Thiroid Segmentation in Us

International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 2, Issue 12, December 2012)

398

Thyroid Segmentation on US Medical Images: An Overview Sheeja Agustin A

1, S. Suresh Babu

2

1Research Scholar, Department of Computer Science, Noorul Islam Centre for Higher Education, Noorul Islam University,

Kumaracoil, Tamil Nadu 2Principal, T K M College of Engineering, Kollam, Kerala

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 [10] includes 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

[11]. In this paper some methods are tested to detect and

segment thyroid US images.

Keyterms- Medical imaging, Thyroid, segmentation, FCM,

Histogram clustering, Quad tree, Region growing etc.

I. INTRODUCTION

Image processing [5] 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. 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. Different

Imaging technologies are:-Radiology, Magnetic resonance

imaging (MRI), Nuclear medicine, Photo acoustic imaging,

Breast thermograph, Tomography and ultrasound imaging.

In image processing segmentation algorithms constitute

one of the main focuses of research.

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. Different applications of image

segmentation are Medical Imaging, Locate objects in

satellite images, Face recognition, Fingerprint recognition,

Traffic control systems etc.

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 [12] means Segmentation of known anatomic

structures from medical images. Structures of interest

include organs such as cardiac ventricles or kidneys,

abnormalities such as tumors and cysts as well as other

structures such as bones, vessels, brain structures etc. 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

[12]. 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).

Thyroid produces thyroid hormones T4( thyroxin) and

T3(triiodothyronine). These thyroid hormones tell the cells

in the body how fast to use energy and create proteins. The

thyroid gland also makes calcitonin, a hormone that helps

to regulate calcium levels in the blood by inhibiting the

breakdown (reabsorption) of bone and increasing calcium

excretion from the kidneys. The Hypothalamus

releases(thyrotopin-releasing hormone), which in turn

causes the pituitary gland to release TSH( thyroid-

stimulating hormone), TSH stimulates the thyroid gland to

produce and secrete thyroid hormones. When there is

sufficient thyroid hormone in the blood, the amount of TSH

decreases to maintain constant amounts of thyroid

hormones, T4 and T3.



399

Fig I: Normal Thyroid US image

The rest of this paper is organized as follows. Section II

describes about preprocessing, section III describes about

segmentation, section IV describes about experimental

results .Finally, the conclusions as well as future directions

are summarized in Section V.

II. PREPROCESSING

The aim of pre-processing[5] is an improvement of the

image data that suppresses undesired distortions or

enhances some image features relevant for further

processing and analysis task. Neighboring pixels

corresponding to one real object have the same or similar

brightness value. Image pre-processing includes Geometric

correction – adjusts locations of pixels, and pixel values

and Radiometric correction – adjusts pixel values, analyst

judgment Image Preprocessing.

A. Noise Reduction

Noise is an important factor that influences image

quality. Noise reduction[5] is necessary to do image

processing and image interpretation so as to acquire useful

information that we want. In this paper the median filter is

used to reduce noise in an image.

B. EDGE Detection

Edges characterize object boundaries and are therefore

useful for segmentation, registration, and identification of

objects in a scene. The sobel operator is used in image

processing, particularly within edge detection algorithms.

The Canny edge detector is an edge detection operator that

uses a multi-stage algorithm to detect a wide range of edges

in images.

C. Enhancement

Contrast enhancement is a technique that able to

suppress speckle in thyroid ultrasound image. One of the

popular methods in contrast enhancement is histogram

equalization. Contrast enhancement is complete by

suppressing speckles – the modulation of image brightness

by random dark and bright region. The procedures start

with computation of calibrated radio frequency (RF)

spectra.

D. Histogram Equalization

The histogram equalization[5] is appropriate to enhance

a given image. The approach is to design a transformation

T (.) such that the gray values in the output is uniformly

distributed in [0, 1].

III. SEGMENTATION

Segmentation [5] is a tool that used widely in many

applications including 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. 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.

A. Fuzzy c-means Algorithm

Fuzzy c-means (FCM)[7] is a data clustering technique

in which a dataset is grouped into n clusters with every data

point in the dataset belonging to every cluster to a certain

degree. For example, a certain data point that lies close to

the center of a cluster[1] will have a high degree of

belonging or membership to that cluster and another data

point that lies far away from the center of a cluster will

have a low degree of belonging or membership to that

cluster.

B. Histogram Lustering

The threshold will most often be intensity or colour

value. Other forms of thresholding [7] exist where the

threshold is allowed to vary across the image, but

thresholding is a primitive technique, and will only work

for very simple segmentation tasks . Thresholding is a non-

linear operation that converts a grayscale image into a

binary image where the two levels are assigned to pixels

that are below or above the specified threshold value. the

selection of initial threshold value is depends upon the

histogram [8] of an image and the gray scale of an image.



400

C. QUAD Tree

The quad tree [6] data structure is widely used in digital

image processing for modeling spatial segmentation of

images and surfaces. A quad tree is a tree in which each

node has four descendants. Since most algorithms based on quad trees require complex navigation between nodes,

efficient traversal methods as well as efficient storage

techniques are of great interest. The quad tree data structure

is a tree in which each node has at most four children. In

digital image processing, quad trees are used to efficiently

store image segmentations.

D. Region Growing

Region growing [7] is a procedure that groups pixels or

sub regions into larger regions based on predefined criteria

for growth. The basic approach is to start with a set of seed

points and from these grow regions by appending to each

seed those neighboring pixels have predefined properties

similar to the seed.

E. Random Walker

The random walker[4] algorithm is an algorithm for

image segmentation. In the first description of the

algorithm,user interactively labels a small number of pixels

with known labels .

The unlabeled pixels are each imagined to release a

random walker, and the probability is computed that each

pixel's random walker first arrives at a seed bearing each

label, i.e., if a user places K seeds, each with a different

label, then it is necessary to compute, for each pixel, the

probability that a random walker leaving the pixel will first

arrive at each seed. This computation may be determined

analytically by solving a system of linear equations. After

computing these probabilities for each pixel, the pixel is

assigned to the label for which it is most likely to send a

random walker. The image is modeled as a graph, in which

each pixel corresponds to a node which is connected to

neighboring pixels by edges, and the edges are weighted to

reflect the similarity between the pixels. for an introduction

to random walks on graphs[2]).

IV. EXPERIMENTAL RESULTS

The experiment involves four thyroid ultrasound images.

For the software development, the MATLAB software is

used . The results are shown below.



401

Fig II: segmentation of thyroid UD image

Fig III: segmentation of thyroid with some disorder



402

Fig IV: segmentation of thyroid with some disorder



403

Fig V: segmentation of thyroid with benign nodule

Fig VI: segmentation of thyroid with benign nodule

V. CONCLUSION AND FUTURE WORK

In this work, segment the thyroid images using FCM,

Histogram clustering, Quad tree, Region growing and

Random Walker methods. The experimental results

shows that FCM efficiently segment thyroid images

compared to other methods. This work will be extended to

segment the thyroid images using fuzzy neurologic and

also classify the images to thyroid, non-thyroid, and

benign/malignant nodules.



404

REFERENCES

[1 ] Francesco Masulli Andrea Schenone, A ]fuzzy clustering based

segmentation system as support to diagnosis in medical imaging

Artificial Intelligence in Medicine 16 (1999) 129–147

[2 ] Songül Albayrak , Fatih Amasyalı Fuzzy C-means clustering on

Medical Diagnostic systems.

[3 ] L. Grady: Random Walks for Image Segmentation (http:/ / www.

cns. bu. edu/ ~lgrady/ grady2006random. pdf), IEEE Trans. on

Pattern Analysis and Machine Intelligence, Vol. 28, No. 11, pp. 1768–1783, Nov., 2006.

[4 ] P. Doyle, J. L. Snell: Random Walks and Electric Networks,

Mathematical Association of America, 1984.

[5 ] R. C. Gonzalez and R. E. Woods, Digital Image processing,Prentice-

Hall, Inc., New Jersey, 2002.

[6 ] M.H. Gross, O.G. Staadt, and R.Gatti. Efficient triangular surface

approximation using wavelets and quadtree data structure. IEEE Transaction On Visualization And Computer Graphics, 2(2):1–13,

June 1996.

[7 ] Digital Image Processing Using MATLAB 2nd edition by Gonzalez, Woods, and Eddins © 2009

[8 ] Ali Kermani Ahmad Ayatollahi Mohammad Talebi Segmentation of Medical Ultrasound Image Based on Local Histogram Range Image

3rd International Conference on Biomedical Engineering and

Informatics , 2010.

[9 ] Changming Zhu, Jun Ni, Yanbo Li1 and Guochang Gu General

Tendencies in Segmentation of Medical Ultrasound Images Fourth

International Conference on Internet Computing for Science and Engineering, 2009.

[10 ] Deepika Koundal1, Savita Gupta1 and Sukhwinder Singh1

“Computer-Aided Diagnosis of Thyroid Nodule: A Review”, International Journal of Computer Science & Engineering Survey

(IJCSES) Vol.3, No.4, August 2012.

[11 ] Nasrul Humaimi Mahmood and Akmal Hayati Rusli “Segmentation

and Area Measurement for Thyroid Ultrasound Image” International

Journal of Scientific & Engineering Research Volume 2, Issue 12, December-2011

[12 ] Chuan-Yu Chang, Yue-Fong Lei, Chin-Hsiao Tseng, and Shyang-

Rong Shih Thyroid Segmentation and Volume Estimation in Ultrasound Images, IEEE Transactionson

biomedicine,2010,vol.57.no.6

[13 ] Chuan-Yu Chang and Yong-Cheng Hong ,Pau-Choo Chung, Chin-

Hsiao Tseng, A Neural Network for Thyroid Segmentation and

volume Estimation in CT Images , IEEE Computational Intelligence Magazine,2011.

Documents

Thiroid Segmentation in Us