DocumentCode :
3380832
Title :
Thyroid classification and segmentation in ultrasound images using machine learning algorithms
Author :
Selvathi, D. ; Sharnitha, V.S.
Author_Institution :
Dept. of ECE, Mepco Schlenk Eng. Coll., Sivakasi, India
fYear :
2011
fDate :
21-22 July 2011
Firstpage :
836
Lastpage :
841
Abstract :
The clinical reports usually offer morphometric data in terms of change relative to a prior study. Therefore, to provide the information about an object clinically in terms of its size and shape, image segmentation and classification are important tools in medical image processing. Ultrasound is a versatile imaging technique that can reveal the internal structure of organs, often with astounding clarity. Ultrasound is unique in its ability to image patient anatomy and physiology in real time, providing an important, rapid and non-invasive means of evaluation. Ultrasound continues to make significant contributions to patient care by reassuring patients and enhancing their quality of life by helping physicians understand their anatomy in ways not possible with other techniques. US imaging is thus one of the most commonly used auxiliary tools in clinical diagnosis. In this paper, an automatic system is developed that classifies the thyroid images and segments the thyroid gland using machine learning algorithms. The classifiers such as SVM, ELM are used. The features such as mean, variance, Coefficient of Local Variation Feature, Histogram Feature, NMSID Feature, and Homogeneity are extracted and these features are used to train the classifiers such as ELM and SVM. The results are compared with the ground truth images obtained from the radiologist and the performance measure such as accuracy is evaluated. It is observed that the segmentation using ELM is better than SVM classifier.
Keywords :
biomedical ultrasonics; feature extraction; image classification; image segmentation; learning (artificial intelligence); medical image processing; patient care; support vector machines; ultrasonic imaging; ELM; NMSID feature; SVM classifier; clinical diagnosis; feature extraction; histogram feature; image classification; local variation feature; machine learning algorithm; mean; medical image processing; morphometric data; patient anatomy; patient care; physiology; quality of life; thyroid classification; thyroid gland; thyroid image; ultrasound image segmentation; variance; Accuracy; Biomedical imaging; Glands; Histograms; Image segmentation; Support vector machines; Ultrasonic imaging; ELM; SVM; Thyroid ultrasound images; classification; segmentation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing, Communication, Computing and Networking Technologies (ICSCCN), 2011 International Conference on
Conference_Location :
Thuckafay
Print_ISBN :
978-1-61284-654-5
Type :
conf
DOI :
10.1109/ICSCCN.2011.6024666
Filename :
6024666
Link To Document :
بازگشت