Self-adjusting binary search trees: an investigation of their space and time efficiency in texture analysis of magnetic resonance images using the spatial gray-level dependence method

摘要

Texture feature extraction is a fundamental stage in texture analysis. Therefore, the reduction of its computational time and memory requirements should be an aim of continuous research. The Spatial Gray Level Dependence Method (SGLDM) is one of the most significant statistical texture description methods, especially in medical image analysis. However, the co-occurrence matrix is inefficient in terms of time and memory requirements. This is due to its dependency on the number of grey levels in the entire image. Its inefficiency puts up barriers to the wider utilization of the SGLDM in a real application environment. This paper investigates the space and time efficiency of self-adjusting binary search trees, in replacing the co-occurrence matrix. These dynamic data structures store only the significant textural information extracted from an image region by the SGLDM. Furthermore, they have the ability to restructure themselves in order to adapt to the co-occurrence distribution of the grey levels in the analyzed region. This results in a better time performance for texture feature extraction. The proposed approach is applied to a number of magnetic resonance images of the human brain and the human femur. A comparison with the co-occurrence matrix, in terms of space and computational time, is performed.