Blood color detection of color ultrasound images based on fuzzy algorithm

Abstract

With the establishment and development of technologies and theories such as computer technology, image processing, pattern recognition and artificial intelligence, image analysis systems have gradually become one of the methods of automatic quantitative analysis and testing in the medical field. However, the current technology is limited to the objectivity and comprehensiveness of blood edge detection, and no detection method with high accuracy can be found. In order to accurately and effectively detect the blood color of the color ultrasound image, this paper classifies the image feature extraction method, and simulates the classical differential algorithm, mathematical morphology algorithm and fuzzy Pal.King. In the following, the classical canny algorithm and its improved algorithm and the improved fuzzy Pal.King algorithm are introduced in detail. Finally, the simulation results are obtained. Among the indicators tested, DD has the highest accuracy. In the curve analysis, the FI value of FIB was > 0.05, the area under the curve of FDP was 67.9%, the sensitivity was 64%, and the specificity was 59%. In this paper, the quantitative analysis of the image feature extraction effect is given by the calculation results, and the subjective and objective unity is achieved. At the same time, the improved algorithm proposed in this paper is applied to the evaluation system for analysis and summary, and the results obtained are consistent with the theoretical analysis.

Keywords

Fuzzy algorithm color ultrasound image

1 Introduction

With the establishment and development of technologies and theories such as computer technology, image processing, pattern recognition and artificial intelligence, medical color image processing has been gradually paid attention to by researchers since the 1970 s, especially the automatic processing and recognition of blood cell image computers. Applying it to medical tests has basically met the requirements for miniaturization and practical use in the development of automatic identification system for blood cell images. Image analysis systems have gradually become one of the methods for automatic quantitative analysis and testing in the medical field.

In this paper, a brief overview of the basic methods of color image extraction is presented. Then, the image feature extraction method is classified and analyzed. The classical differential algorithm, mathematical morphology algorithm and fuzzy Pal. King are simulated. In the following, the classical canny algorithm and its improved algorithm and the improved fuzzy Pal. King algorithm are introduced in detail. Finally, the simulation results are obtained.

2 Related works

In view of the rapid development of the current medical image field, many research teams had conducted in-depth research on medical color ultrasound image detection. Sparse Constraint Single Image Super Resolution (SR) had recently attracted interest. However, most existing sparse representation methods for SR focus on luminance channel information and do not capture interactions between color channels. In [1], the author extended the sparsity-based SR to multiple color channels by considering color information. Edge similarity between RGB bands was used as a cross-channel related constraint. These additional constraints led to a new optimization problem, and the article proposes a tractable solution to effectively solve it. In [2], the author proposed the concept of image quality difference based on generalized Gaussian distribution features, which had the same trend as the change of peak signal-to-noise ratio (PSNR), and the author terminated the update of the dictionary or similar patches. Based on this, the paper proposed two sparse representation algorithms for image super-resolution, one was to further improve the image quality based on image quality assurance, and the other was to shorten the running time. In [3], the author proposed a dual-wavelength digital holographic color image reconstruction method using generalized phase-shift digital holography (GPSDH). In this method, color interference fringes were captured by a digital camera with a Bayer array color filter and phase shifts are performed simultaneously for all wavelengths. The author also verified the proposed method by optical experiments of a dual-wavelength digital holography system. The results shown that the two-color image can be successfully reconstructed without chromatic aberration. In [4], the authors introduced a new dark-guided course to fine convolutional neural network (CNN) framework to solve this problem. First, the authors proposed a data-driven filter approach to approximate an ideal depth image filter. Secondly, the article introduced CNN from course to fine to learn filter kernels of different sizes. In the rough stage, CNN learnt a larger filter kernel to obtain the original high resolution depth image. In [5], the authors introduced a new approach to medical image classification using different convolutional neural network (CNN) architectures. CNN was the most advanced image classification technology for learning the best image features for a given classification task. The content and semantics of an image can vary according to its modality, so the recognition of image modality is an important preliminary step. The key challenge in automatically classifying medical image morphology was due to the visual characteristics of different modalities: some are visually different, while others may have subtle differences.

The characteristic of the fuzzy algorithm is that “fuzziness” is not simply affirming or negating things, but using the degree of membership to reflect the extent to which things belong to the same category. With the subjectivity of people and the uncertainty of visual information, fuzzy theory can be introduced into different levels.

Research teams at home and abroad have applied fuzzy algorithms to different fields and have made great achievements. In [6], the author applied the fuzzy c-means (FCM) clustering algorithm to image segmentation. The authors proposed a cloned nuclear space FCM (CKS_FCM) that improved segmentation performance in a number of ways. In CKS_FCM, an initial cloning algorithm was generated using an immune cloning algorithm, which helped prevent the algorithm from converge to local optima. Second, CKS_FCM improved noise robustness by incorporating spatial information into the objective function of the FCM. In [7], the author applied the fuzzy algorithm to algorithm optimization. The author proposed a generalized hesitant fuzzy hierarchical clustering (GHFHC) algorithm based on Atanassov intuitionistic fuzzy set theory, which extended the traditional hierarchical clustering. It applied only to clear data and introduced a clustering algorithm that can be applied to large data sets with generalized hesitant fuzzy data. The running time of the GHFHC algorithm indicated that its computational complexity was low. In [8], the author applied the fuzzy algorithm to wireless channel modeling. The author proposed a channel equalizer based on particle swarm optimization to adjust adaptive neuro-fuzzy inference system (PSO-ANFIS), which can systematically identify, estimate and equalize wireless communication channels. The training methods and FCM used by the author provide the best regression of the system modeling to adapt to the wireless channel, evaluate the performance of the PSO-ANFIS equalizer, and compare it with the equalizer of the maximum likelihood sequence estimation. In [9], the author applied the fuzzy algorithm to the construction of the load model. The authors proposed a solution for optimal allocation and size adjustment of renewable DG units in radial distribution systems. A multi-objective genetic optimization algorithm was used to generate the optimal Pareto frontier and a fuzzy decision function was used as a blending function to obtain the best compromise solution. The impact of DG-based renewable energy allocation on the most sensitive bus collapses also considered different load models. In [10], the author applied the fuzzy algorithm to the recognition of motion behavior. The author described the nonlinear dynamic motion behavior of a ship equipped with a portable dynamic positioning (DP) control system under external force. In this paper, the fourth-order Runge-Kutta method was used to simulate the six-degree-of-freedom ship motion of the DP system. The results shown that the path and heading deviations were within acceptable limits of the control method used.

3 Method

3.1 Medical image edge detection related theory

(1) Mathematical Morphology

Mathematical morphology is a powerful tool for geometric morphology analysis and description. The two basic operations for image processing are corrosion and expansion. Their different logical combinations form open and closed operations, and the edge intensity operator ES(f) edges can be detected. The image acts as an edge intensity operator to form a ridge at the edge of the jump and a valley at the edge of the roof. Different edges can be detected using different logical combinations of different structural elements and structural operations.

(2) Neural network analysis

There are many forms of neural networks currently used for image edge detection. This method is essentially a recognition process that treats the edge detection process as an edge mode, but only utilizes a neural network in algorithm implementation. Although many existing algorithms can be transformed into neural networks to achieve such as when the decision value function is quadratic, the Euler equation is a first-order differential equation system, which can be solved by a RC network.

3.2 Blood edge parameter measurement model

(1) Extraction of blood flow information

If the pulse wave pressure p(t) and the characteristic impedance Z_c are known, the pulse flow rate q(t) can be obtained, and the cardiac output SV: $SV = \int_{0}^{T} q (t) dt = \int_{0}^{T} \frac{p (t)}{Z_{c}} dt = \frac{1}{Z_{c}} \int_{0}^{T} p (t) dt$ (1)

p(t) can be derived from the method described in continuous blood pressure measurement. Z_c can be estimated under normal physiological parameters. The characteristic quantity K extracted from the pulse wave map area can macroscopically describe the average characteristics of the pulse wave, K and k. There are the following approximate relationships:

$\begin{matrix} k = {(\frac{K}{0.35})}^{3} \\ SV = \frac{0.283}{K^{2}} T (P_{s} - P_{d}) (mL) \\ CO = SV \frac{60}{T} = \frac{17}{K^{2}} (P_{s} - P_{d}) (mL / min) \end{matrix}$ (2)

After the cardiac output SV and the cardiac output CO are obtained, other blood flow information parameters can be derived based on the two.

(2) Extraction of blood vessel information

Vascular information mainly refers to two parameters of peripheral resistance TPR and arterial compliance AC of blood vessels. Substituting P_m and CO, according to the definition of peripheral resistance: $\begin{matrix} = \frac{P_{d} (1 + K \frac{P_{s} - P_{d}}{P_{d}})}{\frac{17}{K^{2}} (P_{s} - P_{d})} \\ = \frac{K^{2}}{17} [\frac{1}{\frac{1}{P_{d}} (P_{s} - P_{d}) + K}] \times 60 \end{matrix}$ (3)

Arterial compliance AC is an intrinsic property of the arterial segment itself, which is independent of blood pressure. It is only determined by the K value and the cardiac cycle T representing factors such as vascular resistance, vessel wall elasticity, and blood viscosity. $\begin{matrix} (t) = [D_{a} (0) - D_{a} (\infty)] e^{- co (\frac{1}{Q_{a}} + \frac{1}{Q_{v}}) t} \\ + Q_{a} (\infty), D_{V} (t) = D_{V} (\infty) [1 - e^{- co (\frac{1}{Q_{a}} + \frac{1}{Q_{v}}) t}] \\ ALK = CO (\frac{1}{Q_{a}} + \frac{1}{Q_{v}}) = CO (\frac{2}{BV} + \frac{2}{BV}) \\ = \frac{4 CO}{BV}, ALT = \frac{ln 2}{ALK} = \frac{0.693}{ALK}, T_{M} = \frac{1}{ALK} \end{matrix}$ (4)

D_a is the arterial blood mixing curve, D_v is the venous blood mixing curve, ALK is the blood flow half-renewal rate, ALT is the blood flow half-update time, and TM is the blood retention time.

3.3 Feature extraction clustering algorithm based on fuzzy theory

(1) Fuzzy set

In the normal set A, the relationship between one element u and A has only two u ∈ A or u ∉ A. If you define a function φ_A (u) as follows: $φ_{A} = {\begin{matrix} 1 & u \in A \\ 0 & u \notin A \end{matrix}$ (5)

A feature function called φ_A. The fuzzy phenomenon has no such clear definition. To describe the fuzzy concept, the judgment function must be extended from 0 and 1 to the continuous interval [0, 1]. The ordinary set feature function becomes the membership function of the fuzzy set. When u is absolutely subordinate to A, χ_A (u) =1, and vice versa, χ_A (u) =0, visible, the normal set is a special case of fuzzy sets. The correspondence between the representation domain element and its membership degree can be expressed in the following form: $A = \sum_{i = 1}^{n} u_{A} (u_{i}) / u_{i}$ (6)

The symbol here needs to be explained. u_A (u_i)/u_i Is not the relationship between the numerator and denominator? It means that u_A (u_i) is the membership of u_i to the fuzzy set A; Σ is not the meaning of summation, but as a split symbol.

(2) FCM fuzzy clustering algorithm

The fuzzy membership matrix U = [u_i] _cn is used as the basis for clustering of sample points, where u_ij indicates the degree to which the j-th sample point belongs to the i-th class. The matrix should satisfy the following constraints: ${\begin{matrix} u_{y} \in [0, 1] \forall i, j \\ \sum_{i = 1}^{c} u_{ij} = 1 \forall j \\ 0 < \sum_{j = 1}^{n} u_{ij} < n \forall i \end{matrix}$ (7)

The process of clustering must be performed according to certain clustering criteria, and finally the exact division of the sample points is obtained. Under the above constraints, the problem of solving the optimal classification is reduced to the following objective function to find the extreme value:

$J (U, V) = \sum_{j = 1}^{n} \sum_{i = 1}^{c} {(u_{ij})}^{m} d_{ij}^{2} (x_{j}, v_{i})$ (8)

Where V ={ v₁, v₂, ⋯ , v_c } is the clustering center matrix, v_i ∈ R^s is the center of the i-th cluster, u_ij is the degree to which the j-th sample point belongs to the i-th class, m is the fuzzy index, and the fuzzy degree of the fuzzy membership degree matrix is controlled. The larger the m, the higher the fuzzy degree. In theory, m can take any value between 1 and ∞.

4 Experiment

4.1 Data source

(1) Data source

All patients included in the study underwent color Doppler ultrasound before the operation of the lower extremity venous examination to observe the function of the patient’s venous valve, with or without blood formation. All patients underwent venous blood 2 tubes under operative conditions, 2.7 ml each, placed in a vacuum-containing sodium oxalate-containing anticoagulation tube, and 1 tube was tested for DD, FIB, and FDP by immunoturbidimetry. The other tube was quickly centrifuged at 2500 r/min for 10 min (centrifugal radius 13.5 cm) using a high-speed cryogenic centrifuge to separate the plasma. The plasma was numbered and stored in a refrigerator at – 80 °C for cryopreservation. After accumulating a certain amount of sample, ELISA, DD, F1 + 2 and TAT were detected by ELISA.

(2) Experimental equipment

Color Doppler ultrasound blood flow detector (using GE vivid 7 multi-function color Doppler ultrasound diagnostic instrument); CA 1500 automatic blood coagulation analyzer; – 80°C ultra-low temperature refrigerator; high-speed low-temperature centrifuge, enzyme Standard instrument (BIO-Rad, USA); EP tube; artificial hip, knee, ankle prosthesis (ZIMMER, USA); sodium citrate anticoagulation tube.

4.2 Evaluation criteria

Table 1 lists the four cases of blood vessel classification.

Table 1
Four cases of blood vessel classification

Real blood vessel pixel True non-vascular pixel

Segmented blood vessel pixel Tp Fp

Segmented non-vascular pixel Fn Tn

	Real blood vessel pixel	True non-vascular pixel
Segmented blood vessel pixel	Tp	Fp
Segmented non-vascular pixel	Fn	Tn

The most widely used evaluation criteria for retinal vascular segmentation are Accuracy, Sensitivity, and Specificity. Accuracy indicates the proportion of the correct pixel to the total pixel of the image. Sensitivity indicates the proportion of the correct segment of the blood vessel to the true blood vessel, and the specificity indicates the proportion of the correctly segmented non-vascular pixel to the true non-vascular pixel. The calculation formula for accuracy, sensitivity and specificity is as follows: $\begin{matrix} Accurac y = (TP + TN) / (TP + FN + TN + FP) \\ Sensitivity = TP / (TP + FN) \\ Specificity = TN / (TN + FP) \end{matrix}$ (9)

5 Results

5.1 Analysis of edge detection results of red blood cell images

The specific evaluation results of edge detection are shown in Fig. 1. In the figure, the abscissa is the signal-to-noise ratio (SNR), the ordinate is the Pratt quality factor f, the solid line is the Pratt quality factor curve corresponding to the method, and the dotted line is the Pratt quality factor curve corresponding to the Pal.King method. It can be seen from the experimental results that when γ≤2 (γ is the number of enhancements), the detection effect of the method is not obvious; when γ>2, the Pratt quality factor curve corresponding to the method has the approximate shape, it can be seen that when γ>2, the method has better anti-noise ability than Pal.King method; in addition, the experimental results also show that when γ is gradually increased, the Pratt quality factor curves of these two methods will gradually become more and more consistently, this is consistent with the actual situation, because as γ increases, the two regions that are enhanced become closer to the effect of image binarization.

Fig. 1

Image edge detection results.

In this paper, experiments were performed using the Lena diagram and the red blood cell diagram. The results are shown in the figure below (the γ value is the number of enhancements in the figure, F_e = 2, and the value of γ is not indicated as 3). As can be seen from the figure, the edge map detected by the algorithm is better than the unenhanced edge map and the edge extracted by the Pal.King method. For the selection of the number of iterations γ, the experimental results show that when γ≥4, the quality of the edge detection is not improved, and sometimes the edge of the detail disappears. When γ= 1, the blur enhancement is insufficient and the edge is not clear enough. When using the method of the present invention, it is appropriate to select γ=2 or γ=3.

5.2 Analysis of blood test results

The trend of DD, FIB and FDP over time is shown in the Fig. 2. It can be seen from the figure that both DD and FDP increased significantly on the first day after surgery, followed by a brief decline on the third day, and the seventh appeared again. Elevated, while FIB has been in a relatively stable state. Patients were divided into DVT-positive group and DVT-negative group according to the results of B-ultrasound. ROC curve was used to analyze the area under DD, FIB, FDP curve and sensitivity and specificity for DVT (Table 2). The results showed that among the three indicators measured, the diagnostic value of DD and FDP was better, the area under the curve was 79.8% and 67.9%, respectively (P < 0.05), while the area under the curve of FIB was only 57%, and P > 0.05, indicating that the diagnostic value of FIB for the formation of DVT after detection is not high. When the area under the curve is taken to the maximum, the sensitivity of DD is 92%, the specificity is 39%, the sensitivity of FDP is 64%, and the specificity is 59%.

Fig. 2

Trends of DD, FIB, and FDP over time.

Table 2

Statistical analysis of the area under the curve

Result variable	Area under the curve	Standard error	P	95% Confidence interval
				Lower limit	Upper limit
DD	0.798	0.048	0.000	0.704	0.892
FDP	0.679	0.061	0.008	0.560	0.798
FIB	0.570	0.061	0.300	0.451	0.689

Under normal physiological conditions, there is a dynamic balance between the coagulation system and the fibrinolytic system in the body. If this balance is destroyed, especially after the vascular endothelial cells are damaged, it will lead to an increase in intravascular coagulation tendency, fibrin. Abnormal aggregation, increased fibrin degradation products, so that various thrombin is activated, which provides a theoretical basis for the use of thrombus markers to diagnose blood.

5.3 Blood edge evaluation and analysis

(1) Subjective evaluation analysis

Table 3 is an evaluation table for image feature extraction.

Table 3
Image feature extraction continuity evaluation

A Image edge continuity is very good, no short cracks, no edges are not connected

B Image edge continuity is better, no obvious break or no obvious edge disconnection

C Image edge continuity is general, there are a few image edge breaks or edges are not connected

D Image edge continuity is poor, there is obvious image edge break or no connection phenomenon

By summarizing the evaluation criteria of continuity, feature positioning performance and smoothness, combined with the experimental results of the various algorithms described above, the following conclusions are obtained.

The positioning accuracy of the differential operator in image feature extraction is relatively high. In the image feature extraction, the Canny operator is more accurate in image feature localization. However, according to the previous experiments, it can be seen that it uses Gaussian smoothing to filter out noise, which is easy to cause some small features to be lost, resulting in missed detection of small features. The pseudo features are extracted. The fuzzy Pal.King algorithm and its improved algorithm have clear boundaries in image feature extraction and accurate positioning.

(2) Objective evaluation analysis

The relevant data of the feature positioning accuracy obtained by performing Pratt quality factor analysis on the self-selected image is shown in Fig. 3. In this paper, the Matlab timing system is used to record the time for each algorithm to complete the feature extraction result of the self-selected image. The statistical results are shown in Fig. 4.

Fig. 3

Image feature extraction algorithms Pratt quality factor.

Fig. 4

Image feature extraction algorithm execution time.

It can be seen from figure 4 that the differential operator, especially the first-order differential operator, has a relatively short execution time because the first-order differential operator operation method is relatively simple.

The classical differential operator has the advantages of simple principle in image feature extraction, and is also easy to implement. It has high positioning accuracy and edge, but the connection is poor. Although the classical Canny algorithm and its improved algorithm are inferior to the differential operator in image feature localization, the degree of refinement of image features is better than that of differential operators. The classic fuzzy Pal.King algorithm can extract the complete features of the image, and has been well applied in pattern recognition and medical image processing.

6 Conclusions

In this paper, a brief overview of the basic methods of color image extraction is presented. Then, the image feature extraction method is classified and analyzed. The classical differential algorithm, mathematical morphology algorithm and fuzzy Pal.King are simulated. In the following, the classical Canny algorithm and its improved algorithm and the improved fuzzy Pal.King algorithm are introduced in detail. Finally, the simulation results are obtained.

This paper uses the threshold selection method in image segmentation to determine the threshold parameter, and then defines a new membership function form according to this parameter, thus transforming the image into an equivalent image blur feature plane, through the target and background of the image. Part of the blur enhancement operation is performed to enhance the grayscale contrast on both sides of the edge, convert it into a spatial image, and finally perform edge extraction. The results of this experiment show that the area, sensitivity and specificity under the ROC curve detected by this method are much higher than other methods. Of all the indicators we tested, DD was the most accurate. In the curve analysis, the FI value of FIB was > 0.05, the area under the curve of FDP was 67.9%, the sensitivity was 64%, and the specificity was 59%.

Footnotes

Acknowledgments

This work was supported by Shenzhen Science and Technology Program (JCYJ20170306095849825, JCYJ20170306095735097, JCYJ20180307124010740), Cultivation Project of Shenzhen Institute of Information Technology (ZY201715).

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A	Image edge continuity is very good, no short cracks, no edges are not connected
B	Image edge continuity is better, no obvious break or no obvious edge disconnection
C	Image edge continuity is general, there are a few image edge breaks or edges are not connected
D	Image edge continuity is poor, there is obvious image edge break or no connection phenomenon