A SegNet Based Image Enhancement Technique for Air-Tissue Boundary Segmentation in Real-Time Magnetic Resonance Imaging Video

In this paper, we propose a new technique for segmentation of the Air-Tissue Boundaries (ATBs) in the upper airway of the vocal tract in the midsagittal plane of the realtime Magnetic Resonance Imaging (rtMRI) videos. The proposed technique uses a segmentation using Fisher-discriminant measure (SFDM) scheme. The paper introduces an image enhancement technique using semantic segmentation in the preprocessing of the rtMRI frames before ATB prediction. We use a deep convolutional encoder-decoder architecture (SegNet) for semantic segmentation of the rtMRI images. The paper examines the significance of the preprocessing before ATB prediction by implementing the SFDM approach with different preprocessing techniques. Experiments with 5779 rtMRI video frames from four subjects demonstrate that using the semantic segmentation based image enhancement of rtMRI frames, the performance of the SFDM approach is improved compared to the other preprocessing approaches. Experiment results also show that the proposed approach yields 8.6% less error in ATB prediction compared with a semi-supervised grid based baseline segmentation approach.