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.
Background: Thyroid nodules are very common in clinical practice. Although most of thyroid nodules are benign, it is crucial to checkout which nodules are more likely to be malignant. Ultrasound is a major diagnostic tool for screening and evaluating thyroid diseases because it is safe, non-invasive, non-radioactive and effective.
Objective: The aim is to identify the role of ultrasound in assessing thyroid nodules and to review various ultrasound criteria predicting malignancy.
Patients and methods: A case series study conducted during the period from January 2015 to February 2016 at the First Surgical Unit, Department of Surgery, Baghdad Teaching Hospital by a team of surgeons. One hundred eighty Patients who underwent surgical i
Background: Thyroid ultrasound has been widely used to differentiate benign from malignant nodules; many investigators have tried to point out few ultrasonographic features in order to identify those lesions, which are at a higher risk of malignancy.
Objectives: To evaluate the efficacy of selected conventional ultrasound (US) features of thyroid focal lesions useful for predicting malignancy and establishing indications for fine-needle aspiration cytology (FNAC).
Patients and Methods:Two hundred and four consecutive patients with thyroid nodules who visited the outpatient clinic of the surgical department of Tikrit University teaching hospital for the period from January 2011 to April 2014, and who underwent surgery for clinical s
Background: Population studies suggest that 3–8% of asymptomatic adults have thyroid nodules. Nodules have a 5–15% prevalence of malignancy. Fine-needle aspiration cytology is the primary and frequently initial tool for assessing the risk of malignancy in thyroid nodules and selecting patients for thyroid surgery.
Patients and Methods: This prospective study was done during the period from June 2007 to November 2008. The study includes 141 patients with palpable solitary or multiple thyroid nodules. Only patients with normal or low TSH values were referred for ultrasound examination and ultrasound guided FNAC, which were done using fine needles (G 20).
Results: eleven patients (7.8%) have insuffici
The present work aims to study the effect of using an automatic thresholding technique to convert the features edges of the images to binary images in order to split the object from its background, where the features edges of the sampled images obtained from first-order edge detection operators (Roberts, Prewitt and Sobel) and second-order edge detection operators (Laplacian operators). The optimum automatic threshold are calculated using fast Otsu method. The study is applied on a personal image (Roben) and a satellite image to study the compatibility of this procedure with two different kinds of images. The obtained results are discussed.
Medical Ultrasound (US) has many features that make it widely used in the world. These features are safety, availability and low cost. However, despite these features, the ultrasound suffers from problems. These problems are speckle noise and artifacts. In this paper, a new method is proposed to improve US images by removing speckle noise and reducing artifacts to enhance the contrast of the image. The proposed method involves algorithms for image preprocessing and segmentation. A median filter is used to smooth the image in the pre-processing. Additionally, to obtain best results, applying median filter with different kernel values. We take the better output of the median filter and feed it into the Gaussian filter, which then
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Ultrasound imaging is often preferred over other medical imaging modalities because it is non-invasive, non-ionizing, and low-cost. However, the main weakness of medical ultrasound image is the poor quality of images, due to presence of speckle noise and blurring. Speckle is characteristic phenomenon in ultrasound images, which can be described as random multiplicative noise that occurrence is often undesirable, since it affects the tasks of human interpretation and diagnosis. Blurring is a form of bandwidth reduction of an ideal image owing to the imperfect image formation process. Image denoising involves processing of the image data to produce a visually high quality image. The denoising algorithms may be classified into two categorie
... Show MoreSegmentation is the process of partition digital images into different parts depending on texture, color, or intensity, and can be used in different fields in order to segment and isolate the area to be partitioned. In this work images of the Moon obtained through observations in Astronomy and space dep. College of science university of Baghdad by ( Toward space telescopes and widespread used of a CCD camera) . Different segmentation methods were used to segment lunar craters. Different celestial objects cause craters when they crash into the surface of the Moon like asteroids and meteorites. Thousands of craters appears on the Moon's surface with ranges in size from meter to many kilometers, it provide insights into the age and geology
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Magnetic Resonance Imaging (MRI) is one of the most important diagnostic tool. There are many methods to segment the
tumor of human brain. One of these, the conventional method that uses pure image processing techniques that are not preferred because they need human interaction for accurate segmentation. But unsupervised methods do not require any human interference and can segment the brain with high precision. In this project, the unsupervised classification methods have been used in order to detect the tumor disease from MRI images. These metho
... Show MoreA snake is an energy-minimizing spline guided by external
constraint forces and influenced by image forces that pull it toward features such as lines and edges. Snakes are active contour models: they lock onto nearby edges, localizing them accurately. Snakes provide a unified account of a number of visual problems, including detection of edges, lines, and motion tracking. We have used snakes successfully for segmentation, in which user-imposed constraint forces guide the snake near features of interest (anatomical structures). Magnetic Resonance Image (MRI) data set and Ultrasound images are used for our experiments.
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