Medical imaging plays a vital role in medical diagnosis and treatment. However, distinct imaging modality yields information only in limited domain. Studies are done for analysis information collected from distinct modalities of same patient. This led to the introduction of image fusion in the field of medicine and the progression of image fusion techniques. Image fusion is characterized as the amalgamation of significant data from numerous images and their incorporation into seldom images, generally a solitary one. This fused image will be more instructive and precise than the indi- vidual source images that have been utilized, and the resultant fused image comprises paramount information. The main objective of image fusion is to incorporate all the essential data from source images which would be pertinent and comprehensible for human and machine recognition. Image fusion is the strategy of combining images from distinct modalities into a single image [1]. The resultant image is utilized in variety of applications such as medical diagnosis, identification of tumor and surgery treatment [2]. Before fusing images from two distinct modalities, it is essential to preserve the features so that the fused image is free from inconsistencies or artifacts in the output.

1. Hybrid Pixel-Based Method
for Multimodal Medical Image Fusion
Based on Integration of Pulse-Coupled
Neural Network (PCNN) and Genetic
Algorithm (GA)
R. Indhumathi, S. Nagarajan, and K. P. Indira
1 Introduction
Medical imaging plays a vital role in medical diagnosis and treatment. However,
distinct imagingmodalityyields informationonlyinlimiteddomain. Studies aredone
for analysis information collected from distinct modalities of same patient. This led to
the introduction of image fusion in the field of medicine and the progression of image
fusion techniques. Image fusion is characterized as the amalgamation of significant
data from numerous images and their incorporation into seldom images, generally
a solitary one. This fused image will be more instructive and precise than the indi-
vidual source images that have been utilized, and the resultant fused image comprises
paramount information. The main objective of image fusion is to incorporate all the
essential data from source images which would be pertinent and comprehensible for
human and machine recognition. Image fusion is the strategy of combining images
from distinct modalities into a single image [1]. The resultant image is utilized in
variety of applications such as medical diagnosis, identification of tumor and surgery
treatment [2]. Before fusing images from two distinct modalities, it is essential to
preserve the features so that the fused image is free from inconsistencies or artifacts
in the output.
Medical images canbeobtainedfromdistinct modalities suchas computedtomog-
raphy (CT), magnetic resonance imaging (MRI), positron emission tomography
(PET), single-photon emission tomography (SPECT) and X-ray. For instant, X-ray
R. Indhumathi (B) · S. Nagarajan
EEE Department, Jerusalem College of Engineering, Chennai 600100, India
e-mail: indhuraja.phd@gmail.com
S. Nagarajan
e-mail: nagu_shola@yahoo.com
K. P. Indira
ECE Department, BVC Engineering College, Allavaram, India
e-mail: kpindiraphd@gmail.com
© Springer Nature Singapore Pte Ltd. 2021
S. Patnaik et al. (eds.), Advances in Machine Learning and Computational Intelligence,
Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-15-5243-4_82
853

2. 854 R. Indhumathi et al.
and computed tomography (CT) are used to provide information about dense struc-
tures like bones, whereas magnetic resonance imaging (MRI) is used to provide
information about soft tissues, while positron emission tomography (PET) provides
information about metabolic activity taking place within the body. Hence, it necessi-
tates to integrate information from distinct images into a single image for a compre-
hensive view. Multimodal medical image fusion helps to diagnose the disease and
also reduces the cost of storage by amalgamating numerous source images into a
single image.
Image fusion can be accomplished at three levels—pixel level, feature level and
decision level [3]. Pixel-level image fusion is usually performed by combining pixel
values from individual source images. Feature-level image fusion performs fusion
only after segmenting the source image into numerous features such as pixel intensity
and edges. Decision-level image fusion is a high-level fusion strategy which utilizes
fuzzy rule, heuristic algorithms, etc. In this paper, image fusion has been performed
on pixel level owing to their advantages such as easy implementation and improved
efficiency.
Image fusion techniques are partitioned into two major categories—spatial-
domain and frequency-domain techniques [4, 5]. Spatial-domain techniques are
further categorized into average, maximum fusion algorithms and principle compo-
nent analysis. Frequency-domain techniques are further categorized into pyramid-
based decomposition and wavelet transforms.
Averaging method is a simple image fusion strategy where the output is deter-
mined by calculating the average value of each pixel [6]. Though easier to under-
stand, averaging method yields output with low contrast and washout appearance.
Choosing maximum fusion rule selects the maximum pixel value from the source
images as the output. This in turn yields a highly focused output [7]. PCA is a
vector space transformation methodology which reduces multi-dimensional datasets
to lower dimensions which is a powerful tool to analyze graphical data since elucida-
tion is tedious for data of higher dimensions. Frequency-domain strategies are further
categorized into pyramidal method and wavelet-based transforms. Pyramidal method
consists of bands of source images. Each level will be usually smaller compared to its
predecessor. This results in higher levels concentrated on higher spatial frequencies.
Pyramidal method is further classified into [8] Gaussian pyramid method [9], Lapla-
cian pyramid method, ratio of low-pass pyramid method and morphological pyramid
method. Gaussian pyramid method is a low pass filtered version of its predecessor.
LP is a bandpass filtered version of its predecessor. In ratio of low-pass pyramid, the
image at each level is the ratio between two successive levels in Gaussian pyramid
method. Morphological pyramid strategy used morphological filters to extract the
details of an image without causing any adverse effects.
Wavelet image fusion techniques are further classified into [10] discrete wavelet
transform (DWT) [11], Stationary wavelet transform (SWT) [12], non-subsampled
contourlet transform (NSCT). DWT technique decomposes an image into low-
and high-frequency components. Low-frequency components provide approxima-
tion information, while high-frequency components provide detailed information
contained within an image. DWT strategy suffers from a major drawback called shift

3. Hybrid Pixel-Based Method for Multimodal Medical Image Fusion … 855
variance. In order to provide a shift-invariant output, Stationary wavelet transform
(SWT) was introduced. This technique accomplishes upsampling strategy which
results in the decomposed image which has the same size as the input image. Though
these wavelet transforms perform well, they failed to perform well along edges. To
provide information along edges, non-subsampled contourlet transform (NSCT) was
proposed which is a geometric analysis procedure which increases localization, shift
invariance, etc. In spite of providing various advantages, NSCT technique requires
proper filter tuning and proper reconstruction filters for any particular application
[13]. To overcome the above disadvantages, neural technique strategies were intro-
duced. Pulse-coupled neural network (PCNN) is a neural network technique evolved
by utilizing the synchronous pulse emergence from cerebral visual cortex of some
mammals.
Pulse-coupled neural network (PCNN) is a most commonly used image fusion
strategy for medical diagnosis and treatment [14]. Initially, manual adjustment was
done to tune PCNN variables. Lu et al. [14] have utilized a distinct strategy where
pulse-coupled neural network (PCNN) was optimized by utilizing multi-swarm fruit
fly optimization algorithm (MFOA). Quality assessment was utilized as hybrid fitness
function which enhances the performance of MFOA. Experimental results illustrate
the effectiveness of the proposed strategy.
Gai et al. [15] have put forward a novel image fusion technique which utilized
pulse-coupled neural network (PCNN) and non-subsampled shearlet transform
(NSST). Initially, the images were decomposed by utilizing non-subsampled shearlet
transform (NSST) strategy. This decomposes the image into low- and high-frequency
components. Low-frequency components have been fused by utilizing improved
sparse representation method. This technique eliminates the detailed information by
using Sobel operator, while information preservation has been done by using guided
filter. High-frequency components have been fused by utilizing pulse-coupled neural
network (PCNN) strategy. Finally, inverse transform is done to yield the fused output.
The effectiveness of the proposed strategy has been validated against seven different
fusion methods. The author has also fused information from three distinct modalities
to justify the superiority of the proposed method. Subjective and objective analyses
illustrate the effectiveness of the proposed method.
A multimodal fusion approach which utilizes non-subsampled shearlet trans-
form (NSST) and simplified pulse-coupled neural network model (S-PCNN) was
put forward by Hajer et al. [16]. Initially, the images were transformed into YIQ
components. The images were initially disintegrated into low- and high-frequency
components using NSST strategy. The low-frequency components were fused using
weight region standard deviation (SD) and local energy, and high-frequency compo-
nents are fused by utilizing S-PCNN strategy and finally, inverse NSST and inverse
YIQ technique. The final discussion illustrates that the proposed strategy outper-
forms quantitatively in terms of performance measures such as mutual information,
entropy, SD, fusion quality and spatial frequency.
Jia et al. [17] have put forward a novel framework which utilized improved adap-
tive PCNN. PCNN is a technique that emerged from the visual cortex of mammals
and has proved to be very suitable in the field of image fusion. The source images

4. 856 R. Indhumathi et al.
were initially fed to the parallel PCNN, and the gray value of the image was utilized
to trigger PCNN. Meanwhile, sum-modified Laplacian was chosen as the evaluation
function, and the linking strength of neuron which corresponds to PCNN was evalu-
ated. The ignition map was generated after ignition of PCNN. The clearer part of the
images was chosen to yield the fused image. Quantitative and qualitative analyses
illustrated that the proposed strategy outperformed than the existing strategies.
In this paper, Wang et al. [18] have put forward an image fusion technique which
utilized discrete wavelet transform (DWT) and dual-channel pulse-coupled neural
network (PCNN). For fusing low-frequency coefficients, choosing maximum fusion
rule has been utilized, while spatial frequency of high-frequency components has
been chosen to motivate dual-channel PCNN. Finally, inverse DWT has been utilized
to yield the fused image. Visual and quantitative analyses illustrated the superiority
of the proposed approach than other image fusion strategies.
Arif et al. [19] proposed an existing image fusion strategies that lacked the capa-
bility to produce a fused image which could preserve the complete information
content from individual source images which utilized combination of curvelet trans-
form and genetic algorithm (GA) to yield a fused image. Curvelet transform helped in
preserving the information along the edges, and genetic algorithm helped to acquire
the fine details from the source images. Quantitative analysis demonstrated that the
proposed strategy outperformed than the existing baseline strategies.
Fu et al. [20] have put forward a novel image fusion approach which utilized
non-subsampled contourlet transform (NSCT) and pulse-coupled neural network
(PCNN) jointly in image fusion algorithms. High- and low-frequency coefficients
have been processed using modified PCNN. Determining the degree of matching
between input images is utilized in fusion rules. Finally, inverse NSCT has been
employed to reconstruct the fused image. Experimental analysis illustrated that the
proposed strategy outperformed wavelet, contourlet and traditional PCNN methods
in terms of higher mutual information content. Also, the proposed strategy preserved
edge as well as texture information, thereby including more information content in
the fused image. The author concluded by stating that research about selection of
parameters for image fusion should be performed deeply.
Image fusion is the strategy in which the input from multiple images is combined
to yield an efficient fused image. Lacewell et al. [21] have put forward a strategy
which utilized combination of discrete wavelet transform and genetic algorithm to
produce an efficient fused image. DWT has been utilized to extract features, while
genetic algorithm (GA) has been utilized to yield an enhanced output. Quantitative
and comparison analyses illustrated that the proposed strategy produced superior
results in terms of mutual information and root mean square error.
Wang et al. [22] have put forward a novel image fusion approach which utilizes
pulse-coupled neural network and wavelet-based contourlet transform. In order to
motivate PCNN, spatial high frequency in WBCT has been utilized. High-frequency
coefficients can be selected by utilizing weighted method of firing times. Wavelet
transform strategies perform better at isolated discontinuities but not along curved
edges especially for 3D images. In order to overcome the above drawbacks, PCNN
hasbeenutilizedwhichperformsbetterforhigher-dimensionalimages.Experimental

5. Hybrid Pixel-Based Method for Multimodal Medical Image Fusion … 857
analysis illustrated that WBCT-PCNN performed better from both subjective and
objective analyses.
From the literature survey, it is inferred that combination of two distinct image
fusiontechniquesprovidesbetterresultsintermsofbothqualityandquantity.Though
the solution may be obtained by utilizing any image fusion strategy, the optimal
solution can be obtained only by utilizing genetic algorithm (GA). Hence, an attempt
has been made to integrate the advantages of both PCNN and GA to yield an output
image from both quality and quantitative analyses.
The rest of the paper is organized as follows: Sect. 2 provides a detailed expla-
nation about pulse-coupled neural network (PCNN), Sect. 3 illustrates about the
proposed methodology, the proposed algorithm is provided in Sect. 4, qualitative
and quantitative analyses have been provided in Sect. 5 and conclusion has been
provided in Sect. 6.
2 Pulse-Coupled Neural Network (PCNN)
A new type of neural network, distinct from traditional neural network strategies,
is pulse-coupled neural network (PCNN) [23]. PCNN is developed by utilizing the
synchronous pulse emergence from cerebral visual cortex of some mammals. A
gathering of neurons is usually associated to frame PCNN. Each neuron correlates
to a pixel value whose intensity is contemplated as external stimulant. Every neuron
interfaces with another neuron in such a manner that a single-layer two-dimensional
cluster of PCNN is constituted. When linking coefficient beta is zero, every neuron
pulses naturally due to external stimulant. When beta is nonzero, the neurons are
associated mutually. At a point, when the neuron fires, its yield subscribes the
other adjacent neurons leading them to pulse before the natural period. The yield
of captured neurons influences the other neurons associated with them to change the
internal activity and the outputs. When iteration terminates, the output of every stage
is added to get an aggregate yield which is known as the firing map.
There are two primary issues in the existing image fusion strategies [24]. Firstly,
pyramid and wavelet transform strategies treat each pixel in an individual manner
rather than considering the relationship between them. Further, the images should
be entirely registered before fusion.
In order to overcome the above drawbacks, PCNN has been proposed [25]. Fusion
strategy usually takes place in two major ways—either by choosing the better pixel
value or by outlining a major–minor network for different networks, thereby choosing
the yield of the first PCNN as the fusion result.
The pulse-coupled neural network comprises three compartments: receptive field,
linking part or modulation and pulse generator.
Receptive field is an essential part which receives input signals from neigh-
boring neurons and external sources. It consists of two internal channels—feeding
compartment (F) and linking compartment (L). Compared to feeding compartment,
the linking inputs have quicker characteristic response time constant. In order to

6. 858 R. Indhumathi et al.
generate the total internal activity (U), the biased and multiplied linking inputs are
multiplied with the feeding inputs. The net result constitutes the linking/modulation
part. At last, pulse generator comprises step function generator and a threshold signal
generator.
The ability of neurons in the network to respond to external stimulant is known
as firing which enables the internal activity of neuron to exceed a certain threshold
value. Initially, the yield of neuron is set to 1. The threshold value starts rotting till the
next internal activity of the neuron. The output generated is then iteratively nourished
back with a delay of single iteration. As soon as the threshold exceeds the internal
activity (U), the output will be reset to zero. A temporal series of pulse outputs are
generated by PCNN after n number of iterations which carries the data about the
input images. Input stimulus which corresponds to pixel’s color intensity is given to
feeding compartment. The pulse output of PCNN helps to make a decision on the
content of the image.
Initially, double-precision operation is performed on the acquired input CT and
PET images. In order to reduce the memory requirements of an image, unsigned
integers (unit 8 or unit 16) can be utilized. An image whose information lattice has
unit 8 and unit 16 class is known as 8-bit image and 16-bit image, respectively.
Though the measure of colors emitted cannot be differentiated in a grayscale
image, the aggregate sum of radiated light for each pixel can be partitioned since a
small measure of light is considered as dark pixels, while more amount of light is
considered as bright pixels. On conversion from RGB to grayscale image, the RGB
value of each pixel is ought to be taken and made as the yield of a solitary value
which reflects the brightness of the pixel.
Normalization (norm) converts the scale of pixel intensity values and is known
as contrast stretching or histogram stretching. Normalization can be determined by
using the following formula
I norm = (I abs − I min)/(I max − I min)
where
abs represents absolute value
min represents minimum value
max represents maximum value
Each neuron in firing pulse model comprises receptive field, modulation field and
pulse generator.
Two important features are necessary to fire a pulse generator—spiking cortex
model(SCM)andsynapticweightmatrix.SCMhasbeendemonstratedincompliance
with Weber–Fechner law, since it has higher sensitivity for low-intensity stimulant
and lower sensitivity for high-intensity stimulant. In order to improve the perfor-
mance and make the output reachable, synaptic weight matrix is applied to linking
field and sigmoid function is applied in firing pulse. PCNN comprises neuron capture

7. Hybrid Pixel-Based Method for Multimodal Medical Image Fusion … 859
property which causes any neuron’s firing to make the nearby neurons whose lumi-
nance is similar to be fired. This property makes the information couple and transmis-
sion to be automatically acknowledged which makes PCNN satisfactory for image
fusion.
In this model, one of the original images is chosen as input to the main PCNN
network randomly and another image as input to the subsidiary network. The firing
information about the subsidiary network is transferred to the main PCNN network
with the help of information couple and transmission properties. By doing so, image
fusion can be figured out. When a neuron is fired, the firing information about the
subsidiary network is communicated to the adjacent neurons and neurons of the main
PCNN network. The capturing property of PCNN makes it suitable for image fusion.
Eventually, the output obtained is transformed to unit 8 format, and finally, the fused
image is obtained.
3 Genetic Algorithm
Genetic algorithm (GA) is a heuristic search algorithm used to solve the problems
of optimization [26]. The essence of GA is to simulate the evolution of nature, i.e., a
processinwhichaspeciesexperiencestheselectiveevolutionandgeneticinheritance.
At first, a random group is formed, and then, with mutual competition and genetic
development, the group goes through the following operation process: selection,
crossover, mutation, etc. The subgroup who has better fitness will survive and form
a new generation. The process cycles continuously until the fittest subgroups are
formed. The surviving groups are supposed to well adapt to the living conditions.
Thus, the genetic algorithm is actually a type of random search algorithm. Moreover,
it is nonlinear and parallelizable, so it has great advantages when compared with the
traditional optimization algorithm.
Four entities that help to define a GA problem are the representation of the candi-
date solutions, the fitness function, the genetic operators to assist in finding the
optimal or near optimal solution and specific knowledge of the problem such as
variables [27]. GA utilizes the simplest representation, reproduction and diversity
strategy. Optimization with GA is performed through natural exchange of genetic
material between parents. Offspring are formed from parent genes. Also, fitness of
offspring is evaluated. The best-fitting individuals are only allowed to breed.
Image fusion based on GA consists of three types of genetic operations—
crossover, mutation and replication [28]. The procedure is as follows:
1. Encode the unknown image weight and define the objective function f (xi). SML
is used as the fitness function using GA.
2. N stands for the initial population size of fusion weight. Pm represents the
probability of mutation and Pc the probability of crossover.
3. Generate randomly a feature array whose length is L to form an initial fusion
weight group.

8. 860 R. Indhumathi et al.
4. Followthestepsbelowandconductiterativeoperationuntilterminationcondition
is achieved.
(a) Calculate the adaptability of an individual in the group.
(b) On the basis of adaptability, Pc and Pm, operate crossover, mutation and
replication.
5. The best-fitting individuals in the surviving subgroup are elected as the result.
So, the optimal fusion weight is obtained.
From the steps above, the optimal fusion weights of the images to be fused can
be obtained after several iterations. However, since this method does not take into
account the relationship between the images to be fused, a lot of time is wasted to
search for the fusion weights in the clear focal regions, which leads to low accuracy
of the fusion.
4 Optimization of Image Fusion Using PCNN aND GA
A new image fusion algorithm using pulse-coupled neural network (PCNN) with
genetic algorithm (GA) optimization has been proposed which uses the firing
frequency of neurons to process the image fusion in PCNN. Aiming at the result
of image fusion being affected by neuron parameters, this algorithm is dependent on
the parameters image gradient and independent of other parameters. The PCNN is
built in each high-frequency sub-band to simulate the biological activity of human
visual system. On comparing with traditional algorithms where the linking strength
of each neuron is set constant or continuously changed according to features of each
pixel, here, the linking strength as well as the linking range is determined by the
prominence of corresponding low-frequency coefficients, which not only reduces
the calculation of parameters but also flexibly makes good use of global features of
images.
The registration has been conducted to the images to be fused, and the image
focusing is different; the fusion coefficients (Q) of the two pixels in the same position
have a certain hidden relationship [29]. Besides, the constraint condition of the fusion
coefficient is Q1 + Q2 = 1, Q1 > 0, Q2 > 0, so modeling for either coefficient is
enough. Therefore, in the process of wavelet transformation, the PCNN for fusion
weight coefficient (Q) of each pixel of each layer in a hierarchical order from high
to low has been built. Q stands for fusion coefficient and S stands for hidden state.
In this model, S is defined as three states, i.e., S ∈ {1, 2, 3}. When the pixel is
in the focused region of image 1, or when located much nearer the focal plane of
image 1 than that of image 2, S is set as 1. When the pixel is in the focused region
of image 2, or if it is located much nearer the focal plane of image 2 than that of
image 1, S is set as 3. If the pixel is not in the focused region of image 1 or image 2
and there is no obvious difference between the distances from focal plane of image 1
and that of image 2, S is set as 2.

9. Hybrid Pixel-Based Method for Multimodal Medical Image Fusion … 861
In addition, if the fusion coefficient in the neighboring region is greater than 0.9
or less than 0.1, then S is set to be 1 or 3. The state transfer matrixes from the parent
node Si to the sub-node Si + 1 are defined as follows.
According to the fact that details of the low frequency in the clear area are more
than that in unclear area, this tries to get fusion weights from high scale to low scale
by using GA after the process of wavelet transformation. Meanwhile, the author
constructs a PCNN for each fusion weight in each layer and figures out its hidden
states layer by layer with the help of the fusion weights calculated by GA. PCNN
is a single-layered, two-dimensional, laterally connected neural network of pulse-
coupled neurons where the inputs to the neuron are given by feeding and linking
inputs. Feeding input is the primary input from the neurons receptive area. The neuron
receptive area consists of the neighboring pixels of corresponding pixel in the input
image. Linking input is the secondary input of lateral connections with neighboring
neurons. The difference between these inputs is that the feeding connections have
a slower characteristic response time constant than the linking connections. Guided
by the hidden states of the fusion weights in the upper layer, the author gets the
values directly from the clear area of the next layer without GA. In this way, the
population size in the process of GA is reduced, which contributes a lot to improving
the precision of calculation in the same amount of time.
5 Algorithm
Step 1: Apply PCNN to N layers of the image and then figure out the fusion
coefficient of Layer N by using GA. Set variate i = 0.
Step 2: Search the neighboring region of Layer N − i for the pixels, whose fusion
coefficients are greater than 0.9 or less than 0.1. Set Si = 1 or 3.
Step 3: Search Layer N − (i + 1) for the pixels whose parent node are Si = 1 or
3, and then, the Qs of these pixels are set to be 1 or 0, and accordingly, Si + 1 are
set to be 1 or 3.
Step 4: Search Layer N − (i + 1) and find out the pixels whose parent node are
Si = 2 and then apply GA to work out the fusion coefficients of these pixels. Set
their Si + 1 to be 2, and set variate i = i + 1. After that, go back to Step 2 and
circulate the operation.
Step 5: Circulate Step 2, Step 3 and Step 4. Work out the fusion coefficients until
the last layer.

10. 862 R. Indhumathi et al.
6 Results and Discussion
6.1 Quantitative Analysis
Percentage Residual Difference (PRD): Percentage residual difference reflects the
degree of deviation between source image and the fused image [24]. Lower the value
of PRD, higher is the quality of the image. On comparing PRD values of PCNN
and GA with PCNN, it is inferred that PCNN and GA offer better results for all 16
datasets (Table 1).
Table 1 Objective analysis of PCNN and hybridization of PCNN and GA
CT PET PCNN
Gradient
PCNN
& GA
A1 A2 A3 A4
1
B1 B2 B3 B4
2
C1 C2 C3 C4
3
D1 D2 D3 D4
4
E1 E2 E3 E4
5
F1 F2 F3 F4
6
G1 G2 G3 G4
7
H1 H2 H3 H4
8
CT PET PCNN
Gradient
PCNN & GA
I1 I2 I3 I4
9
J1 J2 J3 J4
10
K1 K2 K3 K4
11
L1 L2 L3 L4
12
M1 M2 M3 M4
13
N1 N2 N3 N4
14
O1 O2 O3 O4
15
P1 P2 P3 P4
16

11. Hybrid Pixel-Based Method for Multimodal Medical Image Fusion … 863
Root Mean Square Error (RMSE): RMSE is a measure of difference between
predicted value and the actual value [25]. Lower the value of RMSE, higher is the
quality of the image. On comparing RMSE values of PCNN and GA with PCNN, it
is inferred that PCNN and GA offer better results for all 16 datasets (Table 2).
Peak Signal-to-Noise Ratio (PSNR): PSNR is the ratio between maximum
possible power of a signal and the power of corrupting noise affecting the image.
The quality of an image will be better if the value of PSNR is high. On comparing
PSNR values of PCNN and GA with PCNN, it is inferred that PCNN and GA offer
better results for all 16 datasets (Table 3).
Entropy: Entropy reflects the amount of information content which is available
in the fused image. Higher the value of entropy, higher is the quality of fused image.
On comparing entropy values of PCNN and GA with PCNN, it is inferred that PCNN
and GA offer better results for almost all datasets.
7 Conclusion and Future Scope
A new image fusion algorithm using pulse-coupled neural network (PCNN) with
genetic algorithm (GA) optimization has been proposed which uses the firing
frequency of neurons to process the image fusion in PCNN. The performance of
the proposed algorithm has been evaluated using sixteen sets of computed tomog-
raphy (CT) and positron emission tomography (PET) images obtained from Bharat
Scans. Qualitative and quantitative analyses demonstrate that “optimization of image
fusion using pulse-coupled neural network (PCNN) and genetic algorithm (GA)”
outperforms PCNN technique.
The proposed strategy can be extended to merge color images since color carries
remarkable information and our eyes can observe even minute variations in color.
With the emerging advances in remote airborne sensors, ample and assorted informa-
tionisaccessibleinthefieldsofresourceinvestigation,environmentalmonitoringand
disaster prevention. The existing strategies discussed in the literature survey intro-
duce distortion in color. The algorithm proposed can be extended to fuse remote
sensing images obtained from optical, thermal, multispectral and hyperspectral
sensors without any color distortion.
Multimodal medical image fusion has been implemented with static images in
the proposed work. At present, fusion of multimodal video sequences generated by
a network of multimodal sources is turning out to be progressively essential for
surveillance purposes, navigation and object tracking applications. The integral data
provided by these sensors should be merged to yield a precise gauge so as to serve
more efficiently in distinct tasks such as detection, recognition and tracking. From the
fused output, it is conceivable to produce a precise representation of the recognized
scene which in turn finds its use in variety of applications.

12. 864 R. Indhumathi et al.
Table 2 Comparison
analysis of PRD and RMSE
for PCNN and hybridization
of PCNN and GA
Percentage residual error (PRD)
Datasets PCNN (gradient) Hybridization of PCNN and GA
1 0.4273 3.3080e−008
2 0.3893 4.8480e−008
3 0.4878 4.8667e−008
4 0.4283 1.5920e−008
5 0.3807 5.0838e−008
6 0.4216 7.4327e−008
7 0.4041 7.2718e−008
8 0.3904 8.0547e−008
9 0.1121 6.9992e−008
10 46.6390 5.1606e−008
11 0.1795 6.5642e−008
12 6.9132 4.8696e−008
13 0.1654 4.4340e−008
14 1.1723 5.2005e−008
15 0.1393 6.8369e−008
16 1.5822e−004 7.0856e−008
Root mean square error (RMSE)
Datasets PCNN (gradient) Hybridization of PCNN and GA
1 0.0057 5.4255e−013
2 0.0054 6.9413e−013
3 0.0085 6.7392e−013
4 0.0094 2.2869e−013
5 0.0069 8.2856e−013
6 0.0091 8.7955e−013
7 0.0076 8.5858e−013
8 0.0070 8.2650e−013
9 0.0089 9.0324e−013
10 0.0098 7.6707e−013
11 0.0110 9.1334e−013
12 0.0085 8.2101e−013
13 0.0088 6.7181e−013
14 3.2809e−004 4.552e−013
15 0.0077 8.7183e−013
16 0.0049 5.7605e−013

13. Hybrid Pixel-Based Method for Multimodal Medical Image Fusion … 865
Table 3 Comparison
analysis of PSNR and entropy
for PCNN and hybridization
of PCNN and GA
Peak signal-to-noise ratio(PSNR)
Datasets PCNN (gradient) Hybridization of PCNN and GA
1 54.5123 55.7234
2 55.0001 57.2346
3 53.4523 54.8976
4 56.1234 57.1042
5 55.6321 57.1234
6 56.1235 57.8432
7 55.4567 56.7046
8 55.1732 56.5460
9 57.8432 58.4389
10 57.2341 58.8975
11 57.6574 59.1004
12 55.6978 57.9874
13 54.2054 55.5512
14 56.1800 58.1254
15 57.8358 58.2657
16 55.8526 57.2116
Entropy
Datasets PCNN (gradient) Hybridization of PCNN and GA
1 7.3120 8.1423
2 7.4250 8.4146
3 7.3690 8.0799
4 7.9854 8.3523
5 7.8453 8.0733
6 8.0001 8.0452
7 7.7785 8.3483
8 7.4567 8.4209
9 7.3001 8.2272
10 7.5254 7.6642
11 7.3001 7.3740
12 7.9784 8.1282
13 7.8546 8.1151
14 7.8945 8.2251
15 7.9000 8.0205
16 8.1234 8.6886

14. 866 R. Indhumathi et al.
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