Temporal Registration of Cardiac Multimodal Images Using Locally Linear Embedding Algorithm

Purpose: Multimodal Cardiac Image (MCI) registration is one of the evolving fields in the diagnostic methods of Cardiovascular Diseases (CVDs). Since the heart has nonlinear and dynamic behavior, Temporal Registration (TR) is the fundamental step for the spatial registration and fusion of MCIs to integrate the heart's anatomical and functional information into a single and more informative display. Therefore, in this study, a TR framework is proposed to align MCIs in the same cardiac phase. Materials and Methods: A manifold learning-based method is proposed for the TR of MCIs. The Euclidean distance among consecutive samples lying on the Locally Linear Embedding (LLE) of MCIs is computed. By considering cardiac volume pattern concepts from distance plots of LLEs, six cardiac phases (end-diastole, rapid-ejection, end-systole, rapid-filling, reduced-filling, and atrial-contraction) are temporally registered. Results: The validation of the proposed method proceeds by collecting the data of Computed Tomography Coronary Angiography (CTCA) and Transthoracic Echocardiography (TTE) from ten patients in four acquisition views. The Correlation Coefficient (CC) between the frame number resulted from the proposed method and manually selected by an expert is analyzed. Results show that the average CC between two resulted frame numbers is about 0.82±0.08 for six cardiac phases. Moreover, the maximum Mean Absolute Error (MAE) value of two slice extraction methods is about 0.17 for four acquisition views. Conclusion: By extracting the intrinsic parameters of MCIs, and finding the relationship among them in a lower-dimensional space, a fast, fully automatic, and user-independent framework for TR of MCIs is presented. The proposed method is more accurate compared to Electrocardiogram (ECG) signal labeling or time-series processing methods which can be helpful in different MCI fusion methods.

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A nonlinear method for monitoring industrial process

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331220959232 ◽

2020 ◽

pp. 014233122095923

Author(s):

Yuan Li ◽

Chengcheng Feng

Keyword(s):

Fault Detection ◽

Objective Function ◽

Dimensional Space ◽

Detection Methods ◽

High Dimensional ◽

Locally Linear Embedding ◽

Industrial Case Study ◽

Laplacian Eigenmaps ◽

Linear Embedding ◽

Locally Linear

Aiming at fault detection in industrial processes with nonlinear or high dimensions, a novel method based on locally linear embedding preserve neighborhood for fault detection is proposed in this paper. Locally linear embedding preserve neighborhood is a feature-mapping method that combines Locally linear embedding and Laplacian eigenmaps algorithms. First, two weight matrices are obtained by the Locally linear embedding and Laplacian eigenmaps, respectively. Subsequently, the two weight matrices are combined by a balance factor to obtain the objective function. Locally linear embedding preserve neighborhood method can effectively maintain the characteristics of data in high-dimensional space. The purpose of dimension reduction is to map the high-dimensional data to low-dimensional space by optimizing the objective function. Process monitoring is performed by constructing T2 and Q statistics. To demonstrate its effectiveness and superiority, the proposed locally linear embedding preserve neighborhood for fault detection method is tested under the Swiss Roll dataset and an industrial case study. Compared with traditional fault detection methods, the proposed method in this paper effectively improves the detection rate and reduces the false alarm rate.

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Sorting 4DCT Images Using Locally Linear Embedding

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.462-463.150 ◽

2013 ◽

Vol 462-463 ◽

pp. 150-154

Author(s):

Zhao Hui Luo ◽

Zai Fang Xi

Keyword(s):

Respiratory Motion ◽

Dimensional Space ◽

Image Data ◽

Locally Linear Embedding ◽

Time Resolved ◽

One Dimensional ◽

A Value ◽

4D Computed Tomography ◽

Linear Embedding ◽

Locally Linear

Respiratory motion degrades anatomic position reproducibility, and result in significant errors in radiotherapy. 4D computed Tomography (4DCT) can characterize anatomy motion during breathing. Usually, the acquired 4DCT images sequences is out of order. How to rearrange the sequence, i.e. sort 4DCT images has been the focus of 4DCT. In this paper we propose a method based on locally linear embedding (LLE), to reconstruct time-resolved CT volumes. By mapping high dimensional image data with LLE into one dimensional space, each image is assigned a value, then 4DCT images is sorted according to the value to reconstruct a respiratory cycle. Experiments result shows that the method is feasible to sort 4 DCT images without using any external motion monitoring systems.

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CLASSIFICATION-ORIENTED LOCALLY LINEAR EMBEDDING

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001410008159 ◽

2010 ◽

Vol 24 (05) ◽

pp. 737-762 ◽

Cited By ~ 1

Author(s):

PI-FUEI HSIEH ◽

MING-HUA YANG ◽

YI-JAY GU ◽

YU-CHENG LIANG

Keyword(s):

Dimensional Space ◽

Linear Method ◽

Real Data ◽

Data Representation ◽

Classification Performance ◽

Locally Linear Embedding ◽

Real World Data ◽

Label Information ◽

Linear Embedding ◽

Locally Linear

The locally linear embedding (LLE) algorithm is hypothetically able to find a lower dimensional space than a linear method for preserving a data manifold originally embedded in a high dimensional space. However, uneven sampling over the manifold in real-world data ultimately causes LLE to suffer from the disconnected-neighborhood problem. Consequently, the final dimensionality required for the data manifold is multiplied by the number of disjoint groups in the complete data representation. In addition, LLE as an unsupervised method is unable to suppress between-class connections. This means that samples from different classes are mixed during reconstruction. This study presents CLLE, a classification-oriented LLE method that uses class label information from training samples to guide unsupervised LLE. The criterion for neighbor selection is redesigned using class-conditional likelihood as well as Euclidean distance. This algorithm largely eliminates fractured classes and lowers the incidence of connections between classes. Also, a reconnection technique is proposed as a supporting method for ensuring a fully connected neighborhood graph, so that CLLE is able to extract the fewest features. Experiments with simulated and real data show that CLLE exceeds the performance of linear methods. Comparable classification performance can be achieved by CLLE using fewer features. In comparison with LLE, CLLE demonstrates a higher aptitude for and flexibility towards classification.

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