSIM(x, y) =

(2µ

x

µ

y

+ c

1

)(σ

xy

+ c

1

)

(µ

2

x

+ µ

2

y

+ c

1

)(σ

2

x

+ σ

2

y

+ c

2

)

(3)

In the equation 3 o µ represents the mean, the σ

symbolizes the standard deviation and σ

xy

the cova-

riance. And c

1

with c

2

represent constants that avoid

the instability of values.

Unlike MSE, the SSIM value can range from -1 to

1, where 1 indicates perfect likeness.

The essence of SSIM is to model the perceived

change in the structural information of the image,

while the MSE is actually estimating the perceived

errors. There is a subtle difference between the two,

but the results can be great.

In addition, the SSIM is used to analyze small sub-

samples instead of the entire image as in MSE. The

parameters used are the mean of the pixel intensities,

the variance of the intensities, together with the cova-

riance. In this way, a more robust approach is obtai-

ned capable of explaining the changes in the structure

of the image, instead of just the perceived change.

For the quantitative comparison of the ﬁltering

methods in this article, the objective metrics evalua-

tion methods MSE, PSNR and SSIM were used. Such

methods are known as full reference, because they

consider the original image as a reference.

These methods are applied over a DICOM image.

Being that, in this work MatLab software was used

to manipulation of the presented algorithms and the

visualization of the results.

4 METHODOLOGY

The hybrid genetic algorithm (HGA) of this work is

based on the genetic algorithm (GA) proposed by To-

leto (Toledo et al., 2013) and in the method proposed

by Paiva (Paiva, 2016), where each individual of the

population is an image itself, represented by a set of

pixels whose values are integers in the range of 0 to

255. In a similar way to this method starts the propo-

sed algorithm, where a noisy image is used as input

for the method and the other individuals in the popu-

lation are created from applied mutation operators.

Based on the analysis and results demonstrated in

(Paiva, 2016), it was decided that the same parameters

already tested by the author were used as standard va-

lues in the model proposed here. In the step by step of

choosing the best parameters by the author is demon-

strated the effectiveness of each change in metric data

PSNR and SSIM, in addition a whole argumentation

of each result is provided.

According to Paiva (Paiva, 2016), during the

choice of tournament size the worst case of tourna-

ment size 3 tends to be better than the worst case of

the others. However the test of the different local se-

arch rates, although all the results were very close,

the value rate 0.6 was the one that reached the best

results compared to the others. The differences bet-

ween the results with different population sizes sho-

wed up clearer in their 2D approach, however, there is

a superiority of size 15 population where it obtained

good results in about 70 % of cases in both PSNR and

SSIM. Also is demonstrated the effectiveness of two

other parameters, beta whose best value was 1.5 and

execution time equal to 20 minutes.

The proposed algorithm combines the GA method

approach (Toledo et al., 2013), with noises smoothing

techniques in 3d images. In which, the pseudocode is

described in Algorithm 1.

BIODEVICES 2018 - 11th International Conference on Biomedical Electronics and Devices

260

Algorithm 1 : Hybrid Genetic Algorithm for Dicom

(HGA3D).

!th

1: function HGA3D(Dicom path)

2: images ←ReadingPath(path)

3: Population ←createPopulation(images)

4: best ←Population.best

5: while elapsedTime < maxTime do

6: cont ← 0

7: while cont < maxIter do

8: IntermPop ← Population

9: for i ← 1 to Population.size do

10: ind1 ←Parents(Population)

11: ind2 ←Parents(Population)

12: ind3 ←Crossover(ind1,ind2)

13: if (Λ ∈ [0, 1]) ≤LocalSearchRate

then

14: localSearch(ind3)

15: end if

16: IntermPop.append(ind3)

17: end for

18: Sort(IntermPop)

19: Population ←IntermPop[1..Popula-

tion.size]

20: if (best =Population.best) then

21: cont ← cont + 1

22: else

23: cont ← 0

24: end if

25: end while

26: end while

27: end function

The beginning of the HGA consists of creating

the initial population in two steps: ﬁrst, the image

with noise is used as input for three methods of noi-

ses smoothing. Thus, at the end of the ﬁrst stage, the

population has three individuals. Next are cited the

techniques used:

• BM4D (Maggioni et al., 2013)

• 3D median ﬁlter (Jiang and Crookes, 2006)

• Ellipsoid (Yang et al., 2008)

After the ﬁrst stage, one of the outputs of these

techniques is chosen randomly. Then it is passed by a

mutation operator, also in a random way and changes

are realized in the image initially recovered by one of

the initial methods. As Mutation operators was used

three types:

• Intensity change: is a linear operation that con-

sists of multiplying all the pixels of the image by

the same numerical factor.

• Gaussian ﬁlter: the ﬁlter that has the effect of

smoothing the image artifact through a Gaussian

function.

• Average ﬁlter: the technique that allows the smoo-

thing of noises in images by means of calculating

the average of all the ﬁlters of a given vicinity for

each pixel of the original image.

At the end of this stage the resulting image is ad-

ded to the population. Then the mutation process is

repeated until the population reaches the chosen size.

Thus, a hybrid population is formed, constituted of

the output of the three methods of suppression of ini-

tial noises plus the images that went through the mu-

tation process.

The HGA runs for a ﬁxed time, in which the popu-

lation continues to evolve while there is no changes in

the best individual to a maximum number of interacti-

ons. By reaching maximum number of interactions,

the entire population is restarted while only the best

individual is preserved. Posteriorly the population is

created again by the same process already mentioned.

An intermediate population twice the size of the

initial population is created during the process of evo-

lution formed by the current population plus the new

individuals generated. These new individuals are cre-

ated through operators crossover where the parents’

selection is made via the tournament. Shortly after

the parents were chosen, a new operator crossover is

randomly selected for the generation of a new indi-

vidual (son). For this are cited below the three types

available for the choice:

• Operator of a line point: randomly choose a line

of pixels in the image, then all the pixels above

it will come from one parent and the other pixels

that are below it will come from the other parent.

• Operator of a column point: approach similar to

the ﬁrst, but the image is divided by a column rat-

her than a line.

• Uniform Operator: each pixel of the image is cho-

sen randomly from one of the parents with 50 %

chance of the value chosen to be from either pa-

rent.

Once created, the new individual can still be sub-

mitted by a local search operator, a process that has

purpose improve the ﬁnal quality of the solution by

means of transformations in the individual, case sa-

tisﬁed the condition that a real number generated by

the algorithm in the execution that is equal to a value

within the range of 0 to 1 in the algorithm be less than

the local search rate chosen, it will go through one of

the artifact suppression operators already mentioned

in the initial step: BM4D, Median Filter or Ellipsoid.

New Bioinspired Filter of DICOM Images

261

With all intermediate population completed, indi-

viduals are ordered by ﬁtness, from the ﬁrst individu-

als selected in a population of the size chosen at the

outset to form the main population of the HGA for

the next evolution step, where the algorithm veriﬁes if

there are no changes in the best individual of the po-

pulation during a chosen number of executions of the

evolution. Case the best individual does not change

after a maximum number of iterations, so this popu-

lation is restarted. A ﬂow diagram of the execution of

the algorithm is shown in ﬁgure 2.

Figure 2: Flowchart of the algorithm. Source: Own author.

At the outset they begin to form the main popu-

lation of the HGA3D for the next evolutionary step,

where the algorithm checks if there is no change in

the best individual of the population during a chosen

number of evolution executions. If the best individual

does not change after a maximum number of Iterati-

ons, then this population is restarted. A ﬂowchart of

the algorithm execution is shown below.

5 RESULTS

In this chapter the results of the quantitative analysis

of the results will be presented through the evalua-

tion metrics. The comparison established is related to

other methods of ﬁltering three-dimensional images:

3D median and ellipsoid.

The table 1 refers to the amount of MSE for each

image after the ﬁltering, establishing values. In the

column 1 shows the percentage of image degradation,

in column 2 the noise mean, and columns 3, 4 and

5 the respective MSE values obtained for the ﬁlters

of the median 3d, ellipsoid and the proposed ﬁltering

method HGA3D.

Table 1: Evaluation of the result through MSE.

Gaussian additive noise

Noise (MSE) Median Ellipsoid HGA3D

10% (0.0062) 0.0102 0.0102 0.0747

20% (0.0268) 0.0112 0.0189 0.0025

30% (0.0307) 0.0120 0.0115 0.0747

40% (0.0336) 0.0145 0.0073 0.0165

Average 0.0199 0.0119 0.0187

Table 2: Evaluation of the result through PSNR.

Gaussian additive noise

Noise (PSNR) Median Ellipsoid HGA3D

10% (65.61) 72.58 72.93 73.85

20% (61.22) 69.83 69.93 72.76

30% (58.00) 67.07 67.68 71.24

40% (56.06) 65.16 65.69 69.88

Average 68.66 69.05 71.93

Table 3: Evaluation of the result through SSIM.

Gaussian additive noise

Noise (SSIM) Median Ellipsoid HGA3D

10% (0.2651) 0.4810 0.5053 0.8600

20% (0.1076) 0.2946 0.2428 0.8004

30% (0.0955) 0.2319 0.2456 0.6303

40% (0.0887) 0.1826 0.2357 0.5374

Average 0.2875 0.3773 0.7070

In the tables 2 and 3 are related to the qualitative

analysis of PSNR and SSIM. Featuring a design simi-

lar to table 2.

The ﬁrst analysis was done by the MSE metric,

presented in only one case the ﬁlter type HGA3D as

best. However, taking into account the relevance of

this type of meter the best results are those whose

values are the smallest, on the other hand it has the

contestable conﬁdence level of this metric, making it

present the need of comparison with new forms.

In the table 2 is shown an evaluation using a better

metric, this metric demonstrates in numerical data an

approximation of the human perception of the quality

of reconstruction, where not necessarily, but in most

cases the larger PSNR values represent a better recon-

struction of the image.

When comparing the resulting values demonstra-

ted below it is clear the superiority of the data re-

sulting from the proposed method. With efﬁciency

in 100 % of the cases tested in this approach, it is de-

monstrated in the table that in only one case, the va-

BIODEVICES 2018 - 11th International Conference on Biomedical Electronics and Devices

262

lues were close to the genetic algorithm model. After

is realized the difference in values resulting from the

methods stay distant, in addition, it is also remarka-

ble the difference between the average of the HGA3D

with the means of the other competing methods.

The table 3 demonstrates the analysis using the

most accurate evaluative metric currently used, SSIM.

This metric improves traditional methods, that show

inconsistent with human visual perception.

The results demonstrated here by the tables prove

that the combination of several artifact removal

techniques show up very favorable in most images, in

addition, the few amount of limitations of the HGA3D

provides a multitude of options for changing parame-

ters and providing improvements in the ﬁnal image.

As a visual example of the obtained results, it is

shown in ﬁgures 3, 4, 5 and 6. In each ﬁgure, four

images are observed, one referring to the slice added

with noise and another three are results of the me-

dium, ellipsoid and HGA3D ﬁltering. In ﬁgure 3 it

has been the image corrupted with gaussian artifact

and a standard deviation of 10%. In the other ﬁgures

differ in the standard deviation of 20%, 30% and 40%

respectively.

Figure 3: Image corrupted with white additive Gaussian ar-

tifact and standard deviation = 10%.

In ﬁgure 3 was observed that when applying the

noise with low deviation, 20 % , it is not visually per-

ceptible the difference of HGA3D in relation to the

others. However, in Figures 4, 5 and 6 the difference

between the proposed ﬁlter and the other two ﬁlters

that serve as a basis for verifying the quality.

Figure 4: Image corrupted with white additive Gaussian ar-

tifact and standard deviation = 20%.

Figure 5: Image corrupted with white additive Gaussian ar-

tifact and standard deviation = 30%.

6 DISCUSSION

With the introduction of the ﬁlter it is evident that

there is an improvement of resolution in both images,

making them more interesting for the observation of

the image.

In the table 1, it was observed that in three items

the ellipsoid obtained the most efﬁcient ﬁltration con-

dition. The proposed method presented only a signiﬁ-

New Bioinspired Filter of DICOM Images

263

Figure 6: Image corrupted with white additive Gaussian ar-

tifact and standard deviation = 40%.

cant result in the percentage of image degradation of

20 % the best performance. However, the MSE may

exhibit similarity failure.

Thus, the efﬁciency of the HGA3D method is de-

monstrated when compared to the others exposed in

tables 2 and 3, using PSNR and SSIM. Demonstra-

ting the ﬁnal image after ﬁltering that most closely

resembles the original image and provides an incre-

ase in quality.

7 CONCLUSION

There are several techniques for developing DICOM

image ﬁltering, this study applies the hybrid method

of genetic algorithm, in which the method obtains op-

timal ﬁltering and minimizes artifacts.

The efﬁciency of the model adopted as a ﬁlter is

the result of the architecture that is found distributed

in a selective and evolutionary way in two stages. The

ﬁrst stage consists of the BM4D ﬁltering, the 3d me-

dian ﬁlter and the ellipsoid ﬁlter. The second stage is

formed by the application of operators of simple mu-

tations in the previously recovered image, for that was

used: intensity change, gaussian ﬁlter and average ﬁl-

ter.

As comparison the MSE, PSNR and SSIM was

used to estimate the ﬁltering efﬁciency of the restored

images. It was observed experimentally that the adop-

ted ﬁlter is efﬁcient and robust presenting indexes bet-

ter than the others in the PSNR and SSIM.

With the study of the HGA3D can generate more

advances and minimize the artifacts, resulting in a

better performance in the system. The disadvantage

is the limitations of techniques for the random values,

that make it difﬁcult the optimal value deﬁned in the

ﬁltering

In order to apply more efﬁcient methods of recon-

struction of DICOM images, it is intended in future

works to approach the methods with the application

of new ﬁlters to increase efﬁciency. As an example

one has the artiﬁcial intelligence in one of the stages.

ACKNOWLEDGEMENTS

The elaboration of this work would not have been pos-

sible without the collaboration of the Research Group

on Intelligent Engineering and Computing for Advan-

ced Innovation and Development (GECAD) of the In-

stitute of Engineering, Polytechnic Institute of Porto,

Portugal

2

.

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