Hua Mei Chen

Mutual information-based CT-MR brain image registration using generalized partial volume joint histogram estimation

Mutual information (MI)-based image registration has been found to be quite effective in many medical imaging applications. To determine the MI between two images, the joint histogram of the two images is required. In the literature, linear interpolation and partial volume interpolation (PVI) are often used while estimating the joint histogram for registration purposes. It has been shown that joint histogram estimation through these two interpolation methods may introduce artifacts in the MI registration function that hamper the optimization process and influence the registration accuracy. In this paper, we present a new joint histogram estimation scheme called generalized partial volume estimation (GPVE). It turns out that the PVI method is a special case of the GPVE procedure. We have implemented our algorithm on the clinically obtained brain computed tomography and magnetic resonance image data furnished by Vanderbilt University. Our experimental results show that, by properly choosing the kernel functions, the GPVE algorithm significantly reduces the interpolation-induced artifacts and, in cases that the artifacts clearly affect registration accuracy, the registration accuracy is improved.

Performance of mutual information similarity measure for registration of multitemporal remote sensing images

Accurate registration of multitemporal remote sensing images is essential for various change detection applications. Mutual information has recently been used as a similarity measure for registration of medical images because of its generality and high accuracy. Its application in remote sensing is relatively new. There are a number of algorithms for the estimation of joint histograms to compute mutual information, but they may suffer from interpolation-induced artifacts under certain conditions. In this paper, we investigate the use of a new joint histogram estimation algorithm called generalized partial volume estimation (GPVE) for computing mutual information to register multitemporal remote sensing images. The experimental results show that higher order GPVE algorithms have the ability to significantly reduce interpolation-induced artifacts. In addition, mutual-information-based image registration performed using the GPVE algorithm produces better registration consistency than the other two popular similarity measures, namely, mean squared difference (MSD) and normalized cross correlation (NCC), used for the registration of multitemporal remote sensing images.

Imaging for concealed weapon detection: a tutorial overview of development in imaging sensors and processing

Manual screening procedures for detecting concealed weapons such as handguns, knives, and explosives are common in controlled access settings like airports, entrances to sensitive buildings, and public events. The detection of weapons concealed underneath a persons clothing is an important obstacle to the improvement of the security of the general public as well as the safety of public assets like airports and buildings. It is desirable to be able to detect concealed weapons from a standoff distance, especially when it is impossible to arrange the flow of people through a controlled procedure. The goal is the eventual deployment of automatic detection and recognition of concealed weapons. It is a technological challenge that requires innovative solutions in sensor technologies and image processing. A number of sensors based on different phenomenology as well as image processing support are being developed to observe objects underneath peoples clothing. The main aim of this article is to provide a tutorial overview of these developments.

A pyramid approach for multimodality image registration based on mutual information

A pyramid approach for multimodality image registration based on mutual information is presented. The image pyramid is obtained by using the wavelet transform. An exhaustive search algorithm at the coarsest level of the image pyramid is developed. Image partitioning and gray level intensity binning are used to increase the fidelity of the process. Because of the fact that image partitioning is used, our algorithm has the potential to be parallelized and implemented on a multiprocessor computer. Our algorithm has been applied on remotely sensed images. The results show that our algorithm performs fairly well.

Registration and fusion of infrared and millimeter wave images for concealed weapon detection

We present an approach to automatically register and fuse IR and MMW images for concealed weapon detection. The distortion between the two images is assumed to be a rigid body transformation without rotation and we assume that the scale factor can be found from both the sensor parameters and the distance ratio of the object to the two sensors. Our registration procedure involves image segmentation, binary correlation and other image processing algorithms. Our fusion method involves a pyramidal image decomposition scheme based on the wavelet transform. Performance of the image registration and image fusion algorithm is illustrated through an example.

Registration of multimodal brain images: some experimental results

Joint histogram of two images is required to uniquely determine the mutual information between the two images. It has been pointed out that, under certain conditions, existing joint histogram estimation algorithms like partial volume interpolation (PVI) and linear interpolation may result in different types of artifact patterns in the MI based registration function by introducing spurious maxima. As a result, the artifacts may hamper the global optimization process and limit registration accuracy. In this paper we present an extensive study of interpolation-induced artifacts using simulated brain images and show that similar artifact patterns also exist when other intensity interpolation algorithms like cubic convolution interpolation and cubic B-spline interpolation are used. A new joint histogram estimation scheme named generalized partial volume estimation (GPVE) is proposed to eliminate the artifacts. A kernel function is involved in the proposed scheme and when the 1st order B-spline is chosen as the kernel function, it is equivalent to the PVI. A clinical brain image database furnished by Vanderbilt University is used to compare the accuracy of our algorithm with that of PVI. Our experimental results show that the use of higher order kernels can effectively remove the artifacts and, in cases when MI based registration result suffers from the artifacts, registration accuracy can be improved significantly.

A study of joint histogram estimation methods to register multi-sensor remote sensing images using mutual information

Registration is the basic image processing operation in a variety of tasks such as multi-source classification, image fusion and change detection. Automatic intensity based registration techniques are gaining importance. In this paper, we investigate an intensity based technique that utilizes mutual information as the similarity measure. We apply this technique to perform multi-sensor image registration. The performance of a number of joint histogram estimation methods for the determination of mutual information has been evaluated using a measure called registration consistency. These methods include partial volume interpolation, cubic convolution interpolation, linear interpolation, and nearest neighborhood interpolation. The results show that partial volume interpolation produces the most reliable registration consistency. Nearest neighbor interpolation outperforms linear and cubic convolution interpolation.

Automatic registration of infrared and millimeter-wave images for concealed weapon detection

This paper presents an approach to automatically register IR and millimeter wave images for concealed weapons detection application. The distortion between the two images is assumed to be a rigid body transformation and we assume that the scale factor can be calculated from the sensor parameters and the ratio of the two distances from the object to the imagers. Therefore, the pose parameters that need to be found are x-displacement and y-displacement only. Our registration procedure involves image segmentation, binary correlation and some other image processing algorithms. Experimental results indicate that the automatic registration procedure performs fairly well.

On signal/image processing for concealed weapon detection from stand-off range

We present an overview of signal and image processing techniques developed for the concealed weapon detection (CWD) application. The signal/image processing chain is described and the tasks include image denoising and enhancement, image registration and fusion, object segmentation, shape description, and weapon recognition. Finally, a complete CWD example is presented for illustration.