Hierarchical Temporal Memory Network for Medical Image Processing

Abstract

Medical image segmentation is a basic step in medical image analysis, especially for medical image sequences such as CT sequences. Automated segmentation of different objects in the medical image sequences is of great significance to the 3D reconstruction of medical images. A novel image recognition method which can be implemented in automated medical image segmentation is introduced. In contrast with other algorithm, HTM (hierarchical temporal memory) is a network using a spatio-temporary hierarchy that works as our neocortex. The algorithm refereed in this paper consists of three main steps. Firstly, a four level hierarchical structure is established. Secondly, create frames by animating gray images to train the HTM network. During the learning phase, the nodes in HTM network build its representations spatial pooler and temporal pooler for inputs. Thirdly, test with dataset to get the inference result for classification. The results show that the proposed method can recognize the “middle slice” for different given objects when process the medical image sequences.