12-APR Segmentation and Global Hu-F Descriptor for Human Spine MRI Image Retrieval

Abstract

The image retrieval system has been used to provide the needed correct images to the physicians while the diagnosis and treatment process is being conducted. The earlier image retrieval system was a text-based image retrieval system (TBIRS) that used keywords for the image context and it requires human’s help to manually make text annotation on the images. The text annotation process is a laborious task especially when dealing with a huge database and is prone to human errors. To overcome the aforementioned issues, the approach of a content-based image retrieval system (CBIRS) with automatic indexing using visual features such as colour, shape and texture becomes popular. Thus, this study proposes a semi-automated shape segmentation method using a 12-anatomical point representation method of the human spine vertebrae for CBIRS. The 12 points, which are annotated manually on the region of interest (ROI), is followed by automatic ROI extraction. The segmentation method performs excellently, as evidenced by the highest accuracy of 0.9987, specificity of 0.9989, and sensitivity of 0.9913. The features of the segmented ROI are extracted with a novel global Hu-F descriptor that combines a global shape descriptor, a Hu moment invariant, and a Fourier descriptor based on the ANOVA selection approach. The retrieval phase is implemented using 100 MRI data of the human spine for thoracic, lumbar, and sacral bones. The highest obtained precision is 0.9110 using a normalized Manhattan metric for lumbar bones. In a conclusion, a retrieval system to retrieve lumbar bones of the MRI human spine has been successfully developed to help radiologists in diagnosing human spine diseases.