DocumentCode
3127575
Title
Segmentation of thyroid gland in ultrasound image using neural network
Author
Garg, Hitendra ; Jindal, Abhishek
Author_Institution
Dept. of CSE, PEC Univ. of Technol., Chandigarh, India
fYear
2013
fDate
4-6 July 2013
Firstpage
1
Lastpage
5
Abstract
The thyroid gland is highly vascular organ, and lies in the anterior part of the neck just below the thyroid cartilage. Ultrasound imaging is most commonly used to detect and classify abnormalities of the thyroid gland. Other modalities (CT/MRI) are also used. There is a challenge to segment ultrasound medical image which is often blurred and consists of noise as other modalities like CT contains ionizing radiations and expensive. Thus, there is a need to apply a method to automated segment well the objects for future analysis without any assumptions about the object´s topology are made. Various methods or techniques are used for automatic segmention of thyroid gand but the application of neural network in image processing provides a better solution to segmentation problem. In this paper we use Feedforward neural network to classify the region using feature extraction and then segment it. Experiment and results are shown.
Keywords
biomedical ultrasonics; feature extraction; feedforward neural nets; image classification; image segmentation; medical image processing; object detection; CT; MRI; automated object segmentation; feature extraction; feedforward neural network; image processing; neck anterior part; region classification; thyroid cartilage; thyroid gland abnormalities classification; thyroid gland abnormalities detection; ultrasound medical image segmentation; vascular organ; Feature extraction; Histograms; Image segmentation; Neural networks; Noise; Training; Ultrasonic imaging; Feature extraction; Feed forward neural network; Image processing; Thyroid segmentation;
fLanguage
English
Publisher
ieee
Conference_Titel
Computing, Communications and Networking Technologies (ICCCNT),2013 Fourth International Conference on
Conference_Location
Tiruchengode
Print_ISBN
978-1-4799-3925-1
Type
conf
DOI
10.1109/ICCCNT.2013.6726797
Filename
6726797
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