Anatomical Point-of-Interest Detection in Head MRI Using Multipoint Feature Descriptor

Abstract

Automatic detection of specific anatomical points of interest (POIs) in head magnetic resonance imaging (MRI) is a technical bottleneck in medical image registration and big data analysis for head diseases. A technique for automatically retrieving POIs in head MRI scans is explored in this study. A Haar-like feature of the image is introduced to generate the feature description vector of the POI, and the most appropriate standard vector is selected from multiple marked images. A fixed five-point joint feature description is proposed for solid POI detection, and an adaptive three-point joint feature description is proposed for cavity POI detection. A total of 516 head MRI volumes were used for anatomical point detection. A solid point detection experiment was conducted using the POIs of the right/left internal acoustic pore (RIA/LIA). The POIs of the right/left ascending segment of the internal carotid artery in the posterior cavernous sinus (RAS/LAS) were used in a cavity point detection experiment. The experimental results show that the accuracies of the solid point detection for LIA and RIA are 81.8% and 84.7%, respectively. Those of cavity point detection for LAS and RAS are 66.7% and 76.2%. The performance of the proposed method is better than those of BRIEF and SIFT algorithm. The proposed method can facilitate the marking of anatomical points for doctors, thus providing technical support for head image automatic registration and big data analysis for head diseases.

Prognostic potential of a voxelwise invasion risk map of nasopharyngeal carcinoma based on a coordinate system of the nasopharynx

“…However, the TIR map of NPC can be obtained automatically. We assessed the automatic localization method of anatomical landmarks in MRI head images and achieved good results (51). There are many reports on the automatic segmentation of tumor ROIs for NPC in medical images (52)(53)(54)(55).…”