Medical image registration is a highlight of actual research on medical image processing. It play an important role in clinical diagnoses and treating.Mutul information has been deeply researched and popularly applied in image registration. We discuss the principle, virtue, p- rogress and improvement of mutual information(MI). At last, other two similarity measures, a normalized similarity measure based on Jensen′s difference and f-information, are analyzede.

A survey of medical image registration

Each day, robots drive bikes through a white world and repeatedly spread dirt. Dummies fill gas tanks by day and clean streets and buildings at night untill a handling error makes colors appear.

True color

Image Fusion combines two or more input images to a single composite image having redundant and complementary information and gives better visualization. It could be performed on various levels and domains like; images from single or multiple sensors, images taken at same or different times or images from single or multiple views. Among the three levels of fusion; we will focus on the pixel level image fusion. Image fusion methods have a wide area of applications like remote sensing, medical images, multi focused images, military applications, target detection and many more. A novel approach based on Shift Invariant Discrete Wavelet and PCA is presented here. In this paper we will explain the need of fusion, selection of filters for image processing and finally the proposed scheme with results obtained on various data sets. The paper will conclude with comparative analysis with some other data sets.

http://inpressco.com/wp-content/uploads/2013/11/Paper191708-1715.pdf

A Novel Method for Medical Image Fusion and Comparative Analysis

http://inpressco.com/wp-content/uploads/2013/11/Paper191708-17151.pdf

Image fusion is a process in which different images recorded by several sensors from one scene are combined to provide a final image with higher quality compared to each individual input image. In fact, combination of different images recorded by different sensors is one of image fusion methods. The fusion is performed based on maintaining useful features and reducing or removing useless features. The aim of fusion has to be clearly specified. In this paper we propose a new method which combines vision and infrared images by weighting average to provide better image quality. The weighting average is performed in gradient domain. The weight of each image depends on its useful features. Since these images are recorded in night vision, the useful features are related to clear scene details. For this reason, object detection is applied on the infrared image and considered as its weight. The vision image is also considered as a complementary of infrared image weight. The averaging is performed in gradient of input images, and final composed image is obtained by Gauss-Seidel method. The quality of resulted image by the proposed algorithm is compared to the obtained images by state-of-the-art algorithms using quantitative and qualitative measures. The obtained results show that the proposed algorithm provides better image quality.

Fusion infrared and visible images using optimal weights

This thesis work is motivated by the potential and promise of image fusion technologies in the multi sensor image fusion system and applications. With specific focus on pixel level image fusion, the process after the image registration is processed, we develop graphic user interface for multi-sensor image fusion software using Microsoft visual studio and Microsoft Foundation Class library.

In this thesis, we proposed and presented some image fusion algorithms with low computational cost, based upon spatial mixture analysis. The segment weighted average image fusion combines several low spatial resolution data source from different sensors to create high resolution and large size of fused image. This research includes developing a segment-based step, based upon stepwise divide and combine process. In the second stage of the process, the linear interpolation optimization is used to sharpen the image resolution. Implementation of these image fusion algorithms are completed based on the graphic user interface we developed. Multiple sensor image fusion is easily accommodated by the algorithm, and the results are demonstrated at multiple scales. By using quantitative estimation such as mutual information, we obtain the experiment quantifiable results. We also use the image morphing technique to generate fused image sequence, to simulate the results of image fusion.

While deploying our pixel level image fusion algorithm approaches, we observe several challenges from the popular image fusion methods. While high computational cost and complex processing steps of image fusion algorithms provide accurate fused results, they also makes it hard to become deployed in system and applications that require real-time feedback, high flexibility and low computation ability

/pdf/pixel-level-image-fusion-algorithms-for-multi-camera-imaging-3lnmux0ja6.pdf

Sicong Zheng

Papers

Pixel-level Image Fusion Algorithms for Multi-camera Imaging System