Chen-Lun Lin

Multimodal Medical Image Registration Using Particle Swarm Optimization

In image guided surgery, the registration of pre- and intra-operative image data is an important issue. In registrations, we seek an estimate of the transformation that registers the reference image and test image by optimizing their metric function (similarity measure). To date, local optimization techniques, such as the gradient decent method, are frequently used for medical image registrations. But these methods need good initial values for estimation in order to avoid the local minimum. In this paper, we propose a new approach using particle swarm optimization (PSO) for medical image registrations. Particle swarm optimization is a stochastic, population-based evolutionary computer algorithm. The effectiveness of PSO has been demonstrated for both rigid and non-rigid medical image registration.

Hybrid particle swarm optimization and its application to multimodal 3D medical image registration

In the area of medical image analysis, 3D multimodality image registration is an important issue. In the processing of registration, an optimization approach has been applied to estimate the transformation of the reference image and target image. Some local optimization techniques are frequently used, such as the gradient descent method. However, these methods need a good initial value in order to avoid the local resolution. In this paper, we present a new improved global optimization approach named hybrid particle swarm optimization (HPSO) for medical image registration, which includes two concepts of genetic algorithms—subpopulation and crossover.

Hybrid particle swarm optimization for 3-D image registration

In image guided surgery, the registration of pre-and intra-operative image data is an important issue. In registrations, we seek an estimate of the transformation that registers the reference image and test image by optimizing their metric function (similarity measure). To date, local optimization techniques, such as the gradient decent method, are frequently used for medical image registrations. But these methods need good initial values for estimation in order to avoid the local minimum. Recently several global optimization methods such as genetic algorithm (GA) and particle swarm optimization (PSO) have been proposed for medical image registration. In this paper, we propose a new approach named hybrid particle swarm optimization (HPSO) for 3-D medical image registration, which incorporates two concepts (subpopulation and crossover) of genetic algorithms into the conventional PSO. Experimental results with both mathematic test functions and medical volume data show that the proposed HPSO performs much better results than conventional gradient decent method, GA and PSO.

Non-rigid image registration with anatomical structure constraint for assessing locoregional therapy of hepatocellular carcinoma

PURPOSE Assessing the treated region with locoregional therapy (LT) provides valuable information for predicting hepatocellular carcinoma (HCC) recurrence. The commonly used of assessment method is inefficient because it only compares two-dimensional CT images manually. In our previous work, we automatically aligned the two CT volumes to evaluate the therapeutic efficiency using registration algorithms. The non-rigid registration is applied to capture local deformation, however, it usually destroys internal structure. Taking these into consideration, this paper proposes a novel non-rigid registration approach for evaluating LT of HCC to maintain the image integrity. METHOD In our registration algorithm, a global affine transformation combined with localized cubic B-spline is used to estimate the significant non-rigid motions of two livers. The proposed method extends a classical non-rigid registration based on mutual information (MI) that uses an anatomical structure term to constrain the local deformation. The energy function can be defined based on the total one associated with the anatomical structure and deformation information. Optimal transformation is obtained by finding the equilibrium state in which the total energy is minimized, indicating that the anatomical landmarks have found their correspondences. Thus, we can use the same transformation to automatically transform the ablative region to the optimal position. RESULTS Registration accuracy is evaluated using the clinical data. Improved results are obtained with respect to all criteria in our proposed method (MI-LC) than those in the MI-based non-rigid registration. The landmark distance error (LDE) of MI-LC is decreased by an average of 3.93mm compared to the case of MI-based registration. Moreover, it could be found regardless of how many landmarks applied in our proposed method, a significant reduction in LDE values using registrations based on MI-LC compared with those based on MI is confirmed. CONCLUSION Our proposed approach can guarantee the continuity, the accuracy and the smoothness of structures by constraining the anatomical features. The results clearly indicate that our method can retain the local deformation of the image. In addition, it assures the anatomical structure stability.

Nonrigid registration for evaluating locoregional therapy of hepatocellular carcinoma

The assessment of the treated margin with locoregional therapy (LT), for hepatocellular carcinoma (HCC), is the common method for predicting HCC recurrence in most hospital. However, tumors sometimes cannot be removed clearly with LT in limited conditions. The therapeutic efficiency of HCC is often evaluated by comparing 2D fusion images of computed tomography (CT) or magnetic resonance imaging (MRI) between the preoperation and the postoperation. However, judgment about whether the tumors exist in the treated margin after LT by using 2D slices sometimes is difficult. It is desirable to develop a suitable image registration algorithm to automatically align the two volumes in order to transform the treated margin of the postoperative volume to the tumor of the preoperative volume to assess the therapeutic efficiency after treatment of HCC. With taking these into consideration, this paper proposed an automatic 3D fusion imaging approach for medical image by using the nonrigid registration method that aligning an ablative margin - that is the treated margin after LT, onto the locations of HCC. In our registration algorithm, a rigid global transformation combined with localized B-spline is used to estimate the significant nonrigid motions of the liver between before and after LT. Our proposed approach can ensure the feasibility, the accuracy and the efficacy to assess the treated margin for HCC. Furthermore, this method can be adapted to register multi-modality medical images. We demonstrate the effectiveness of our proposed method by comparing the difference criterions of fusion evaluation on medical images. The results clearly indicate that our method extremely useful in the evaluation of the treated margin, in addition, it remain the motion and local deformation of the volume.

PCA based regional mutual information for robust medical image registration

Mutual information (MI) is a widely used entropy-based similarity metric for medical image registration. It can be used for both mono-modality and multi-modality registration. Recently an improved mutual information metric named regional mutual information (RMI) has been proposed for robust image registration by including some regional or special information. Though RMI has been demonstrated more effective and more robust than traditional MI, it takes larger computation cost because of computation of high-dimensional joint distribution. For improvement of RMI, we propose a novel PCA based regional mutual information (PRMI) to implement a more robust and faster medical image registration.

Particle Swarm Optimization for Reconstruction of Penumbral Images

The representation of the movements of animation plays an important role in the manufacture of animation. Generally, we control the movement of characters with the animation timeline that is developed as sorts of layers. However, in the manufacture of Japanese style animation, i.e., anime, the general timeline makes the work procedure too complicated to draw and edit frames user-friendly. We propose a new kind of timeline called frame chain and woodcut that provide a very good visual effect and easy to use. We approach the frame chain and woodcut with our proposed algorithms and technique. We also present our anime engine system with the new dynamic timeline.