Thyroid Nodule Image Analysis using Morphological Segmentation
DOI:
https://doi.org/10.24297/jac.v13i6.5667Keywords:
Thyroid, Morphological operation, Ultrasound, Segmentation, TumorAbstract
Computer-aided investigative processing has become an important part of medical practice. New growth of high expertise and use of a choice of imaging modalities, more confront arise so that high rate information can be produced for disease finding and behavior. Ultrasonography of Thyroid gland is the most common, portable, widely accessible, cheap, painless and secure. It is used to distinct the thyroid nodule images that are classified into two categories: (i) benign thyroid ample, (ii) malignant lump of thyroid gland. In this paper, Mathematical Morphology is used to segment the thyroid region and measure the area, perimeter, width and height of the thyroid area. Thyroid nodule images are taken from twenty peoples as samples.
Keywords— Thyroid, Morphological operation, Ultrasound, Segmentation, Tumor
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References
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[11] Nasrul Humaimi Mahmood and Akmal Hayati Rusli, “Segmentation and Area Measurement for Thyroid Ultrasound Imageâ€, International Journal of Scientific & Engineering Research Volume 2, Issue 12, December-2011.
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[14] Stamos Katsigiannis, Eystratios G. Keramidas, Dimitris Maroulis, “A Contourlet Transform Feature Extraction Scheme for Ultrasound Thyroid Texture Classificationâ€, Published in Engineering Intelligent Systems, Special issue: Artificial Intelligence Applications and Innovations, Volume 18, No. 3/4, 2010.
[15] Dimitris K. Iakovidis, Eystratios G. Keramidas, Dimitris Maroulis, “Fusion of fuzzy statistical distributions for classification of thyroid ultrasound patternsâ€, Artificial Intelligence in Medicine, Volume 50, 2010, pp. 33–41.
[16] Chuan-Yu Chang, Yue-Fong Lei, Chin-Hsiao Tseng and Shyang-Rong Shih, “Thyroid Segmentation and Volume Estimation in Ultrasound Images†IEEE Trans. on Biomedical Engineering, Vol. 57, No. 6, 2010.
[2] M.Malathi, Srinivasan, “Segmentation and classification methods of ultrasound thyroid nodule image: issues and challengesâ€, International journal of applied engineering research, volume 10, 2015.
[3] Gireesha H M, Nanda S, “Thyroid Nodule Segmentation and Classification in US imagesâ€, International Journal of Engineering Research & Technology, Volume 3, Issue 5, 2014, pp. 2252–2256.
[4] S. Binny, “Mean-shift filtering and segmentation in ultra sound thyroid imagesâ€, International Journal of Research in Commerce, IT & Management, Volume 3, Issue 3, 2013, pp. 126-130.
[5] Nikita Singh, Alka Jindal, “A Segmentation Method and Comparison of Classification Methods for Thyroid Ultrasound Imagesâ€, International Journal of Computer Applications, Volume 50 - No.11, 2012.
[6] Ms. Nikita Singh, Mrs Alka Jindal, “A segmentation method and classification of diagnosis for thyroid nodulesâ€, IOSR Journal of Computer Engineering, Volume 1, Issue 6, 2012, PP 22-27.
[7] Sheeja Agustin A, S. Suresh Babu, “Thyroid Segmentation on US Medical Images: An Overviewâ€, International Journal of Emerging Technology and Advanced Engineering, Volume 2, Issue 12, 2012.
[8] Nikita Singh, Alka Jindal, “Ultra sonogram Images for Thyroid Segmentation and Texture Classification in Diagnosis of Malignant (Cancerous) or Benign (Non- Cancerous) Nodulesâ€, International Journal of Engineering and Innovative Technology Volume 1, Issue 5, 2012.
[9] Deepika Koundal, Savita Gupta and Sukhwinder Singh, “Computer-Aided Diagnosis of Thyroid Nodule: A Reviewâ€, International Journal of Computer Science & Engineering Survey, Volume 3, No.4, 2012.
[10] Ioannis Legakis, Michalis A. Savelonas, Dimitris Maroulis and Dimitris K. Iakovidis, “Computer-based nodule malignancy risk-assessment in thyroid ultrasound imagesâ€, International Journal of Computers and Applications, Volume 33, No. 1, 2011.
[11] Nasrul Humaimi Mahmood and Akmal Hayati Rusli, “Segmentation and Area Measurement for Thyroid Ultrasound Imageâ€, International Journal of Scientific & Engineering Research Volume 2, Issue 12, December-2011.
[12] Ali Kermani, Ahmad Ayatollahi, Ahmad Mirzaei, Mohammad Barekatain, “Medical ultrasound image segmentation using genetic active contourâ€, J. Biomedical Science and Engineering, Volume 4, 2011, pp. 105-109.
[13] D. Selvathi and V. S. Sharnitha, “Thyroid Segmentation in ultrasound images using Support Vector Machineâ€, International Journal of Neural Networks and Applications, 4(1), 2011, pp. 7-12.
[14] Stamos Katsigiannis, Eystratios G. Keramidas, Dimitris Maroulis, “A Contourlet Transform Feature Extraction Scheme for Ultrasound Thyroid Texture Classificationâ€, Published in Engineering Intelligent Systems, Special issue: Artificial Intelligence Applications and Innovations, Volume 18, No. 3/4, 2010.
[15] Dimitris K. Iakovidis, Eystratios G. Keramidas, Dimitris Maroulis, “Fusion of fuzzy statistical distributions for classification of thyroid ultrasound patternsâ€, Artificial Intelligence in Medicine, Volume 50, 2010, pp. 33–41.
[16] Chuan-Yu Chang, Yue-Fong Lei, Chin-Hsiao Tseng and Shyang-Rong Shih, “Thyroid Segmentation and Volume Estimation in Ultrasound Images†IEEE Trans. on Biomedical Engineering, Vol. 57, No. 6, 2010.
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Published
2017-02-10
How to Cite
Mohan, M., & Srinivasan, D. (2017). Thyroid Nodule Image Analysis using Morphological Segmentation. JOURNAL OF ADVANCES IN CHEMISTRY, 13(6), 6254–6258. https://doi.org/10.24297/jac.v13i6.5667
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