Analysis of Three-dimensional Magnetic Resonance Human Liver Images

Abstract

Recent development in three-dimensional (3D) imaging techniques such as magnetic resonance imaging or computed tomography with application in medical science demands the development of appropriate 3D image processing methods. It is therefore of interest to develop methods of image analysis, which would make use of this additional information. This article presents the classification and segmentation of 3D MR human liver images. The described experiments investigate whether it is possible to improve the accuracy of homogenous texture classification with the use of 3D analysis. Classification was performed both for 3D and two-dimensional data samples using Gray Level Co-occurrence Matrices. The proposed segmentation method is based on the 3D network of synchronized oscillators applied for 3D data. The principles of oscillator network operation are described here. The network was tested on sample 3D artificial images, and the segmentation results were compared with those obtained with the use of multilayer feedforward perceptron. It is demonstrated that the advantage of the discussed approach is its resistance to changes of visual image information caused, for example, by noise, that are very often present in biomedical images. The proposed technique was applied for segmentation of 3D liver images, and the sample results are presented and discussed.