Literature DB >> 19193513

Active contours guided by echogenicity and texture for delineation of thyroid nodules in ultrasound images.

Michalis A Savelonas1, Dimitris K Iakovidis, Ioannis Legakis, Dimitris Maroulis.   

Abstract

Thyroid nodules are solid or cystic lumps formed in the thyroid gland and may be caused by a variety of thyroid disorders. This paper presents a novel active contour model for precise delineation of thyroid nodules of various shapes according to their echogenicity and texture, as displayed in ultrasound (US) images. The proposed model, named joint echogenicity-texture (JET), is based on a modified Mumford-Shah functional that, in addition to regional image intensity, incorporates statistical texture information encoded by feature distributions. The distributions are aggregated within the functional through new log-likelihood goodness-of-fit terms. The JET model requires only a rough region of interest within the thyroid gland as input and automatically proceeds with precise delineation of the nodules, revealing their shape and size. The performance of the JET model was validated on a range of US images displaying hypoechoic and isoechoic nodules of various shapes. The quantification of the results shows that the JET model: 1) provides precise delineations of thyroid nodules as compared to "ground truth" delineations obtained by experts and 2) copes with the limitations of the previous thyroid US delineation approaches as it is capable of delineating thyroid nodules regardless of their echogenicity or shape.

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Year:  2008        PMID: 19193513     DOI: 10.1109/TITB.2008.2007192

Source DB:  PubMed          Journal:  IEEE Trans Inf Technol Biomed        ISSN: 1089-7771


  5 in total

1.  Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks.

Authors:  Jinlian Ma; Fa Wu; Tian'an Jiang; Qiyu Zhao; Dexing Kong
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-07-31       Impact factor: 2.924

2.  Tumor boundary detection in ultrasound imagery using multi-scale generalized gradient vector flow.

Authors:  Yi Le; Xianze Xu; Li Zha; Wencheng Zhao; Yanyan Zhu
Journal:  J Med Ultrason (2001)       Date:  2014-08-05       Impact factor: 1.314

3.  Recent advances in molecular diagnosis of thyroid cancer.

Authors:  Ioannis Legakis; Konstantinos Syrigos
Journal:  J Thyroid Res       Date:  2011-03-23

4.  Ultrasonic S-Detect mode for the evaluation of thyroid nodules: A meta-analysis.

Authors:  Jinyi Bian; Ruyue Wang; Mingxin Lin
Journal:  Medicine (Baltimore)       Date:  2022-08-26       Impact factor: 1.817

5.  Evaluation of Commonly Used Algorithms for Thyroid Ultrasound Images Segmentation and Improvement Using Machine Learning Approaches.

Authors:  Prabal Poudel; Alfredo Illanes; Debdoot Sheet; Michael Friebe
Journal:  J Healthc Eng       Date:  2018-09-23       Impact factor: 2.682

  5 in total

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