Unsupervised Segmentation Method for Thyroid Nodules in Ultrasound Images | ||
| Iraqi Journal of Science | ||
| Article 1, Volume 57, Issue 4, September 2016, Pages 2994-3004 | ||
| Authors | ||
| Hajer Z. Refaat; Faleh H. Mahmood | ||
| Abstract | ||
| 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. A different thyroid disorder includes Hyperthyroidism, Hypothyroidism, and thyroid nodules (benign/malignant). Ultrasound imaging is most commonly used to detect and classify abnormalities of the thyroid gland. Segmentation method is a tool that used widely in many applications including medical image processing. One of the common applications of segmentation is in medical image analysis for clinical diagnosis that has an important role in terms of quality and quantity. The main objective of this research is to use the Computer-Aided Diagnosis (CAD) algorithms to help the early detection of thyroid tumors. Thyroid ultrasound images may contain speckle noise which leads to obtain incorrect result. In order to obtain good accuracy; the noise must be removed from the input image. Those propose method is started with pre-processing of the thyroid ultrasound image to enhance its contrast and removing the undesired noise in order to make the image suitable for further processing. In our proposed work, we are using bilateral filter and unsharp filter to remove speckle noise to perform the pre-processing operations on the thyroid ultrasound images. The segmentation process is performed by using Fuzzy C-Means (FCM) algorithm to detect and segment thyroid ultrasound images for the thyroid region extracted image to 6 classes for two sample normal and abnormal images. The resulted segmented ultrasound images, and then used to extract the tumor region from thyroid's image. | ||
| Keywords | ||
| Medical Imaging; Thyroid nodules; Ultrasound imaging; bilateral filtering; unsharp filtering; Segmentation; fuzzy c; mean | ||
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