Abstract
In biomedical field, embedded numerous sensing nodes can be used to monitor and interact with physical world based on signal analysis and processing. Data from many different sources can be collected into massive data sets via localized sensor networks. Understanding the environment requires collecting and analyzing data from thousands of sensors monitoring, this is big data environment. The application of biomedical image fusion for bigdata computing has strong development momentum, bigdata biomedical image fusion is one of key problems, so the fusion method study is a hot topic in the field of signal analysis and processing. The existing methods have many limitations, such as large delay, data redundancy, more energy cost, low quality, so novel fusion computing method based on spherical coordinate for bigdata biomedical image of WSN is proposed in this paper. In this method, the three highfrequency coefficients in wavelet domain of biomedical image are preprocessed. This preprocessing strategy can reduce the redundant ratio of bigdata biomedical image. Firstly, the highfrequency coefficients are transformed to the spherical coordinate domain to reduce the correlation in the same scale. Then, a multiscale model product (MSMP) is used to control the shrinkage function so as to make the small wavelet coefficients and some noise removed. The highfrequency parts in spherical coordinate domain are coded by improved SPIHT algorithm. Finally, based on multiscale edge of biomedical image, it can be fused and reconstructed. Experimental results indicate the novel method is effective and very useful for transmission of bigdata biomedical image, which can solve the problem of data redundancy, more energy cost and low quality.
1. Introduction
Advances in digital sensors, communications, computation, and storage have created huge collections of data. Trillions of bytes of data every day such as satellite images, driving directions, and image retrieval continually add new services. Digital data are being generated by many different sources in the field of signal analysis and processing, including digital imagers (telescopes, video cameras, MRI machines). Data from the many different sources can be collected into massive data sets via localized sensor networks, as well as the Internet. Understanding the environment requires collecting and analyzing data from thousands of sensors monitoring, this is big data environment. For example, modern medicine collects huge amounts of data about patients through imaging technology [13]. By applying data mining to data sets for large numbers of patients, medical researchers are gaining fundamental insights into the genetic and environmental causes of diseases, and creating more effective means of diagnosis. Bigdata biomedical image is one of important research focuses.
In biomedical field, embedded numerous sensing nodes (as Fig. 1) can be used to monitor and interact with physical world, such as Pulse Oximeter, Glucose Meter, Electro cardiogram (ECG), CT, MR, Social Alarm Devices, and so on. Wireless Sensor Network (WSN) can be used in all kinds of biomedical applications [47]. WSN can do telemonitoring of human physiological data, tracking and monitoring patients and doctors inside a hospital, drug administration in hospitals, and so on.
Fig. 1Embedded numerous sensing nodes in WSN
In WSN, data coming from multiple wireless sensor nodes are aggregated and they reach the same routing node to the sink [811]. The application of biomedical image fusion for bigdata computing has strong development momentum, especially in medical therapeutics, so the fusion method study is a hot topic. Generally, the fusion for bigdata biomedical image can be divided into 4 levels [1215]: signal, pixel, feature and symbol. According to fusion different levels, there are relative specific fusion methods. For the pixel and the feature, the fusion methods have principal component analysis, pyramidbased, wavelet transform, etc.
Discrete Wavelet Transform (DWT) can decompose the image into different scales. The wavelet coefficients have the following characteristics [1618]: lowfrequency subbands contain most of the energy of the original image, while the highfrequency coefficients take less energy and mostly in the image edge. In the same direction, the subband coefficients of the same level show some correlations. The probability that the amplitude of each high frequency subband coefficient steps down is big. According to the band, the coefficients form a tree structure from lowfrequency to high frequency.
For bigdata biomedical image, the existing methods have many limitations, such as large delay, data redundancy, more energy cost, and low quality.
Said [6] presented encoding implementation based on set partitioning in hierarchical trees (SPIHT), which is highly efficient in coding nontextured images. This encoding algorithm can be stopped at any compressed file size or let it run until the compressed file is a representation of a nearly lossless image. However, SPIHT coding has a serious drawback: repeated scan. This disadvantage makes encoding efficiency less. In recent years, many researchers [1923] proposed different improved SPIHT algorithm. However, those improvement methods for SPIHT algorithm only optimized the code scheme, but neglected the transform part.
Kassim [7] proposed SPIHTWPT coder scheme, which selected an optimal wavelet packet transformation basis for SPIHT. The selected basis could efficiently compact the highfrequency subband energy into as few trees as possible and avoid parental conflicts. This algorithm achieved effectively results for highly textured images.
Ansari [9] introduced CSPIHT algorithm to reduce the bit rate. In this method the contextual part of the image was encoded selectively on the high priority basis with a very low compression rate and the background of the image is separately encoded with a low priority and a high compression rate and they were recombined for the reconstruction of the image.
Paper [10] proposed highthroughput memoryefficient arithmetic coder architecture for the SPIHT image compression. Instead of using the list mechanism, this method used a breadthfirssearch algorithm to avoid rescanning the wavelet transform coefficients.
Khan [11] introduced a novel method of noreference objective quality assessment of wavelet coded images. This method was used to estimate the quantization errors in terms of MSE reduction in SPIHT coding algorithm. Many researches have proposed other improved algorithms about SPIHT.
We improve the SPIHT algorithm and optimize the transform part in this paper. In transform part, after analyzing the characteristics of the wavelet coefficients, we introduce a strategy to preprocess the coefficients. Firstly, the three highfrequency wavelet coefficients are transformed to spherical coordinate domain. In this way, the correlation between wavelet coefficients in the same decomposition scale can be further reduced. However, this strategy does not affect the correlation between wavelet coefficients in the different decomposition scales. MultiScale Model Product (MSMP) is used to control the wavelet shrinkage functions, which can remove the coefficients that have little effect on the visual and some noise information. This preprocessing strategy can reduce the redundant ratio of bigdata biomedical image. In coding part of biomedical image, the lowfrequency part contains the most energy of the image, and this part does not participate into the spherical transformation. Therefore, it can be encoded separately by DPCM. The highfrequency parts in spherical coordinate domain adopt the improved SPIHT coding algorithm. We introduce a matrix of maximum pixel (MMP) to overcome the rescan disadvantage of traditional SPIHT algorithm. When the threshold is updated, we just compare the new threshold to the coefficients in MMP. This mechanism avoids the comparison between the new thresholds to every pixel. The improved SPIHT algorithm greatly improves the fusion efficiency.
The biomedical image fusion with coding based on spherical coordinate domain contains the following steps: the image wavelet decomposition in different scale, the three highfrequency wavelet coefficients transforming to spherical coordinate domain, removing the coefficients by MSMP and encoding the image, transmitting to fusion center by WSN, making fusion in center, reconstructing the image by the processed coefficient.
2. Method
As we know, SPIHT algorithm is based on bit plane. It organizes the wavelet coefficients into spatial orientation trees. SPIHT with lists algorithm uses three different lists to store significant information of wavelet coefficients for image coding purpose [812]. Three lists are list of insignificant sets (LIS), list of insignificant pixels (LIP), and list of significant pixels (LSP). At first, SPIHT combines nodes into one set which is denoted as insignificant. With traveling each tree node, sets in the LIS are partitioned into four different subsets which are tested for significant state.
2.1. Basic principle
Discrete wavelet transform (DWT) can decompose the image into different scales. The wavelet coefficients have the following characteristics: lowfrequency subbands contain most of the energy of the original image, while the highfrequency coefficients take less energy and mostly in the image edge. In the same direction, the subband coefficients of the same level show some correlations. The probability that the amplitude of each high frequency subband coefficient steps down is big. According to the band, the coefficients form a tree structure from lowfrequency to high frequency (Fig. 2).
SPIHT algorithm is based on bit plane. This algorithm encodes following the importance of coefficients. It organizes the wavelet coefficients into spatial orientation trees (Fig. 3). SPIHT with lists algorithm uses three different lists to store significant information of wavelet coefficients for image coding purpose [6]. Three lists are list of insignificant sets (LIS), list of insignificant pixels (LIP), and list of significant pixels (LSP). At first, SPIHT combines nodes into one set which is denoted as insignificant. With traveling each tree node, sets in the LIS are partitioned into four different subsets which are tested for significant state [6].
Fig. 2Wavelet multiscale decomposition
Fig. 3Spatial orientation tree
2.2. Wavelet domain to spherical domain for signal analysis and processing
The human visual system is characterized in observing an image signal analysis and processing. We are more sensitive to low frequency of an image, while less sensitive to the high frequency parts. Based on this characteristic of the human eyes, discrete wavelet transform decomposes the image into different scales and processes the image in different resolutions. The coefficients in the same scales are great relevance. If we can reduce the correlations and reserve the main information, we will achieve high compression ratio when compressing an image. And we will reduce the image storage space and improve the efficiency of the transmission channel. From this viewpoint, the three high frequency coefficients in wavelet domain are transformed into spherical domain.
The signal $f\left(x\right)$ denotes an image. After DWT transformation, the image decomposes into a low frequency and three high frequencies. ${C}_{j,k}^{\left(1\right)}$ is the horizontal high frequency, ${C}_{j,k}^{\left(2\right)}$ is the vertical high frequency and ${C}_{j,k}^{\left(3\right)}$ is the diagonal high frequency. This paper transforms the three high frequencies to spherical coordinate domain:
where, the $j$, $k$ denote the coordinates of the coefficients.
After the spherical transformation, the coefficients in the wavelet domain are mapped to spherical coordinate domain. The corresponding relationship between the various components is shown as Fig. 4.
Spherical transform can further remove the correlations of the wavelet coefficients in the same scales. The energy of the three high frequencies in the wavelet domain is totally transformed to the radial components in the spherical domain. While the phase angles just retain the pixel location information. Therefore, we just process the radial components and decrease the complexity of the algorithm. Also it avoids directly process the wavelet coefficients.
Fig. 4Transform from wavelet domain to spherical domain
2.3. Multiscale module products in processing
Premise 1. In the wavelet domain, with the increase of decomposition scales, the wavelet coefficients of effective signal will increase or remain unchanged. While the wavelet coefficients of noise and unimportant coefficients will decrease.
Premise 2. Spherical transform reduces the correlation in the same scale. However, it does not change the correlation of the coefficients in different scales.
Based on the two premises mentioned above and inspired by the ideas of multiscale product in the wavelet domain, multiscale norm products (MSNP) in spherical domain are defined as follows:
Definition 1. The MSNP of pixel point ($x$, $y$) in spherical coordinates is as:
where $k$ is the decomposition scales.
The MSNP can enlarge the distance between effective signal and unimportant signal. It avoids the shortcoming that smaller wavelet coefficients are wrongly removed by traditional methods. Therefore, it will effectively remove the noise information and unimportant coefficients without loss of the important feature of the original image.
Theorem 1. The signal to noise ratio (SNR) and peak signal to noise ratio (PSNR) of the fusion image is defined as follows. If $\mathrm{P}\mathrm{S}\mathrm{N}\mathrm{R}>\lambda $ (noise ratio threshold, the default is 20 dB), the fusion result is valid:
Proof. Based on the above Eqs. (18) and (19), we can calculate the value of SNR and PSNR of the fusion image. Generally, if the PSNR of one image is larger than the noise ratio threshold $\lambda $ ( the default value of $\lambda $ is 20 dB), the image is clear and readable, otherwise, the image is vague. If PSNR $>$ (noise ratio threshold, the default is 20 dB), so the fused image is readable and acceptable, that is to say, the fusion result is valid.
2.4. The processing of wavelet coefficients of image signal
In the spherical domain, $R\left(j,k\right)$ takes the energy of the pixel point ($j$, $k$). The greater the radial components $\leftR\left(j,k\right)\right$ is, the more important of the image information which contains. The radial components whose absolute value is small contain the secondary information and some noise information. In order to achieve large compression ratio and do not affect the visual effect, this paper processes the radial components by softthreshold shrinkage:
where, $T$ denotes the wavelet shrinkage threshold.
As Fig. 5, there are different shrinkage strategies, we take the threshold acting on the MSMP. This algorithm avoids killing the small amplitude coefficients and can effectively preserve the useful information of the original image.
In the spherical coordinates, angle and the radial components are independent to each other. Small angle does not always correspond to small energy. If the $R\left(j,k\right)$ is zero, this indicate the point ($j$, $k$) signal energy is zero. Therefore, the corresponding two components $\theta \left(j,k\right)$ and $\phi \left(j,k\right)$ can be set to zero, which does not affect our recognition.
Fig. 5Several shrinkage threshold functions
2.5. Coding of Lowfrequency part of image signal
After wavelet transform, the energy carried by the low frequency almost closes to 90 % of the original image. In order to achieve a higher quality of the reconstructed image, we use the DPCM coding to process the low frequency coefficients. The size of the original image $f\left(x,y\right)$ is $M$×$N$, $LL\left(j,k\right)$ denotes the value of the pixel point ($j$, $k$) in the low frequency wavelet coefficients. We use the row $M/$2 and column $N/$2 to estimate other wavelet coefficients (as Fig. 6).
Firstly, keep $LL\left(j,N/2\right)$ and $LL\left(M/2,k\right)$ unchanged. Other coefficients are estimated by the following formula:
where, $j=\text{1,}\text{\u2026}\text{,}\text{}M$, $k=\text{1,}\text{\u2026}\text{,}\text{}N$.
Secondly, estimate the row $M/2$ and the column $N/2$:
Using the following formula to recover the low frequency coefficients:
Fig. 6DPCM coding in lowfrequency part
2.6. Coding of Highfrequency part of image signal
After being preprocessed, the coefficients of the high frequencies are transformed to spherical coordinate domain. The correlation between coefficients in the same scale is reduced. The radial components $R\left(j,k\right)$ are processed by soft threshold function. A lot of zero points are produced. This promises a high compression ratio.
We suppose that MMP is for Matrix of Maximum Pixel. $H$ is the set of coordinates of all spatial orientation tree roots (nodes in the highest pyramid level). $O\left(j,k\right)$ is the set of coordinates of all offspring of node ($j$, $k$). $D\left(j,k\right)$ is the set of coordinates of all descendants of the node ($j$, $k$). $L\left(j,k\right)$ is the set of coordinates of all nonchild descendants of the node ($j$, $k$), where $L\left(j,k\right)=D\left(j,k\right)O\left(j,k\right)$. $T\left(j,k\right)$ is the spatial orientation tree whose root is the node ($j$, $k$). $T$ is the maximum scan threshold ${2}^{n}$.
We introduce our improved algorithm, when scanning the list LIS, the matrix MMP will be established to preserve the largest pixel nodes of descendant of the set $D\left(j,k\right)$ and $L\left(j,k\right)$. Once the threshold updated, we just need to compare the new threshold with the corresponding coefficients of MMP. This method can effectively reduce the number of comparisons and improve scanning efficiency. The function used for testing set significant state is defined by the following formula:
where, $M$ is a set of coordinates.
Improved algorithm is as Fig. 7.
Fig. 7Improved SPIHT algorithm
2.7. Fusion computing method for image signal
The fusion computing rule of biomedical image is basically expressed by Eq. (10):
where, $I\left(x,y\right)$ is the fused image, ${I}_{1}\left(x,y\right)$ and ${I}_{2}\left(x,y\right)$ are two input images, $w\left(\cdot \right)$ is the discrete wavelet transform method, ${w}^{1}\left(\cdot \right)$ is the inverse discrete wavelet transform method, $\varphi $ is the fusion rule.
We use threshold $T$ and shrinkage function to process the high frequency coefficients. Hard threshold function is as:
Soft threshold function is as:
where $w\left(x,y\right)$ denotes wavelet coefficients, ${w}^{\mathrm{*}}\left(x,y\right)$ is wavelet coefficients after processing, $T$ is the threshold, $\mathrm{s}\mathrm{i}\mathrm{g}\mathrm{n}\left(\cdot \right)$ is sign function.
We process the low frequency and high frequency coefficients by inverse wavelet transform and reconstruct the image. We choose a class of orthogonal sample wavelets to be transformed. Dyadic wavelet transform in the direction of $k$ defines as follows:
where $I\left(x,y\right)\in {L}^{2}\left({R}^{2}\right)$, ${2}^{j}$ ($j=$1, 2,…) is the transform scale, $\psi \left(x,y\right)$ for the wavelet of two directions, and using Eq. (13), we get:
where $\theta \left(x,y\right)$ is the continuous sample scale function, the smoothing level of the filter depends on it. Binary gradient in the image is as follows:
We define the edge point under the different scales in the image fusion. We can find the local maximum of the different types under the different scales about the above gradient. It is given by Eq. (16):
Along the gradient direction ${A}_{2}^{j}I$ (Eq. (17)), ${M}_{2}^{j}I$ in Eq. (16) reach the local maximum. Under each corresponding scale, we can find the corresponding edge points with the gradient modulus (wavelet transform values), these local maximum in a collective form is expressed as the Eq. (17):
$\begin{array}{l}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\\ {P}_{{2}^{j}}\left(I\right)=\left\{{P}_{{2}^{j},i}=\left({x}_{i},{y}_{i}\right);{\nabla}_{{2}^{j}}I\left({x}_{i},{y}_{i}\right)\right\}.\end{array}$
Along the direction of ${A}_{{2}^{j}}I({x}_{i},{y}_{i})$, the value of ${M}_{{2}^{j}}I({x}_{i},{y}_{i})$ gets the maximum (${p}_{{2}^{j},i}=({x}_{i},{y}_{i})$). For a $J$level dyadic wavelet transform, the multiscale edge of the image is expressed as follows:
where ${S}_{{2}^{j}}I(x,y)$ is the lowpass component of the image $I\left(x,y\right)$ under the scale value ${2}^{J}$.
According to the above method, we obtain the maximum set ${P}_{{2}^{j}}^{1}\left(I\right)$ and ${P}_{{2}^{j}}^{2}\left(I\right)$ of the local gradient, that is the point (edge) representation of the image. The set contains the edge points of two images ${I}_{1}\left(x,y\right)$ and ${I}_{2}\left(x,y\right)$ in the same scale. In accordance with the above fusion rule $\u0424$, then the image edge point set after point fusion is:
When the largest scale value is ${2}^{J}$, the approximation for the lowpass component after fusion is:
When the multiscale point is confirmed, the image can be reconstructed.
The pointbased fusion example is shown in the left part of Fig. 8. Based on the threshold $T$, Fig. 8 expresses the fused results of the head CT image (gray edge) and MR image (black edge), the left image for the transform scale $j=$ 3, the right graph for the enlarged one of the domain.
Fig. 8Fusion by point under a certain scale
Fig. 9Fusion path based on curve line
Two orthogonal and neighborhood maximum points can link up along the direction of ${A}_{{2}^{j}}I({x}_{i},{y}_{i})$, and it covers some edge curves of image profile, so the close maximum points, whose partial modulus ${M}_{{2}^{j}}I({x}_{i},{y}_{i})$ is in the threshold, can link up to form a chain set. In this set, fusion path can be computed as follows:
where $s$, $t$ are pixel points, $A\left(s\right)$, $B\left(s\right)$, $A\left(t\right)$, $B\left(t\right)$ are coordinate values of $s$, $t$. $\Vert \bullet \Vert $ is model value. Fig. 9 has shown one kind of fusion path. Figs. 10 and 11 have shown other kinds of fusion path, where 1, 2,..., 9 are pixel points, the curve line marked “cut” is fusion path.
Fig. 10Fusion by old graph cut method
Fig. 11Fusion by new graph cut method
The relative area of the fusion process is as Fig. 12. The method is that two images merge into an image, actually becomes the two area fuse into one area. The fusion method is as follows:
(1) Preprocess the original image to eliminate all interference by denoising and smoothing process.
(2) Biomedical image in each sensor does wavelet transformation in wavelet domain. Then the highfrequency coefficients of each image are transformed to the spherical coordinate domain to reduce the correlation in the same scale.
Fig. 12Relative area of the fusion process
(3) Multiscale model product (MSMP) is used to control the shrinkage function to make the small wavelet coefficients and some noise removed. The highfrequency parts in spherical coordinate domain are coded by improved SPIHT algorithm. The lowfrequency part contains the most energy of the image, which can be encoded separately by DPCM.
(4) The coded image data in each sensor should be transmitted to fusion center by WSN. In fusion center, the coded image data should be decoded.
(5) Do edge processing for the decoded image by the above multiscale edge detection method. Construct the point area and make the two area fuse into one area.
(6) By the principle that the important and continuous boundary generates the longer edge curve, we check the resulting area boundary by step (5). Is it OK? If it is, then the resulting image by this fusion method is valid, otherwise, repeat step (5) to continue.
(7) Based on the multiscale edge of biomedical image, the image can be reconstructed.
3. Results
Based on the above correlation fusion algorithm, we have done tests and analysis for the fusion application of the CT and MR image, in order to test the feasibility and applicability of this method.
Based on our fusion method, we have done a large number of fusion experiments by the CT and CT images and CT and MR images. The fusion steps are as follows:
(1) The corresponding preprocessing for two kinds of images, including image noise removal, image correction, and image alignment [2022]. We have carried out image noise removal, image correction and image alignment in our own developed image processing software system [22]. Generally, data calibration involves space calibration, time calibration and measurement unit conversion. In view of the actual situation of this paper, we have mainly carried out space calibration. The socalled space calibration is to ensure that the coordinates of the measured data is consistent, the effect of coordinate transformation and error range is acceptable. The images to be merged, if not aligned, have been done rotation, translation, compression, stretching and other activities by the above descried software system.
(2) The preprocessed image in each sensor does wavelet transformation in wavelet domain. Then the highfrequency coefficients of each image are transformed to the spherical coordinate domain. After it coded by improved SPIHT and DPCM, they are sent to the fusion center by WSN and done fusion processing.
(3) Do edge processing for the decoded image by the above multiscale edge detection method. Construct the point area and make the two area fuse into one area.
(4) Perform the sixth step and the seventh step of the aforementioned fusion method. The threshold of modulus ${M}_{{2}^{j}}I({x}_{i},{y}_{i})$ is 2.0, the threshold of the close distance ${T}_{1}$ is 2.0, ${T}_{2}=$3.0. When selecting different thresholds, the fusion result is slightly different.
Fig. 13The original biomedical images for experiments
(5) Reconstruct image by the processed coefficient and the reconstructed image is the fused image.
Fig. 13 is the original colorful biomedical CT images for experiments. It are full of noise images. Based on this images, they can be processed according to the aforementioned fusion steps.
Fig. 14 is the edge detection example of biomedical images. Fig. 15 is the fusion result of the CT biomedical images. In Fig. 15(a) and (b) are two original CT biomedical images. Fig. 15(g) is fused result by our method of this paper. Figs. 15(c), (d), (e) and (f) are fused results by the methods from reference [1821], respectively. From the result of the examples of biomedical image fusion, we can see that our method has good quality.
Based on the fusion results, we can know the above presented multiscale correlation algorithm is feasible. In order to confirm the availability of the proposed method, we have done a large number of experiments. In comparison with the belief degree of other methods, the belief degree of the proposed method in this paper is much higher. In addition, by our project, we have done an onthespot practical application, it has been proved that its belief degree is as high as 95 % or more, it is now clear that for some application fields of demanding the high fusion quality like medical diagnosis, the method is very valuable.
Fig. 14Edge detection example of biomedical images
Fig. 15Fusion result of the CT biomedical image
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f)
g)
4. Discussion
As we know, samplebased image fusion can be divided into pixelbased image fusion and patchbased image fusion [13]. Pixelbased image fusion can generate a pixel each time, while samplebased image fusion a patch. Generally, pixelbased image fusion can easily reflect image diversities but can’t keep image structures and it is slow, as patchbased image fusion fast and can better maintain image characteristic information within blocks but the color transition between blocks may not be smooth which will cause a decline in quality. Patchbased image fusion method is based on samplebased image fusion method, which divides the input image into blocks with fixed size. The constraint rule of overlapping regions between blocks is to select matching blocks and generate images that without repeat and continuous on arbitrarily large size. Patchbased image fusion changes synthesis unit from pixel into block which significantly improves synthesis rate compared to the pixelbased algorithm, and maintains the overall structure. Early image fusion is based on feature matching method which regards image as feature and by the sample matches feature to generate a new image. This algorithm operation is large, slow despite the increase in quality. In recent years, it mainly adopts Markov Random Field (MRF) model to fuse [410].
We do the comparison of relative method and give the experimental results of bigdata biomedical images in PSNR, MSE and runtime.
Fig. 16 is the comparison of PSNR for kinds of denoising methods to Gaussian white noising of the same CT biomedical image among our method and several relative methods from aforementioned references.
Fig. 17 is the comparison of MSE for kinds of denoising methods to Gaussian white noising CT biomedical image among our method and several relative methods from aforementioned references.
Fig. 16Comparison of PSNR for CT biomedical image
Fig. 17Comparison of MSE for CT biomedical image
Fig. 18 is the comparison of PSNR for kinds of denoising methods to Gaussian white noising of the same MR biomedical image among our method and several relative methods from aforementioned references.
Fig. 19 is the comparison of MSE for kinds of denoising methods to Gaussian white noising MR biomedical image among our method and several relative methods from aforementioned references.
From the comparison of PSNR and MSE, we can see that our method in this paper has better results.
Fig. 20 is fusion runtime comparison of CT and MR image among our method and several relative methods from aforementioned references. Fig. 20 shows that our method retains the advantages in fusion quality and runtime in WSN. The results indicate the novel method is effective and very useful for transmission of bigdata biomedical image (especially, in the wireless environment).
Fig. 18PSNR comparison of MR biomedical image
Fig. 19MSE comparison of MR biomedical image
Fig. 20Fusion runtime comparison of CT & MR image
5. Conclusions
We have proposed novel fusion computing method Based on Spherical Coordinate for bigdata biomedical image of WSN in this paper. In this method, the three highfrequency coefficients in wavelet domain of biomedical image are preprocessed. This preprocessing strategy can reduce the redundant ratio of bigdata biomedical image. Firstly, the highfrequency coefficients are transformed to the spherical coordinate domain to reduce the correlation in the same scale. Then, a multiscale model product (MSMP) is used to control the shrinkage function so as to make the small wavelet coefficients and some noise removed. The highfrequency parts in spherical coordinate domain are coded by improved SPIHT algorithm. Finally, based on the multiscale edge of biomedical image, it can be fused and reconstructed. Experimental results indicate the novel method is effective and very useful for transmission of bigdata biomedical image, which can be especially used in wireless environment, which can solve the problem of data redundancy, more energy cost and low quality.
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This research work is supported by National Natural Science Foundation of China (Grant Nos. 61170173, 61202169, 61571328), Tianjin Key Natural Science Foundation (No. 13JCZDJC34600), Training plan of Tianjin University Innovation Team (No. TD125016).