Quantitative Analysis of Thyroid Nodules’ Severity and Changes in the Voice Box

Abstract

Voice is the most natural way to express one’s thoughts, but thyroid disease degrades the quality of one’s voice. Thyroid nodules are lumps of thyrocytes in the thyroid gland that may press the voice box, which results in voice change. To visualize the growth in the thyroid gland, the modified 3D Level Set Volumetric Segmentation (3D LSVS) techniques were used. Thyroid nodules’ severity can be measured based on the volume of the lump. An inferential statistic occurs with confidence intervals between the substance of very high concern (SVHC), Systemic Viral Infection (SVI), STMW and SVHD using Multivariate analysis (MVA) and testing. Acoustic analysis measured the voice changes. Different prevalent methods are compared concurrently and the results of quantitative analysis are better.

KEYWORDS:

• Level set segmentation
• Region
• Thyroid gland
• Voice box

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Antony Sophia N

Antony Sophia N is an assistant professor of computer science and engineering at Rajalakshmi Institute of Technology and also doing her PhD in Anna University Chennai. She obtained a Bachelor of Engineering and Master of Engineering in computer science and engineering from an institution affiliated with Anna University, Chennai. She published papers in 10 international journals. Her area of interest includes image processing, medical imaging, data mining, neural networks, data analytics, and pattern recognition.

G. Wiselin Jiji

G Wiselin Jiji is a professor of computer science and engineering at Dr Sivanthi Aditanar College of Engineering, Tiruchendur. She has published more than 78 scientific research papers. She is a recipient of 10 national and three state awards. Her long-term research focuses on computer-aided detection (CAD) and measurement (CAM) of lesions in medical images. CAD research aims at discovering the fundamental perception processes of human vision in the image-based diagnosis of lesions and developing mathematical/computational models that describe them. Her area of interest is computer-aided detection and diagnosis of abnormality using medical images and medical image analysis such as image enhancement, segmentation, feature extraction, object detection, and pattern recognition. Email: [email protected]