# Efficacy Evaluation of Ultrasound with Active Contour Model for Hemodialysis in Children with Renal Failure

Authors: Jiawen Huo; Aizhi Peng; Fenfang Chen; Fen Chen; Lanling Shen; Hongxia Yan
Journal: Computational and Mathematical Methods in Medicine
 (2022)

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## Abstract

This study was aimed to explore the efficacy of ultrasound with active contour model (ACM) for hemodialysis in children with renal failure. The pulse coupled neural network (PCNN) was used to extract the initial contour of the ultrasound images, and the cloud model-based ACM was used to accurately segment the images, whose effect was compared with the classic Snake model. 84 children with chronic renal failure who received hemodialysis treatment in hospital were selected as research objects. There were 42 cases in the control group who were diagnosed by conventional ultrasound and 42 cases in the observation group who were diagnosed by ultrasound with the algorithm. Then, 42 children who underwent healthy physical examination (health group) were selected for comparison of related analysis indicators. The error rates of different algorithms were compared to analyze the levels of inflammatory factors in different groups of patients after hemodialysis. The results showed that the error rate of classical Snake model was 18.87% and that of ACM algorithm model was 11.01%, and the error rate of ACM algorithm model was significantly lower (P<0.05). After hemodialysis, the level of tumor necrosis factor (TNF)-α was 38.76 pg/mL in the observation group and 40.05 pg/mL in the control group, which was notably decreased in both groups, especially in the observation group (P<0.05). After hemodialysis, transforming growth factor (TGF)-β1 was 7.76 ng/mL in the observation group and 7.60 ng/mL in the control group, which was significantly reduced in both groups. After treatment, UA and Scr in both groups were significantly reduced, and the reduction was more significant in the observation group (P<0.05). HGB and RBC were significantly increased in both groups, and the increase was more significant in the observation group (P<0.05). In summary, ACM algorithm had a good segmentation effect on the ultrasonic images of children with renal failure. This study provided guidance for clinicians to choose the algorithm for the application of ultrasonic imaging diagnosis.


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## Body

## 1. Introduction

Acute renal failure refers to a systemic pathological process in which the urinary function of both kidneys is rapidly impaired in a short period of time due to various reasons, resulting in the rapid accumulation of metabolic wastes in the body, which in turn causes serious disturbances in the body environment [1]. The pathogenesis of acute renal failure mainly includes prerenal factors, renal parenchymal lesions, and acute urinary tract obstruction. Metabolic changes in body functions include oliguria-type acute renal failure. There are many reasons for renal failure, accumulation of metabolites and toxic substances, disturbance of water, electrolyte and acid-base balance, and renal endocrine dysfunction. In recent years, the incidence of chronic renal failure in children in China has shown an increasing trend, which seriously threatens the health of children. Neonatal acute renal failure mortality is as high as 25%-50%, with substantial kidney damage, which is more harmful to children. Many scientists are devoted to the clinical research of chronic renal failure in children [2, 3]. Hemodialysis is a common modality for the treatment of renal failure. Pediatric disease is the main indication for hemodialysis in various renal failures. The application of hemodialysis in renal failure in children is also increasingly widespread, and the application of hemodialysis in renal failure and vasculitis affects the inflammatory state of the disease [4, 5]. Hemodialysis can lead the patient’s blood out of the body and remove some pathogenic substances through a purification device, so as to quickly purify the blood, which can improve the overall situation and control the disease, and has a positive significance in the treatment of acute renal failure [6–8].The ultrasonic Doppler imaging system uses an integrated ultrasonic transducer to transmit ultrasonic pulses at the site to be measured, and the echo signals can be processed to obtain blood flow information in the blood vessels. Ultrasound equipment can provide continuous images and can display the parts that need to be diagnosed in real time. Noninvasive ultrasonography has high sensitivity and good real-time performance [9]. The main advantage of color Doppler vascular ultrasound is that it can visualize the situation of renal great vessels and understand the resistance of small arteries in the kidney and can reflect the blood flow situation of interlobular arterial arcuates and other vessels in the kidney [10–12]. Color Doppler vascular ultrasound can also help to determine whether there is renal vein main thrombosis and is used to detect the formation of renal arteriovenous fistula, which is of great value in the diagnosis of renal diseases [13, 14]. Introducing a neural network into medical image segmentation, which consists of a large number of parallel nodes, is realized by adjusting the connection relationship and connection weight between nodes. Correct selection in image feature extraction can greatly reduce the computational complexity and improve the overall performance of the segmentation algorithm [15, 16]. Active contour model (ACM) combines the knowledge of physics, geometry, and approximation ethics and comprehensively utilizes the information of regions and boundaries to segment the target image. ACM is categorized as geometric and parametric one (Snake model). The Snake model requires more calculation and can artificially obtain limit points for ultrasound image segmentation, and fully automatic ultrasound image segmentation is also an urgent problem to be solved at present. Most algorithms cannot completely automatically and accurately segment the target area, and the medical knowledge model is conducive to the segmentation of the target contour [17, 18]. The research on the segmentation of ultrasound images still needs to be continued. In this study, it was proposed to introduce an active contour segmentation algorithm in ultrasonic imaging and use an improved pulse coupled neural network (PCNN) to extract the initial contour of the ultrasonic image. The improved PCNN algorithm can effectively segment the ultrasonic image. It was hoped to provide evidence-based evidence for the noise reduction of ultrasound images and analysis of imaging information in children with renal failure.


## 2. Materials and Methods

### 2.1. Segmentation Algorithms of Ultrasound Images

Two-dimensional B-ultrasound is the main ultrasonic diagnostic technology in China. Ultrasound has various characteristics of sound waves. During the ultrasonic propagation, ultrasonic waves pass through tissues and organs with different structures, resulting in different degrees of attenuation, the receiving end receives echoes of different degrees, and the section images of the organs or tissues are obtained by marking them with different points of light on the screen [19, 20]. The ultrasonic wave goes through constructive interference and destructive interference in the process of back transmission, and speckle noise will appear on the image after it is converted into an image. During the ultrasonic imaging, the interference position of ultrasonic echo signals is random, so the speckle noise appearing on the ultrasonic image is also random [21].PCNN is a neural network model based on the principle of cat vision. Compared with neural networks, PCNN can extract effective information in complex backgrounds and has the characteristics of synchronous pulse firing and global coupling. The processing mechanism and signal form are similar to the physiological basis of the human visual nervous system. The automatic selection of network parameters selects differential purification to achieve initial contour extraction of ultrasound images [22]. Neurons received input signals from feed inputs and connect inputs. The feed input was the main input of the accepting part of the neuron, consisting of adjacent pixels of pixels in the image; and the auxiliary input of a neuron was the connection input, which consisted of the lateral connections of adjacent neurons.The standard PCNN neuron model was represented by the equation as follows:(1)Fi,ja=exp−af+Ii,j+VF∑K,IMi,j,k,lYi+k,j+ia−1,Li,ja=exp−bL+Li,ja−1+VL∑K,IWi,j,k,lYk,la−1,Qi,ja=Fi,ja1+βLi,ja,Qi,ja=1,Qi,ja>θa‐10,Qi,ja≤θa‐1,θi,ja=exp−bθ+θi,ja−1+VθYijn.In the above equations,Li,j represented the input signal of the ultrasound image; i,j represented the gray value and texture feature of the pixel, Yk,la−1 was the output pulse 0 or 1 of the neuron, Fi,ja referred to the feeding input of the neuron, Qi,ja stood for the internal obtained signal of the neuron, and θi,ja was dynamic threshold. The conveying unit accepted the input signal, and the internal unit combined the conveying unit and the connecting unit. The value of the internal active unit was compared with a gradually decreasing dynamic threshold, and the signal of the internal active unit can be continuously strengthened until the dynamic threshold was exceeded. After this iterative delay, the output of the neuron served as the iterative input for the feedback signal.It was assumed that, within the range of‐π,π, the phase of the echo signal obeyed a normal distribution, and the number of scatterers in the resolution unit was large enough, the joint distribution of the real part and the imaginary part was a Gaussian distribution, and its joint probability density can be expressed by the following equation:
(2)PMr,Mi=12πσ2exp−Mr2+Mi22σ2.In the above equation,σ2 represented the variance of the function, and the ultrasonic scattered wave signal can be expressed as the product of two Gaussian density functions, as given in below equation:
(3)M=Mr2+Mi2.The resulting probability density function was as follows:(4)ρM=Mσ2exp−M22σ2,M≥00,M<0.For general image denoising, logarithmic function transformation can be used to transform it from system noise to additive noise, and then inverse transformation is performed at last. IfPi,l, P’i,l, and Ni,l were assumed to represent the real noise-free image, the inputted noise image, and the multiplicative noise signal, respectively, then the noise image containing Ni,l can be expressed as follows:
(5)P′i,l=Pi,l∗Ni,l.After the logarithm of both sides of Equation (2) was taken synchronously, the result was as follows:
(6)lnP′i,l=lnPi,l∗lnNi,l.When a noisy image was denoised, it was easy to cause damage to the edge details of the image. An anisotropic diffusion filter model can be introduced for processing, and its expression can be expressed as shown in the following equation:(7)∂I∂t=divc∇I∇I,(8)It=0=I0.In Equations (7) and (8), I represented the pixel to be processed, div represented the divergence operator, ∇ represented the gradient operator, and t represented the number of iterations, which meant that the degree of noise reduction was related to time or the number of iterations. According to the difference between the diffusion coefficient and the gradient, the following diffusion equation can be obtained:
(9)c∇I1=11+∇I/k2,c∇I2=exp−∇I/k2.The gradient magnitude in the model can detect the edge area of the image. Whenc∇I tended to 0, the current pixel was the edge of the image, and the diffusion was suppressed; when c∇I tended to 1, the diffusion was enhanced, and the discrete form of the PM model can be expressed by the following equation:
(10)I^x=Ix+λ∑p∈Nc∇Ix,p∇Ix,p.In Equation (10), Ix represented the discrete sampling of the noise image, x and p represented the pixel coordinates of the image, respectively, and λ represented the custom parameter for adjusting the degree of expansion.Using the PCNN neuron model, the input signal in the receptive domain was summed according to the connection weight, and most of the pixel gray values were used as the signal in the input domain. If the input signal was not 0, the connected neuron fired; then, the internal activity signal of the neuron was increased. It meant that the pixel gray value corresponding to the neuron was increased, and the neuron can fire synchronously with the adjacent neurons in advance. The degree difference can be ignored, and a complete area can be divided. When PCNN was applied in image segmentation, it was necessary to select multiple network parameters, connection coefficientβ, time decay constant, internal amplification coefficient VF, VL, Vθ, etc. These parameters can be properly adjusted to obtain ideal segmentation results, and the closed contour can be obtained without prior knowledge of the target model by using the ACM algorithm.


### 2.2. ACM

On the premise of obtaining the prior shape of the segmentation target, ACM has evolved into a deformed template technology. The only disadvantage is that it requires initial contour and cannot effectively automatically segment images with complex structures. In this study, the PCNN was adopted to process the ultrasound images, the initial contour was obtained according to the characteristics of the ultrasound images, and then the improved PCNN was used for accurate segmentation. The traditional edge detection algorithm was a bottom-up segmentation method, which needed to segment and detect the underlying image information first and then fuse the high-level information, which was easy to be affected by the noise of the underlying image. ACM broken this limitation and continuously approached the true contour of the target through the process of seeking the minimum value of the capability norm. The flow chart of the basic idea of ACM for image segmentation is shown in Figure1.Figure 1
Schematic diagram of the ACM algorithm flow.The Snake model regarded the contour of the segmentation target as a series of points to form a curve and took the action of internal and external forces to continuously approach the real contour.(11)Gsnake=∫10GintVS+GextVSds.In the Equation (11), s was the normalized arc length parameter, Vs=xs,ys was the contour curve of the image plane Ix,y, x,y ∈Snake model, and Gint represented the internal energy of the curve; then, the equation was expressed as follows:
(12)GintVS=αsv′s+βsv′′s22.v’s reflected the continuity of the first derivative of the curve and also the elastic quantity of the curve. v”s was the second derivative of the smooth line of the curve, which represented the rigid energy of the curve. α and β were weight parameters. When the parameter was not zero, it represented that the curve was a smooth continuous curve.Image power represented the underlying features of the image, textures, lines, and edges. This power can make the Snake model tend to the true contours. External energy was manmade external restraint ability, and high-level processing methods can be used to unify the model.The deformation process energy functional Gsnake minimum process of Snake curve model is expressed as follows:(13)GintVS=αsv′s+βsv′′s22+GextVSds.Equation (13) was obtained by the variational method, and the minimum value equation satisfied the Euler-Lagrange equation:
(14)aSV″S−βsV‴s−∇Gext=0.It could be converted to force equation, as expressed in the following equation:(15)Fext+Fint=0,Fint=aSV″S−βsV‴s,Fext=−∇Eext.The Snake curve is gradually fixed to the true contour of the target in the attractive force, and the force is constantly changing as the Snake curve is fixed. In the ideal case when the sum of internal and external forces equals 0, Snake stays in the true contour of the target. When the weight parameter is constant, Snake can be converted into two independent equations, as follows:(16)ax″s+βxs+δGext/δx=0ay″s+βys+δGext/δy=0.WhenAs and βs were not constant, the discrete form of Snake energy function was expressed as follows:
(17)Gsnake=∑i=1AGintvi+Gextvi.ACM is a local optimization algorithm essentially. Convergence will occur when extracting target contours, and the discrete difference analysis evolutionary algorithm of cloud model is selected for discrete optimization. The cloud model can be intelligently controlled and evaluated fuzzy. Many simulation results show that the differential evolution algorithm of the cloud model can significantly improve the convergence speed and accuracy. Therefore, the cloud model can be applied to intelligent optimization algorithms and can reflect the randomness and ambiguity of entropy for the overall digital characteristics.


### 2.3. Research Objects

In this study, 84 children with chronic renal failure who received hemodialysis in hospital from January 2019 to January 2021 were included. The control group included 42 children diagnosed and treated with conventional ultrasound, including 26 boys and 16 girls, aged 2-11 years, with a mean age of6.58±2.68 years old. Hemodialysis was performed 78 times, including 46 times of hypoxic hemodialysis and 32 times of continuous hemodialysis. In addition, 42 healthy physical examination children (health group) were selected for comparison of related analysis indicators.There were 42 children in the observation group, including 19 boys and 23 girls, aged from 11 months to 12 years, with an average age of6.23±2.98 years old. 71 times of hemodialysis were performed, including 38 times of hemodialysis and 33 times of continuous hemodialysis. The general data of the two groups of children were comparable, and the results showed that there was no significant statistical difference (P>0.05). All family members of the children signed the informed consent forms, and this experiment was approved by the ethics committee of hospital.Inclusion criteria were as follows: (i) the patients met the relevant standards set by the nephrology group of the Chinese medical pediatric association and had certain proteinuria and oliguria; (ii) patients had no other vital organ diseases; (iii) patients had no obvious abnormality of cardiopulmonary function; and (iv) informed consent signed by the family members of the children.Exclusion criteria were as follows: (1) children with congenital heart disease, (2) children with incomplete case data, (3) premature children, (4) mental disorders, and (5) children with language and communication disorders.


### 2.4. Ultrasound Diagnostic Methods

All 84 children received color Doppler ultrasound diagnostic imaging examination. Color Doppler ultrasound diagnostic instrument was adopted, the probe frequency was 4.0 MHz to 5.5 MHz, and the coronal section scan was performed with the lateral waist of the test object as the detection point. The examination information included the patient’s kidney size, parenchymal thickness, echo, and blood flow distribution.


### 2.5. Observed Indexes

The error rate of different algorithms was compared. After ultrasonic diagnosis, the ultrasound images of children before and after denoising were compared, and the levels of tumor necrosis factorα (TNF-α) and transforming growth factor β1 (TGF-β1) as well as laboratory indicators of two groups were compared. The peak signal-to-noise ratio (PSNR) and mean square error (MSE) of the two algorithms were compared, and the MCC and DSC of the two algorithms were compared.


### 2.6. Noise Reduction Quality Assessment

The noise reduction effect of ultrasound images adopts subjective evaluation and objective evaluation. The subjective evaluation is mainly to evaluate the image noise reduction effect through visual observation. This evaluation method is easily affected by the subjective consciousness of the evaluator, so there is no quantitative standard to measure the quality of noise reduction. Therefore, some objective performance evaluation indicators were introduced for evaluation, including peak signal-to-noise ratio (PSNR) and mean square error (MSE). The function expressions of the above indicators are as follows:(18)PSNR=10lgpemax21/h⋅l∑a=1,b=1h,lfa,b−f∧a,b2,(19)MSE=1h⋅l∑a=1,b=1h,lfa,b−f∧a,b2.In Equations (18) and (19), pemax represented the maximum value of all pixels on the image; h and l represented the height and width of the image, respectively; fa,b referred to the gray value of each pixel of the noise-free image, and f^a,b was the gray value of each pixel of the image after noise reduction.


### 2.7. Statistical Analysis

SPSS19.0 statistical software was used for the processing of survey data. Measurement data that conformed to normal distribution were expressed asmean±standarddeviation (−x±s), and count data that did not conform were expressed as frequency and frequency (%). The t test data was adopted, and line Chi-square test was adopted for quality comparison. P<0.05 meant the difference was statistically significant; otherwise, it was not significant.


## 2.1. Segmentation Algorithms of Ultrasound Images

Two-dimensional B-ultrasound is the main ultrasonic diagnostic technology in China. Ultrasound has various characteristics of sound waves. During the ultrasonic propagation, ultrasonic waves pass through tissues and organs with different structures, resulting in different degrees of attenuation, the receiving end receives echoes of different degrees, and the section images of the organs or tissues are obtained by marking them with different points of light on the screen [19, 20]. The ultrasonic wave goes through constructive interference and destructive interference in the process of back transmission, and speckle noise will appear on the image after it is converted into an image. During the ultrasonic imaging, the interference position of ultrasonic echo signals is random, so the speckle noise appearing on the ultrasonic image is also random [21].PCNN is a neural network model based on the principle of cat vision. Compared with neural networks, PCNN can extract effective information in complex backgrounds and has the characteristics of synchronous pulse firing and global coupling. The processing mechanism and signal form are similar to the physiological basis of the human visual nervous system. The automatic selection of network parameters selects differential purification to achieve initial contour extraction of ultrasound images [22]. Neurons received input signals from feed inputs and connect inputs. The feed input was the main input of the accepting part of the neuron, consisting of adjacent pixels of pixels in the image; and the auxiliary input of a neuron was the connection input, which consisted of the lateral connections of adjacent neurons.The standard PCNN neuron model was represented by the equation as follows:(1)Fi,ja=exp−af+Ii,j+VF∑K,IMi,j,k,lYi+k,j+ia−1,Li,ja=exp−bL+Li,ja−1+VL∑K,IWi,j,k,lYk,la−1,Qi,ja=Fi,ja1+βLi,ja,Qi,ja=1,Qi,ja>θa‐10,Qi,ja≤θa‐1,θi,ja=exp−bθ+θi,ja−1+VθYijn.In the above equations,Li,j represented the input signal of the ultrasound image; i,j represented the gray value and texture feature of the pixel, Yk,la−1 was the output pulse 0 or 1 of the neuron, Fi,ja referred to the feeding input of the neuron, Qi,ja stood for the internal obtained signal of the neuron, and θi,ja was dynamic threshold. The conveying unit accepted the input signal, and the internal unit combined the conveying unit and the connecting unit. The value of the internal active unit was compared with a gradually decreasing dynamic threshold, and the signal of the internal active unit can be continuously strengthened until the dynamic threshold was exceeded. After this iterative delay, the output of the neuron served as the iterative input for the feedback signal.It was assumed that, within the range of‐π,π, the phase of the echo signal obeyed a normal distribution, and the number of scatterers in the resolution unit was large enough, the joint distribution of the real part and the imaginary part was a Gaussian distribution, and its joint probability density can be expressed by the following equation:
(2)PMr,Mi=12πσ2exp−Mr2+Mi22σ2.In the above equation,σ2 represented the variance of the function, and the ultrasonic scattered wave signal can be expressed as the product of two Gaussian density functions, as given in below equation:
(3)M=Mr2+Mi2.The resulting probability density function was as follows:(4)ρM=Mσ2exp−M22σ2,M≥00,M<0.For general image denoising, logarithmic function transformation can be used to transform it from system noise to additive noise, and then inverse transformation is performed at last. IfPi,l, P’i,l, and Ni,l were assumed to represent the real noise-free image, the inputted noise image, and the multiplicative noise signal, respectively, then the noise image containing Ni,l can be expressed as follows:
(5)P′i,l=Pi,l∗Ni,l.After the logarithm of both sides of Equation (2) was taken synchronously, the result was as follows:
(6)lnP′i,l=lnPi,l∗lnNi,l.When a noisy image was denoised, it was easy to cause damage to the edge details of the image. An anisotropic diffusion filter model can be introduced for processing, and its expression can be expressed as shown in the following equation:(7)∂I∂t=divc∇I∇I,(8)It=0=I0.In Equations (7) and (8), I represented the pixel to be processed, div represented the divergence operator, ∇ represented the gradient operator, and t represented the number of iterations, which meant that the degree of noise reduction was related to time or the number of iterations. According to the difference between the diffusion coefficient and the gradient, the following diffusion equation can be obtained:
(9)c∇I1=11+∇I/k2,c∇I2=exp−∇I/k2.The gradient magnitude in the model can detect the edge area of the image. Whenc∇I tended to 0, the current pixel was the edge of the image, and the diffusion was suppressed; when c∇I tended to 1, the diffusion was enhanced, and the discrete form of the PM model can be expressed by the following equation:
(10)I^x=Ix+λ∑p∈Nc∇Ix,p∇Ix,p.In Equation (10), Ix represented the discrete sampling of the noise image, x and p represented the pixel coordinates of the image, respectively, and λ represented the custom parameter for adjusting the degree of expansion.Using the PCNN neuron model, the input signal in the receptive domain was summed according to the connection weight, and most of the pixel gray values were used as the signal in the input domain. If the input signal was not 0, the connected neuron fired; then, the internal activity signal of the neuron was increased. It meant that the pixel gray value corresponding to the neuron was increased, and the neuron can fire synchronously with the adjacent neurons in advance. The degree difference can be ignored, and a complete area can be divided. When PCNN was applied in image segmentation, it was necessary to select multiple network parameters, connection coefficientβ, time decay constant, internal amplification coefficient VF, VL, Vθ, etc. These parameters can be properly adjusted to obtain ideal segmentation results, and the closed contour can be obtained without prior knowledge of the target model by using the ACM algorithm.


## 2.2. ACM

On the premise of obtaining the prior shape of the segmentation target, ACM has evolved into a deformed template technology. The only disadvantage is that it requires initial contour and cannot effectively automatically segment images with complex structures. In this study, the PCNN was adopted to process the ultrasound images, the initial contour was obtained according to the characteristics of the ultrasound images, and then the improved PCNN was used for accurate segmentation. The traditional edge detection algorithm was a bottom-up segmentation method, which needed to segment and detect the underlying image information first and then fuse the high-level information, which was easy to be affected by the noise of the underlying image. ACM broken this limitation and continuously approached the true contour of the target through the process of seeking the minimum value of the capability norm. The flow chart of the basic idea of ACM for image segmentation is shown in Figure1.Figure 1
Schematic diagram of the ACM algorithm flow.The Snake model regarded the contour of the segmentation target as a series of points to form a curve and took the action of internal and external forces to continuously approach the real contour.(11)Gsnake=∫10GintVS+GextVSds.In the Equation (11), s was the normalized arc length parameter, Vs=xs,ys was the contour curve of the image plane Ix,y, x,y ∈Snake model, and Gint represented the internal energy of the curve; then, the equation was expressed as follows:
(12)GintVS=αsv′s+βsv′′s22.v’s reflected the continuity of the first derivative of the curve and also the elastic quantity of the curve. v”s was the second derivative of the smooth line of the curve, which represented the rigid energy of the curve. α and β were weight parameters. When the parameter was not zero, it represented that the curve was a smooth continuous curve.Image power represented the underlying features of the image, textures, lines, and edges. This power can make the Snake model tend to the true contours. External energy was manmade external restraint ability, and high-level processing methods can be used to unify the model.The deformation process energy functional Gsnake minimum process of Snake curve model is expressed as follows:(13)GintVS=αsv′s+βsv′′s22+GextVSds.Equation (13) was obtained by the variational method, and the minimum value equation satisfied the Euler-Lagrange equation:
(14)aSV″S−βsV‴s−∇Gext=0.It could be converted to force equation, as expressed in the following equation:(15)Fext+Fint=0,Fint=aSV″S−βsV‴s,Fext=−∇Eext.The Snake curve is gradually fixed to the true contour of the target in the attractive force, and the force is constantly changing as the Snake curve is fixed. In the ideal case when the sum of internal and external forces equals 0, Snake stays in the true contour of the target. When the weight parameter is constant, Snake can be converted into two independent equations, as follows:(16)ax″s+βxs+δGext/δx=0ay″s+βys+δGext/δy=0.WhenAs and βs were not constant, the discrete form of Snake energy function was expressed as follows:
(17)Gsnake=∑i=1AGintvi+Gextvi.ACM is a local optimization algorithm essentially. Convergence will occur when extracting target contours, and the discrete difference analysis evolutionary algorithm of cloud model is selected for discrete optimization. The cloud model can be intelligently controlled and evaluated fuzzy. Many simulation results show that the differential evolution algorithm of the cloud model can significantly improve the convergence speed and accuracy. Therefore, the cloud model can be applied to intelligent optimization algorithms and can reflect the randomness and ambiguity of entropy for the overall digital characteristics.


## 2.3. Research Objects

In this study, 84 children with chronic renal failure who received hemodialysis in hospital from January 2019 to January 2021 were included. The control group included 42 children diagnosed and treated with conventional ultrasound, including 26 boys and 16 girls, aged 2-11 years, with a mean age of6.58±2.68 years old. Hemodialysis was performed 78 times, including 46 times of hypoxic hemodialysis and 32 times of continuous hemodialysis. In addition, 42 healthy physical examination children (health group) were selected for comparison of related analysis indicators.There were 42 children in the observation group, including 19 boys and 23 girls, aged from 11 months to 12 years, with an average age of6.23±2.98 years old. 71 times of hemodialysis were performed, including 38 times of hemodialysis and 33 times of continuous hemodialysis. The general data of the two groups of children were comparable, and the results showed that there was no significant statistical difference (P>0.05). All family members of the children signed the informed consent forms, and this experiment was approved by the ethics committee of hospital.Inclusion criteria were as follows: (i) the patients met the relevant standards set by the nephrology group of the Chinese medical pediatric association and had certain proteinuria and oliguria; (ii) patients had no other vital organ diseases; (iii) patients had no obvious abnormality of cardiopulmonary function; and (iv) informed consent signed by the family members of the children.Exclusion criteria were as follows: (1) children with congenital heart disease, (2) children with incomplete case data, (3) premature children, (4) mental disorders, and (5) children with language and communication disorders.


## 2.4. Ultrasound Diagnostic Methods

All 84 children received color Doppler ultrasound diagnostic imaging examination. Color Doppler ultrasound diagnostic instrument was adopted, the probe frequency was 4.0 MHz to 5.5 MHz, and the coronal section scan was performed with the lateral waist of the test object as the detection point. The examination information included the patient’s kidney size, parenchymal thickness, echo, and blood flow distribution.


## 2.5. Observed Indexes

The error rate of different algorithms was compared. After ultrasonic diagnosis, the ultrasound images of children before and after denoising were compared, and the levels of tumor necrosis factorα (TNF-α) and transforming growth factor β1 (TGF-β1) as well as laboratory indicators of two groups were compared. The peak signal-to-noise ratio (PSNR) and mean square error (MSE) of the two algorithms were compared, and the MCC and DSC of the two algorithms were compared.


## 2.6. Noise Reduction Quality Assessment

The noise reduction effect of ultrasound images adopts subjective evaluation and objective evaluation. The subjective evaluation is mainly to evaluate the image noise reduction effect through visual observation. This evaluation method is easily affected by the subjective consciousness of the evaluator, so there is no quantitative standard to measure the quality of noise reduction. Therefore, some objective performance evaluation indicators were introduced for evaluation, including peak signal-to-noise ratio (PSNR) and mean square error (MSE). The function expressions of the above indicators are as follows:(18)PSNR=10lgpemax21/h⋅l∑a=1,b=1h,lfa,b−f∧a,b2,(19)MSE=1h⋅l∑a=1,b=1h,lfa,b−f∧a,b2.In Equations (18) and (19), pemax represented the maximum value of all pixels on the image; h and l represented the height and width of the image, respectively; fa,b referred to the gray value of each pixel of the noise-free image, and f^a,b was the gray value of each pixel of the image after noise reduction.


## 2.7. Statistical Analysis

SPSS19.0 statistical software was used for the processing of survey data. Measurement data that conformed to normal distribution were expressed asmean±standarddeviation (−x±s), and count data that did not conform were expressed as frequency and frequency (%). The t test data was adopted, and line Chi-square test was adopted for quality comparison. P<0.05 meant the difference was statistically significant; otherwise, it was not significant.


## 3. Results

### 3.1. Cloud Model and Digital Features

In this study, the cloud model was applied to the algorithm. Considering the image of the individual fitness value and evolutionary algebra to the algorithm, a two-dimensional cloud generator was established, and the positive and negative cascade normal cloud generator was used to perturb the individual. Relationship between the cloud model and the digital features showed that the expectation Gx determines the center position of the estimated data, the entropy Gn can reflect the degree of deviation and dispersion of the cloud droplet from the center position, and the entropy of the entropy reflects the thickness of the cloud droplet. It was determined by ambiguity and randomness jointly. The uncertainty measurement of the super-entropy He reaction entropy uses the cloud model to reflect the overall characteristics, and the relationship between the three is shown in Figure2.Figure 2
The relationship between digital features of cloud model. (a)Gx=0, Gx=1, and He=0.04; (b) Gx=1, Gx=1, and He=0.04; (c) Gx=0, Gx=0.2, and He=0.04; (d) Gx=0, Gx=1, and He=0.2.
(a)(b)(c)(d)


### 3.2. Error Rate for Pixel Evaluation of Segmentation Algorithm

The ultrasound image was segmented, and the segmentation results of each algorithm were compared. The segmentation model obtained the average pixel error rate after splitting on the test set. The smallest segmentation error rate was realized using the improved active contour model. Compared with the classic Snake model, the average error rate is shown in Figure3. The error rate of the classic Snake model was 18.87% and that of the ACM algorithm model was 11.01%. Therefore, the error of the algorithm was significantly lower than that of the ACM algorithm model.Figure 3
Pixel evaluation error rate of segmentation algorithm.


### 3.3. Ultrasound Images

Ultrasound image of 12-year-old boy was selected for analysis here, which was scanned in the renal coronal section. The color flow map of the renal vascular tree, the blood vessels inside and outside the kidney, and the blood flow in the renal cortex can be clearly seen. Figure4(a) is the original image, and Figure 4(b) is the denoised image. After denoising by an intelligent algorithm, the image clarity was significantly improved.Figure 4
Comparison of ultrasound images before and after denoising. (a) The original image and (b) the denoised image.
(a)(b)The ultrasound images with different segmentation effects were compared, and the results are illustrated in Figure5. The ACM algorithm showed a good segmentation effect, which was better than the manual segmentation of images and the segmentation effect of the classic Snake model.Figure 5
Comparison of ultrasonic image segmentation. (a) denoised image, (b) manually segmented image, (c) segmentation effect of classical snake model, and (d) segmentation effect of ACM.
(a)(b)(c)(d)


### 3.4. Segmentation Effect of Two Algorithms

Figure6 showed the comparison of MCC and DSC of the two algorithms, where Figure 6(a) is MCC and Figure 6(b) is DSC. The MCC and DSC of ACM algorithm were significantly higher than those of Snake algorithm and the segmentation effect was better (P<0.05).Figure 6
MCC and DSC comparison of two algorithms. (a) MCC and (b) DSC. *Compared with Snake algorithm,P<0.05.
(a)(b)


### 3.5. Denoising Effect of Two Algorithms

Figure7 showed the comparison of PSNR and MSE of two algorithms, where Figure 7(a) is PSNR and Figure 7(b) is MSE. The PSNR of ACM algorithm was significantly higher than that of Snake algorithm, while the MSE was significantly lower than that of Snake algorithm, and the denoising effect was better (P<0.05).Figure 7
PSNR and MSE comparison of two algorithms. (a) PSNR and B: MSE. *Compared with Snake algorithm,P<0.05.
(a)(b)


### 3.6. Comparative Analysis of Inflammatory Factors TNF-α and TGF-β1

Before and after hemodialysis, the TNF-α and TGF-β1 levels of the two groups of children were analyzed. As demonstrated in Figures 8 and 9, the inflammatory factors were significantly decreased before and after hemodialysis, and the difference between observation group and control group as well as health group was significant (P<0.05).Figure 8
Comparison of TNF-α values in different groups. *Compared with the control group, P<0.05; #Compared with health group, P<0.05.Figure 9
Comparison of TGF-β1 values in different groups. *Compared with the control group, P<0.05; #Compared with health group, P<0.05.


### 3.7. Comparison of Laboratory Indexes before and after Treatment

Figure10 showed the comparative analysis of laboratory indicators between two groups before and after treatment, where Figure 10(a) is UA, Figure 10(b) is Scr, Figure 10(c) is HGB, and Figure 10(d) is RBC. After treatment, UA and Scr in both groups were significantly reduced, and the reduction was more significant in observation group (P<0.05). HGB and RBC were significantly increased in two groups, and the increase was more significant in observation group (P<0.05).Figure 10
Comparative analysis of laboratory indicators between two groups before and after treatment. (a) UA; (b) Scr; (c) HGB; and (d) RBC. *Compared with control group,P<0.05.
(a)(b)(c)(d)


## 3.1. Cloud Model and Digital Features

In this study, the cloud model was applied to the algorithm. Considering the image of the individual fitness value and evolutionary algebra to the algorithm, a two-dimensional cloud generator was established, and the positive and negative cascade normal cloud generator was used to perturb the individual. Relationship between the cloud model and the digital features showed that the expectation Gx determines the center position of the estimated data, the entropy Gn can reflect the degree of deviation and dispersion of the cloud droplet from the center position, and the entropy of the entropy reflects the thickness of the cloud droplet. It was determined by ambiguity and randomness jointly. The uncertainty measurement of the super-entropy He reaction entropy uses the cloud model to reflect the overall characteristics, and the relationship between the three is shown in Figure2.Figure 2
The relationship between digital features of cloud model. (a)Gx=0, Gx=1, and He=0.04; (b) Gx=1, Gx=1, and He=0.04; (c) Gx=0, Gx=0.2, and He=0.04; (d) Gx=0, Gx=1, and He=0.2.
(a)(b)(c)(d)


## 3.2. Error Rate for Pixel Evaluation of Segmentation Algorithm

The ultrasound image was segmented, and the segmentation results of each algorithm were compared. The segmentation model obtained the average pixel error rate after splitting on the test set. The smallest segmentation error rate was realized using the improved active contour model. Compared with the classic Snake model, the average error rate is shown in Figure3. The error rate of the classic Snake model was 18.87% and that of the ACM algorithm model was 11.01%. Therefore, the error of the algorithm was significantly lower than that of the ACM algorithm model.Figure 3
Pixel evaluation error rate of segmentation algorithm.


## 3.3. Ultrasound Images

Ultrasound image of 12-year-old boy was selected for analysis here, which was scanned in the renal coronal section. The color flow map of the renal vascular tree, the blood vessels inside and outside the kidney, and the blood flow in the renal cortex can be clearly seen. Figure4(a) is the original image, and Figure 4(b) is the denoised image. After denoising by an intelligent algorithm, the image clarity was significantly improved.Figure 4
Comparison of ultrasound images before and after denoising. (a) The original image and (b) the denoised image.
(a)(b)The ultrasound images with different segmentation effects were compared, and the results are illustrated in Figure5. The ACM algorithm showed a good segmentation effect, which was better than the manual segmentation of images and the segmentation effect of the classic Snake model.Figure 5
Comparison of ultrasonic image segmentation. (a) denoised image, (b) manually segmented image, (c) segmentation effect of classical snake model, and (d) segmentation effect of ACM.
(a)(b)(c)(d)


## 3.4. Segmentation Effect of Two Algorithms

Figure6 showed the comparison of MCC and DSC of the two algorithms, where Figure 6(a) is MCC and Figure 6(b) is DSC. The MCC and DSC of ACM algorithm were significantly higher than those of Snake algorithm and the segmentation effect was better (P<0.05).Figure 6
MCC and DSC comparison of two algorithms. (a) MCC and (b) DSC. *Compared with Snake algorithm,P<0.05.
(a)(b)


## 3.5. Denoising Effect of Two Algorithms

Figure7 showed the comparison of PSNR and MSE of two algorithms, where Figure 7(a) is PSNR and Figure 7(b) is MSE. The PSNR of ACM algorithm was significantly higher than that of Snake algorithm, while the MSE was significantly lower than that of Snake algorithm, and the denoising effect was better (P<0.05).Figure 7
PSNR and MSE comparison of two algorithms. (a) PSNR and B: MSE. *Compared with Snake algorithm,P<0.05.
(a)(b)


## 3.6. Comparative Analysis of Inflammatory Factors TNF-α and TGF-β1

Before and after hemodialysis, the TNF-α and TGF-β1 levels of the two groups of children were analyzed. As demonstrated in Figures 8 and 9, the inflammatory factors were significantly decreased before and after hemodialysis, and the difference between observation group and control group as well as health group was significant (P<0.05).Figure 8
Comparison of TNF-α values in different groups. *Compared with the control group, P<0.05; #Compared with health group, P<0.05.Figure 9
Comparison of TGF-β1 values in different groups. *Compared with the control group, P<0.05; #Compared with health group, P<0.05.


## 3.7. Comparison of Laboratory Indexes before and after Treatment

Figure10 showed the comparative analysis of laboratory indicators between two groups before and after treatment, where Figure 10(a) is UA, Figure 10(b) is Scr, Figure 10(c) is HGB, and Figure 10(d) is RBC. After treatment, UA and Scr in both groups were significantly reduced, and the reduction was more significant in observation group (P<0.05). HGB and RBC were significantly increased in two groups, and the increase was more significant in observation group (P<0.05).Figure 10
Comparative analysis of laboratory indicators between two groups before and after treatment. (a) UA; (b) Scr; (c) HGB; and (d) RBC. *Compared with control group,P<0.05.
(a)(b)(c)(d)


## 4. Discussion

Renal failure is one of the critical pediatric diseases, often accompanied by multiple organ failure, which causes great physical harm to patients. At present, the age of patients with renal failure in China is gradually getting younger, and the incidence of renal failure in children is also increasing [23]. The commonly used clinical treatment is hemodialysis. Ultrasound has the advantages of noninvasive, convenient operation, high accuracy of results, and good repeatability and has been widely used in renal failure examination [24].With the development of computer technology, artificial intelligence algorithm is widely used in clinical imaging, improving image quality and diagnosis effect. Image segmentation is an important step in medical image processing. In recent years, ACM algorithm has been widely used in the field of image segmentation. ACM algorithms can segment complex structures. Ohs et al. [25] analyzed images of distal radius fractures using ACM model and proposed a new method for automatic contour image of distal radius fractures based on 3D morphological geodesic active contour (3D-GAC). 60 profiles of distal radial fractures and conservatively treated radii were compared with the current gold standard hand-painted 2D profiles over the healing process one year after the fracture to assess the accuracy of the algorithm. It was found that the 3D-GAC method showed improved fault resistance robustness in the treatment of cortical interruption, fracture fragments, etc. Using 3D-GAC method can ensure more accurate results, reduce the workload of manual contour drawing, and improve the work efficiency. In this study, the improved ACM algorithm was used to segment and analyze B-ultrasound images of children with renal failure, and the classical Snake model was introduced for comparison. Oshiro and Nishimura [26] conducted image processing on breast ultrasound images, extracted ROI contour on the ultrasound images, adopted GVF active contour model, and found that the contour spots of the lesions in the ultrasound images were fewer and clearer. Compared with other ACM algorithm applications, the improved ACM algorithm was adopted in this study for kidney B-ultrasound image segmentation and processing, and the classical Snake model was introduced for comparison. The algorithm adopted in this study was more advanced and applicable. It was found that the error rate of the classical Snake model was 18.87% and the error rate of the ACM algorithm model was 11.01%, which showed higher accuracy of the ACM algorithm model. ACM algorithm model can improve the accuracy of diagnosis in B-ultrasound images of children with renal failure, which can be popularized and applied in clinic.Inflammatory response is one of the important pathological bases in patients with renal failure. The main causes of chronic renal failure are primary glomerulonephritis, chronic pyelonephritis, diabetic nephropathy, etc. Inflammatory reaction will lead to renal parenchymal damage in patients, and the basic renal function is affected. TNF-α is a pleiotropic cytokine secreted and produced by activated mast cells that promotes inflammation and is associated with various types of kidney inflammation. TNF-α has been associated with kidney damage in children, and lowering TNF-α can help reduce kidney damage in children. Serum TNF-α levels in healthy children were compared with those in children with renal failure. The results showed that serum TNF-α levels in children with renal failure were significantly higher than those in the control group (P<0.05) and TNF-α decreased significantly after hemodialysis, which was consistent with the results of Zwolińska et al. [27]. TGF-β1 is a pleiotropic cytokine that can differentiate, regulate, apoptosis, and migrate in different cells. TGF-β1 signal peptide is related to autoimmune, fibrosis, and cancer. Blocking TGF-β1 signaling pathway can prevent renal fibrosis. Increased secretion of TGF-β1 and its receptors is also a hallmark of chronic kidney disease. Elevated TGF-β1 expression is causally associated with the progression of renal fibrosis induced by diabetes, hypertension, obstructive, ischemic, and toxin-induced injury. TGF-β1 signaling is broadly negatively controlled at levels of TGF-β1 receptor, SMAD2/3 activation, complex assembly, and promoter involvement, and it plays a key role in tissue homeostasis and many pathologies. Changes in TGF-β1 levels often lead to renal epithelial cell dedifferentiation and growth arrest, fiber proliferation response, and inflammation [28]. In this study, TGF-β1 levels in normal and renal failure children were compared, and TGF-β1 levels in the control group and the observation group were significantly reduced after hemodialysis (P<0.05).In summary, the levels of TNF-α and TGF-β1 in children with renal failure were significantly higher than those in healthy children and improved significantly after hemodialysis. The improved ACM algorithm can effectively segment renal ultrasound images, improve the diagnostic accuracy of doctors, and has positive clinical application, which can be promoted and applied.


## 5. Conclusion

The cloud model was applied to improve the effective segmentation of ACM. The improved ACM algorithm could effectively segment renal ultrasound images, which had reference significance for clinical treatment. The improved ACM algorithm showed a good segmentation and denoising effect on the ultrasound images of children with renal failure, thereby improving the diagnostic accuracy of the disease. The significance and contribution of this study is that it can assist doctors in the diagnosis of clinical ultrasound imaging in children with renal failure treated by hemodialysis. However, further research was needed on the segmentation standard of ultrasound images at this stage based on results of this study. Computer-aided diagnosis system is a complex multidisciplinary system, which needs to be deeply studied on the basis of many studies. The application of intelligent algorithms in image maps still needs further research from many angles, and it needs to be confirmed by more clinical studies in future research.



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*Source: 3665841-2022-08-05.xml*