This paper presents a new, real-time, ghost correction method for echo planar imaging (EPI) that has been
implemented using the Imaging Calculation Environment (ICE) on a 3T Siemens MRI System. Conventional
methods for correcting EPI image ghost are based on image phase correction or on a reference scan. This new
method is also based on image phase correction, but uses a new algorithm for automatic determination of the
phase correction, which allows entirely automated operation. With implementation of the new correction method
in ICE, ghost-corrected images are automatically generated and loaded into the system's image database
immediately after completion of each EPI scan. Experiments showed that this real time ghost correction method
consistently reduced the ghost intensity in EPI images and improved overall image quality. On average, the ghost
to signal ratio (GSR) improved from 13.0% to 3.2% using the new method.
This paper proposes a new image enhancement method based on fuzzy assemble theory, resulting in a good vision effect. The method has a good impact on detail description comparing with traditional methods based on fuzzy assembly theory. It obviates the loss of information caused by traditional method.
An effective method based on mutual information to solve the problem of medical image retrieval is presented in this paper. This method has three features, which are scale invariance, position invariance and rotation invariance. The complexity of algorithm is significantly reduced, and image segmentation is avoided. The results show that the performance of retrieval by this method is better than that by others.
With the development of PACS system more and more medical images are stored in the database, retrieving images in such database becomes more and more difficult. that is why we introduce the way based on image content. In this paper the method of retrieving medical image is presented, which is based on both histogram and correlation algorithm. Histogram algorithm has characteristics of rotation invariance, position invariance and scale invariance. However, the limitation of histogram algorithm is obvious, it can't represent the spatial information of image. Hence, we introduce algorithm of correlation into our method to solve this problem. The method presented in this paper has characteristics of exactness and quickness in retrieving medical images, and which can retrieve image directly without processing image through image segmentation. So it can be realized easily. There was no any relative report about using this method to retrieve images before. Our work shows the method of correlation has a better performance in retrieving medical images, therefore it is a promising approach in this field.