scholarly journals Texture Synthesis Based Thyroid Nodule Detection From Medical Ultrasound Images: Interpreting and Suppressing the Adversarial Effect of In-place Manual Annotation

2020 ◽  
Vol 8 ◽  
Author(s):  
Siqiong Yao ◽  
Junchi Yan ◽  
Mingyu Wu ◽  
Xue Yang ◽  
Weituo Zhang ◽  
...  
Keyword(s):  
Thyroid Nodule ◽  
Texture Synthesis ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Manual Annotation ◽  
Nodule Detection

scholarly journals Thyroid Nodules Classification in Medical Ultrasound Images using Deep Learning

2020 ◽  
Vol 9 (7) ◽  
pp. 1211-1215
Keyword(s):  
Deep Learning ◽  
Thyroid Nodule ◽  
Thyroid Nodules ◽  
Second Opinion ◽  
Learning Approach ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Cad System ◽  
Improve Accuracy

Ultrasound scanning is most excellent significant diagnosis techniques utilized for thyroid nodules identification. A thyroid nodule is unnecessary cells that can develop in your base of neck which can be normal or cancerous. Many Computer added diagnosis systems (CAD) have been developed as a second opinion for radiologist. The thyroid nodules classification using machine learning and deep learning approach is latest trend which is using to improve accuracy for differentiation of thyroid nodules from benign and malignant type. In this paper we review the most recent work on CAD system which uses different feature extraction technique and classifier used for thyroid nodules classification with deep learning approach. This paper we illustrate the result obtained by these studies and highlight the limitation of each proposed methods. Moreover we summarize convolution neural network (CNN) architecture for classification of thyroid nodule. This literature review is meant at researcher but it also useful for radiologist who is interesting in CAD tool in ultrasound imaging for second opinion.

scholarly journals Computer-Aided Thyroid Nodule Detection in Ultrasound Images

2005 ◽  
Author(s):  
D.E. Maroulis ◽  
M.A. Savelonas ◽  
S.A. Karkanis ◽  
D.K. Iakovidis ◽  
N. Dimitropoulos
Keyword(s):  
Thyroid Nodule ◽  
Ultrasound Images ◽  
Nodule Detection ◽  
Computer Aided

ΤND: A Thyroid Nodule Detection System for Analysis of Ultrasound Images and Videos

2010 ◽  
Vol 36 (3) ◽  
pp. 1271-1281 ◽  
Author(s):  
Eystratios G. Keramidas ◽  
Dimitris Maroulis ◽  
Dimitris K. Iakovidis
Keyword(s):  
Thyroid Nodule ◽  
Detection System ◽  
Ultrasound Images ◽  
Nodule Detection

scholarly journals A Novel Neutrosophic Method for Automatic Seed Point Selection in Thyroid Nodule Images

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
S. O. Haji ◽  
R. Z. Yousif
Keyword(s):  
Thyroid Nodule ◽  
Automatic Segmentation ◽  
Similarity Index ◽  
Added Value ◽  
Speckle Reduction ◽  
Ultrasound Images ◽  
Seed Selection ◽  
Point Selection ◽  
Seed Point ◽  
Nodule Detection

The thyroid nodule is one of the endocrine issues caused by an irregular cell development. This rate of survival can be improved by earlier nodule detection. Accordingly, the accurate recognition of the nodule is of the utmost importance in providing powerful results in building the survival rate. The reduction in the accuracy of manual or semiautomatic segmentation methods for thyroid nodule detection is due to many factors, basically, the lack of experience of the sonographer and latency of operation. Most lesion regions in ultrasound images are homogeneous. Therefore, the value of entropy in these regions is high compared to its neighbours. Based on this criterion, a novel procedure for automatically selecting the seed point in thyroid nodule images is proposed. The proposed system consists of three components: neutrosophic image enhancement and speckle reduction to reduce speckle noise and automatic seed selection algorithm extracted from the centre of candidate block in ultrasound thyroid images based on the principle that most of its Higher Order Spectra Entropies (HOSE) from Radon Transform (RT) at different angles are within the range between average and maximum entropies, and the region growing image segmentation is applied with the constant threshold. The performance of proposed automatic segmentation method is compared with other methods in terms of calculating, True Positive (TP) value (96.44 ± 3.01%), False Positive (FP) value (3.55 ± 1.45%), Dice Coefficient (DC) value (92.24 ± 6.47%), Similarity Index (SI) (80.57 ± 1.06%), and Hausdroff Distance (HD) (0.42 ± 0.24 pixels). The proposed system can be considered as an added value to the malignancy diagnosis in thyroid nodule by an endocrinologist.

scholarly journals Motion blur invariant for estimating motion parameters of medical ultrasound images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Barmak Honarvar Shakibaei Asli ◽  
Yifan Zhao ◽  
John Ahmet Erkoyuncu
Keyword(s):  
Image Quality Assessment ◽  
Medical Image Analysis ◽  
Main Idea ◽  
Delta Function ◽  
Motion Blur ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Fetal Ultrasound ◽  
Dirac Delta ◽  
Motion Parameters

AbstractHigh-quality medical ultrasound imaging is definitely concerning motion blur, while medical image analysis requires motionless and accurate data acquired by sonographers. The main idea of this paper is to establish some motion blur invariant in both frequency and moment domain to estimate the motion parameters of ultrasound images. We propose a discrete model of point spread function of motion blur convolution based on the Dirac delta function to simplify the analysis of motion invariant in frequency and moment domain. This model paves the way for estimating the motion angle and length in terms of the proposed invariant features. In this research, the performance of the proposed schemes is compared with other state-of-the-art existing methods of image deblurring. The experimental study performs using fetal phantom images and clinical fetal ultrasound images as well as breast scans. Moreover, to validate the accuracy of the proposed experimental framework, we apply two image quality assessment methods as no-reference and full-reference to show the robustness of the proposed algorithms compared to the well-known approaches.

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