An Improved Air Tissue Boundary Segmentation Technique for Real Time Magnetic Resonance Imaging Video Using Segnet

Valliappan, CA and Kumar, A and Mannem, R and Karthik, GR and Ghosh, PK (2019) An Improved Air Tissue Boundary Segmentation Technique for Real Time Magnetic Resonance Imaging Video Using Segnet. In: 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019, 12 May 2019 - 17 May 2019, Brighton, pp. 5921-5925.

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Abstract

This paper presents an improved methodology for the segmentation of the Air-Tissue boundaries (ATBs) in the upper airway of the human vocal tract using Real-Time Magnetic Resonance Imaging (rtMRI) videos. Semantic segmentation is deployed in the proposed approach using a Deep learning architecture called SegNet. The network processes an input image to produce a binary output image of the same dimensions having classified each pixel as air cavity or tissue, following which contours are predicted. A Multi-dimensional least square smoothing technique is applied to smoothen the contours. To quantify the precision of predicted contours, Dynamic Time Warping (DTW) distance is calculated between the predicted contours and the manually annotated ground truth contour. Four fold experiments are conducted with four subjects from the USC-TIMIT corpus, which demonstrates that the proposed approach achieves a lower DTW distance of 1.02 and 1.09 for the upper and lower ATB compared to the best baseline scheme. The proposed SegNet based approach has an average pixel classification accuracy of 99.3 across all the subjects with only 2 rtMRI videos (~180 frames) per subject for training.

Item Type:	Conference Paper
Publication:	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	Audio signal processing; Binary images; Deep learning; Image segmentation; Magnetic resonance imaging; Magnetism; Pixels; Resonance; Semantics; Speech communication, Dynamic time warping; Learning architectures; Least-square smoothing; Pixel classification; Real time; SegNet; Semantic segmentation; Tissue boundary, Tissue
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	30 Nov 2022 09:05
Last Modified:	30 Nov 2022 09:05
URI:	https://eprints.iisc.ac.in/id/eprint/78387

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