International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-2 Issue-3, February 2013 88 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: C1020022313 /2013©BEIESP A Review of Thyroid Disorder Detection, Segmentation and Classification on Medical Images Sheeja Agustin A, S. Suresh Babu 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. Different thyroid disorders include Hyperthyroidism, Hypothyroidism, goiter, 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. In this paper, a broad review on the researches that are developed for the thyroid diagnosis using medical images is presented along with the classification. A short description about thyroid, thyroid diseases and thyroid diagnosis are also presented. Key terms:-Medical imaging, Thyroid, Thyroid disorders, Segmentation, Classification. I. INTRODUCTION Image processing is any form of signal processing for which the input is an image such as photograph or video frame, the output of image processing may be either an image or parameters related to the image. Image processing usually refers to digital image processing. Digital image processing is the use of computer algorithms to perform image processing on digital images. Different Imaging technologies are:-Radiology, Magnetic resonance imaging (MRI), Nuclear medicine, Photo acoustic imaging, Breast thermograph, Tomography and ultrasound imaging. Medical imaging is the technique and process used to create images of the human body for clinical purpose or medical science including the study of normal anatomy and physiology. Image segmentation is the process of partitioning an image into multiple segment or set of pixels used to locate object and boundaries. Each of the pixels in a region is similar with respect to some characteristics such as color, intensity or texture. 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. Medical image segmentation methods generally have restrictions because medical images have very similar gray level and texture among the interested objects. Therefore, significant segmentation error may occur. Manuscript Received on February, 2013. Sheeja Agustin, Research Scholar, Computer science & Engineering, Noorul Islam Centre for Higher Education, Noorul Islam University, Kumaracoil,TamilNadu, India. Dr.S.Suresh Babu, Electronics & Communication Engineering, Professor, TKM College of Engineering, Kollam, India. Another difficulty may arise due to the lack of sufficient training samples. In the field of Image analysis segmentation of medical images is a challenging problem due to poor resolution and weak contrast of medical images. Medical image segmentation means Segmentation of known anatomic structures from medical images. Now a day’s Medical image analysis has played more and more important role in many clinical procedures and in detecting different types of human diseases. Now a day’s most of the peoples have thyroid diseases. For diagnosing thyroid diseases, Ultrasound (US) and Computerized Tomography (CT) are two of the most popular imaging modalities. US imaging is inexpensive, non-invasive and easy to use. US images are often adopted due to their cost-effectiveness and portability in smaller hospitals. The thyroid is well suited to ultrasound study because of its superficial location, size and echogenicity .Computer-Aided Diagnosis (CAD) of Thyroid Ultrasound is necessary in order to delineating nodules, classifying benign/malignant and estimating the volumes of thyroid tissues to increase reliability and reduce invasive operations such as biopsy and Fine Needle Aspiration (FNA). Fig1: Normal Thyroid US image In digital image classification the conventional statistical approaches for image classification use only the gray values. Different advanced techniques in image classification like Artificial Neural Networks (ANN), Support Vector Machines (SVM), Fuzzy measures, Genetic Algorithms (GA), Fuzzy support Vector Machines (FSVM) and Genetic Algorithms with Neural Networks are being developed for image classification. The use of textural features in ANN helps to resolve misclassification. SVM was found competitive with the best available machine learning algorithms in classifying high-dimensional data sets. ANN is a parallel distributed processor that has a natural tendency for storing experiential knowledge. Image classification using neural networks is done by texture feature extraction and then applying the back propagation algorithm. The rest of this paper is organized as follows. In Section II describes about different disorders and symptoms, section III describes review