Thyroid Disorder Detection Using Image Segmentation in Medical Images

POLEPOGU RAJESH , KUNDURU UMAMAHESWARI

US (Ultrasonography), CT (Computer Tomography) algorithm, MRI (Magnetic Resonance Imaging), Threshold segmentation

Now-a-days Bio-medical image processing is the most challenging and emerging field in medical diagnosis. Processing of US images is one of the crucial parts of this field. Thyroid is a small butterfly shaped gland located in the front of the neck just below the Adams apple. Thyroid is one of the endocrine gland, which produces hormones that help the body to control metabolism. Different thyroid disorders include Hyperthyroidism, Hypothyroidism, goitre, and thyroid nodules (benign/malignant). Ultrasound imaging is most commonly used to detect and classify abnormalities of the thyroid gland. Other modalities (CT/MRI) are also used. Computer aided diagnosis (CAD) help radiologists and doctors to increase the diagnosis accuracy, reduce biopsy ratio and save their time and effort. Numerous researches have been carried out in thyroid medical images and that are utilized for the diagnosis process. An automatic system is developed that classifies the thyroid images and segments the thyroid nodular area using machine learning algorithms. The thyroid measurement and recognition system is very useful in the medical field because the measurement of thyroid is important for the doctor diagnostic and medical analysis. The objective of this paper is to provide a complete solution to diagnosis the suspicious thyroid region in the thyroid gland. The integral region is further acquired by applying a thresholding method and specific region growing method to potential points. For better diagnosis purpose we can use MATLAB tool. The image undergoes the contrast enhancement to suppress speckle. The enhancement image is used for further processing of segmentation the thyroid region by local region-based active contour. The thyroid region is segmented into two parts, which are right and left with the active contour method separately. This is accordingly to the thyroid have two lobes; right lobe and left lobe. Experimental results of the thyroid region segmentation show high potential of our proposed approach.

Published Paper   E-Certificate

"Thyroid Disorder Detection Using Image Segmentation in Medical Images", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.1, Issue 6, page no.215 - 225, June-2016, Available :https://ijsdr.org/papers/IJSDR1606041.pdf

Volume 1 Issue 6, June-2016

Pages : 215 - 225

Paper Reg. ID: IJSDR_160496

Published Paper Id: IJSDR1606041

Downloads: 000347405

Research Area: Engineering

Country: VIJAYAWADA, ANDHRA PRADESH, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR1606041

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR1606041

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

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