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Yadav N, Dass R, Virmani J. A systematic review of machine learning based thyroid tumor characterisation using ultrasonographic images. J Ultrasound 2024; 27:209-224. [PMID: 38536643 PMCID: PMC11178762 DOI: 10.1007/s40477-023-00850-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 11/22/2023] [Indexed: 06/15/2024] Open
Abstract
Ultrasonography is widely used to screen thyroid tumors because it is safe, easy to use, and low-cost. However, it is simultaneously affected by speckle noise and other artifacts, so early detection of thyroid abnormalities becomes difficult for the radiologist. Therefore, various researchers continuously address the limitations of sonography and improve the diagnosis potential of US images for thyroid tissue from the last three decays. Accordingly, the present study extensively reviewed various CAD systems used to classify thyroid tumor US (TTUS) images related to datasets, despeckling algorithms, segmentation algorithms, feature extraction and selection, assessment parameters, and classification algorithms. After the exhaustive review, the achievements and challenges have been reported, and build a road map for the new researchers.
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Affiliation(s)
- Niranjan Yadav
- Department of Electronics and Communication Engineering, Deenbandhu Chhotu Ram University of Science and Technology Murthal, Sonepat, 131039, India.
| | - Rajeshwar Dass
- Department of Electronics and Communication Engineering, Deenbandhu Chhotu Ram University of Science and Technology Murthal, Sonepat, 131039, India
| | - Jitendra Virmani
- Central Scientific Instruments Organization, Council of Scientific and Industrial Research, Chandigarh, 160030, India
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2
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Chen H, Yu MA, Chen C, Zhou K, Qi S, Chen Y, Xiao R. FDE-net: Frequency-domain enhancement network using dynamic-scale dilated convolution for thyroid nodule segmentation. Comput Biol Med 2023; 153:106514. [PMID: 36628913 DOI: 10.1016/j.compbiomed.2022.106514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 12/22/2022] [Accepted: 12/31/2022] [Indexed: 01/05/2023]
Abstract
Thyroid nodules, a common disease of endocrine system, have a probability of nearly 10% to turn into malignant nodules and thus pose a serious threat to health. Automatic segmentation of thyroid nodules is of great importance for clinicopathological diagnosis. This work proposes FDE-Net, a combined segmental frequency domain enhancement and dynamic scale cavity convolutional network for thyroid nodule segmentation. In FDE-Net, traditional image omics method is introduced to enhance the feature image in the segmented frequency domain. Such an approach reduces the influence of noise and strengthens the detail and contour information of the image. The proposed method introduces a cascade cross-scale attention module, which addresses the insensitivity of the network to the change in target scale by fusing the features of different receptive fields and improves the ability of the network to identify multiscale target regions. It repeatedly uses the high-dimensional feature image to improve segmentation accuracy in accordance with the simple structure of thyroid nodules. In this study, 1355 ultrasound images are used for training and testing. Quantitative evaluation results showed that the Dice coefficient of FDE-Net in thyroid nodule segmentation was 83.54%, which is better than other methods. Therefore, FDE-Net can enable the accurate and rapid segmentation of thyroid nodules.
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Affiliation(s)
- Hongyu Chen
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Ming-An Yu
- Department of Interventional Medicine, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Cheng Chen
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Kangneng Zhou
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Siyu Qi
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Yunqing Chen
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Ruoxiu Xiao
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China; Shunde Innovation School, University of Science and Technology Beijing, Foshan, 100024, China.
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3
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Nie X, Zhou X, Tong T, Lin X, Wang L, Zheng H, Li J, Xue E, Chen S, Zheng M, Chen C, Du M. N-Net: A novel dense fully convolutional neural network for thyroid nodule segmentation. Front Neurosci 2022; 16:872601. [PMID: 36117632 PMCID: PMC9475170 DOI: 10.3389/fnins.2022.872601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 08/11/2022] [Indexed: 11/13/2022] Open
Abstract
Medical image segmentation is an essential component of computer-aided diagnosis (CAD) systems. Thyroid nodule segmentation using ultrasound images is a necessary step for the early diagnosis of thyroid diseases. An encoder-decoder based deep convolutional neural network (DCNN), like U-Net architecture and its variants, has been extensively used to deal with medical image segmentation tasks. In this article, we propose a novel N-shape dense fully convolutional neural network for medical image segmentation, referred to as N-Net. The proposed framework is composed of three major components: a multi-scale input layer, an attention guidance module, and an innovative stackable dilated convolution (SDC) block. First, we apply the multi-scale input layer to construct an image pyramid, which achieves multi-level receiver field sizes and obtains rich feature representation. After that, the U-shape convolutional network is employed as the backbone structure. Moreover, we use the attention guidance module to filter the features before several skip connections, which can transfer structural information from previous feature maps to the following layers. This module can also remove noise and reduce the negative impact of the background. Finally, we propose a stackable dilated convolution (SDC) block, which is able to capture deep semantic features that may be lost in bilinear upsampling. We have evaluated the proposed N-Net framework on a thyroid nodule ultrasound image dataset (called the TNUI-2021 dataset) and the DDTI publicly available dataset. The experimental results show that our N-Net model outperforms several state-of-the-art methods in the thyroid nodule segmentation tasks.
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Affiliation(s)
- Xingqing Nie
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
- Fujian Key Lab of Medical Instrumentation and Pharmaceutical Technology, Fuzhou University, Fuzhou, China
| | - Xiaogen Zhou
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
- Fujian Key Lab of Medical Instrumentation and Pharmaceutical Technology, Fuzhou University, Fuzhou, China
| | - Tong Tong
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
- Fujian Key Lab of Medical Instrumentation and Pharmaceutical Technology, Fuzhou University, Fuzhou, China
- Imperial Vision Technology, Fuzhou, China
- *Correspondence: Tong Tong
| | - Xingtao Lin
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
- Fujian Key Lab of Medical Instrumentation and Pharmaceutical Technology, Fuzhou University, Fuzhou, China
| | - Luoyan Wang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
- Fujian Key Lab of Medical Instrumentation and Pharmaceutical Technology, Fuzhou University, Fuzhou, China
| | - Haonan Zheng
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
- Fujian Key Lab of Medical Instrumentation and Pharmaceutical Technology, Fuzhou University, Fuzhou, China
| | - Jing Li
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
- Fujian Key Lab of Medical Instrumentation and Pharmaceutical Technology, Fuzhou University, Fuzhou, China
| | - Ensheng Xue
- Fujian Medical Ultrasound Research Institute, Fuzhou, China
- Department of Ultrasound, Fujian Medical University Union Hospital, Fuzhou, China
| | - Shun Chen
- Fujian Medical Ultrasound Research Institute, Fuzhou, China
- Department of Ultrasound, Fujian Medical University Union Hospital, Fuzhou, China
- Shun Chen
| | - Meijuan Zheng
- Fujian Medical Ultrasound Research Institute, Fuzhou, China
- Department of Ultrasound, Fujian Medical University Union Hospital, Fuzhou, China
| | - Cong Chen
- Fujian Medical Ultrasound Research Institute, Fuzhou, China
- Department of Ultrasound, Fujian Medical University Union Hospital, Fuzhou, China
| | - Min Du
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
- Fujian Key Lab of Medical Instrumentation and Pharmaceutical Technology, Fuzhou University, Fuzhou, China
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Yang Q, Geng C, Chen R, Pang C, Han R, Lyu L, Zhang Y. DMU-Net: Dual-route mirroring U-Net with mutual learning for malignant thyroid nodule segmentation. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10228298] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010–January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. The breast tumor research community can utilize this survey as a basis for their current and future studies.
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Koundal D, Sharma B, Guo Y. Intuitionistic based segmentation of thyroid nodules in ultrasound images. Comput Biol Med 2020; 121:103776. [PMID: 32568671 DOI: 10.1016/j.compbiomed.2020.103776] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 04/21/2020] [Accepted: 04/21/2020] [Indexed: 10/24/2022]
Abstract
Accurate delineation of thyroid nodules in ultrasound images is vital for computer-aided diagnosis. Most segmentation methods are semi-automated for thyroid nodules and require manual intervention, which increases the processing time and errors. We propose an automated intuitionistic fuzzy active contour method (IFACM) that integrates intuitionistic fuzzy clustering with an active contour for thyroid nodule segmentation using ultrasound images. Intuitionistic fuzzy clustering is used for the initialization of an active contour and estimation of the parameters required to automatically control the curve evolution. The IFACM was tested extensively on both artificial and real ultrasound images. The IFACM obtained a higher value of true positive (95.1% ± 2.86%), overlap metric (93.1 ± 2.95%), and dice coefficient (90.90 ± 3.08), indicating that the boundary delineated by the IFACM fits best to true nodules. Moreover, it obtained a lower value of false positive (04.1% ± 3.24%) and Hausdorff distance (0.50 ± 0.21 in pixels), further verifying the higher similarity of shape and boundary, respectively. According to the significance test, the results of the proposed method were more significant than those of the other segmentation methods. The main benefit of the IFACM is the automatic identification of nodules on the basis of image characteristics, which eliminates manual intervention. In all the experiments, all initial contours were automatically defined closer to the boundaries of the nodule, which is a benefit of the IFACM. Moreover, this method can segment multiple nodules in a single image efficiently.
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Affiliation(s)
- Deepika Koundal
- Department of Virtualization, School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India.
| | - Bhisham Sharma
- Chitkara University School of Engineering and Technology, Chitkara University, Himachal Pradesh, India.
| | - Yanhui Guo
- Department of Computer Science, University of Illinois at Springfield, Springfield, IL, USA.
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Kumar V, Webb J, Gregory A, Meixner DD, Knudsen JM, Callstrom M, Fatemi M, Alizad A. Automated Segmentation of Thyroid Nodule, Gland, and Cystic Components From Ultrasound Images Using Deep Learning. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:63482-63496. [PMID: 32995106 PMCID: PMC7521441 DOI: 10.1109/access.2020.2982390] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Sonographic features associated with margins, shape, size, and volume of thyroid nodules are used to assess their risk of malignancy. Automatically segmenting nodules from normal thyroid gland would enable an automated estimation of these features. A novel multi-output convolutional neural network algorithm with dilated convolutional layers is presented to segment thyroid nodules, cystic components inside the nodules, and normal thyroid gland from clinical ultrasound B-mode scans. A prospective study was conducted, collecting data from 234 patients undergoing a thyroid ultrasound exam before biopsy. The training and validation sets encompassed 188 patients total; the testing set consisted of 48 patients. The algorithm effectively segmented thyroid anatomy into nodules, normal gland, and cystic components. The algorithm achieved a mean Dice coefficient of 0.76, a mean true positive fraction of 0.90, and a mean false positive fraction of 1.61×10-6. The values are on par with a conventional seeded algorithm. The proposed algorithm eliminates the need for a seed in the segmentation process, thus automatically detecting and segmenting the thyroid nodules and cystic components. The detection rate for thyroid nodules and cystic components was 82% and 44%, respectively. The inference time per image, per fold was 107ms. The mean error in volume estimation of thyroid nodules for five select cases was 7.47%. The algorithm can be used for detection, segmentation, size estimation, volume estimation, and generating thyroid maps for thyroid nodules. The algorithm has applications in point of care, mobile health monitoring, improving workflow, reducing localization time, and assisting sonographers with limited expertise.
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Affiliation(s)
- Viksit Kumar
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine & Science, Rochester, MN, 55905, USA
| | - Jeremy Webb
- Department of Radiology, Mayo Clinic College of Medicine & Science, Rochester, MN, 55905, USA
| | - Adriana Gregory
- Department of Radiology, Mayo Clinic College of Medicine & Science, Rochester, MN, 55905, USA
| | - Duane D Meixner
- Department of Radiology, Mayo Clinic College of Medicine & Science, Rochester, MN, 55905, USA
| | - John M Knudsen
- Department of Radiology, Mayo Clinic College of Medicine & Science, Rochester, MN, 55905, USA
| | - Matthew Callstrom
- Department of Radiology, Mayo Clinic College of Medicine & Science, Rochester, MN, 55905, USA
| | - Mostafa Fatemi
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine & Science, Rochester, MN, 55905, USA
| | - Azra Alizad
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine & Science, Rochester, MN, 55905, USA
- Department of Radiology, Mayo Clinic College of Medicine & Science, Rochester, MN, 55905, USA
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8
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Chen J, You H, Li K. A review of thyroid gland segmentation and thyroid nodule segmentation methods for medical ultrasound images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 185:105329. [PMID: 31955006 DOI: 10.1016/j.cmpb.2020.105329] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 01/08/2020] [Accepted: 01/08/2020] [Indexed: 05/07/2023]
Abstract
Background and objective Thyroid image segmentation is an indispensable part in computer-aided diagnosis systems and medical image diagnoses of thyroid diseases. There have been dozens of studies on thyroid gland segmentation and thyroid nodule segmentation in ultrasound images. The aim of this work is to categorize and review the thyroid gland segmentation and thyroid nodule segmentation methods in medical ultrasound. Methods This work proposes a categorization approach of thyroid gland segmentation and thyroid nodule segmentation methods according to the theoretical bases of segmentation methods. The segmentation methods are categorized into four groups, including contour and shape based methods, region based methods, machine and deep learning methods and hybrid methods. The representative articles are reviewed with detailed descriptions of methods and analyses of correlations between methods. The evaluation metrics for the reviewed segmentation methods are named uniformly in this work. The segmentation performance results using the uniformly named evaluation metrics are compared. Results After careful investigation, 28 representative papers are selected for comprehensive analyses and comparisons in this review. The dominant thyroid gland segmentation methods are machine and deep learning methods. The training of massive data makes these models have better segmentation performance and robustness. But deep learning models usually require plenty of marked training data and long training time. For thyroid nodule segmentation, the most common methods are contour and shape based methods, which have good segmentation performance. However, most of them are tested on small datasets. Conclusions Based on the comprehensive consideration of application scenario, image features, method practicability and segmentation performance, the appropriate segmentation method for specific situation can be selected. Furthermore, several limitations of current thyroid ultrasound image segmentation methods are presented, which may be overcome in future studies, such as the segmentation of pathological or abnormal thyroid glands, identification of the specific nodular diseases, and the standard thyroid ultrasound image datasets.
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Affiliation(s)
- Junying Chen
- School of Software Engineering, South China University of Technology, Guangzhou, Guangdong 510006, China.
| | - Haijun You
- School of Software Engineering, South China University of Technology, Guangzhou, Guangdong 510006, China.
| | - Kai Li
- Department of Ultrasound, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong 510630, China.
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Chiu LY, Chen A. A Variance-reduction Approach to Detection of the Thyroid-nodule Boundary on Ultrasound Images. ULTRASONIC IMAGING 2019; 41:206-230. [PMID: 30990130 DOI: 10.1177/0161734619839648] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
To perform computer-aided diagnosis of the thyroid nodules on ultrasound images, the location and boundary of nodules should be clearly defined. However, the identification of thyroid nodule boundary is a difficult issue due to the biological characteristics of the nodules, the physics and quality of ultrasound imaging, and the subjective factors and operating conditions of the operator. In this study, we propose a novel and semiautomatic method for detecting the boundary of thyroid nodule based on the Variance-Reduction (V-R) statistics without image preprocessing. The region of interest (ROI) is first automatically generated according to the initial inputs of the nodule's major and minor axes. The boundary candidate pixel points are then extracted by using the V-R statistics from the grayscale values of all pixel points in the ROI. Three filtering methods are further applied to eliminate the outlier pixel points to ensure that the remaining candidate pixel points are located on the nodule boundary. Finally, the remaining pixel points are smoothened and linked together to form the final boundary. The proposed method is validated with ultrasound images of 538 thyroid nodules, with manual delineation by experienced radiologist as gold standard. The effectiveness is evaluated and compared with previous publications using boundary error metrics and overlapping area metrics with the same data set. The results show that the normalized average mean boundary error is 1.02%, the true positive overlapping area ratio achieves 93.66% and false positive overlapping area ratio is limited to 7.68%. In conclusion, our proposed method is reliable and effective in detecting thyroid nodule boundary on ultrasound images.
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Affiliation(s)
- Ling-Ying Chiu
- 1 Institute of Industrial Engineering, National Taiwan University, Taipei
| | - Argon Chen
- 1 Institute of Industrial Engineering, National Taiwan University, Taipei
- 2 Department of Mechanical Engineering, National Taiwan University, Taipei
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Prochazka A, Gulati S, Holinka S, Smutek D. Classification of Thyroid Nodules in Ultrasound Images Using Direction-Independent Features Extracted by Two-Threshold Binary Decomposition. Technol Cancer Res Treat 2019; 18:1533033819830748. [PMID: 30774015 PMCID: PMC6379796 DOI: 10.1177/1533033819830748] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
In recent years, several computer-aided diagnosis systems emerged for the diagnosis of
thyroid gland disorders using ultrasound imaging. These systems based on machine learning
algorithms may offer a second opinion to radiologists by evaluating a malignancy risk of
thyroid tissue, thus increasing the overall diagnostic accuracy of ultrasound imaging.
Although current computer-aided diagnosis systems exhibit promising results, their use in
clinical practice is limited. One of the main limitations is that the majority of them use
direction-dependent features. Our intention has been to design a computer-aided diagnosis
system, which will use only direction-independent features, that is, it will not be
dependent on the orientation and the inclination angle of the ultrasound probe when
acquiring the image. We have, therefore, applied histogram analysis and segmentation-based
fractal texture analysis algorithm, which calculates direction-independent features only.
In our study, 40 thyroid nodules (20 malignant and 20 benign) were used to extract several
features, such as histogram parameters, fractal dimension, and mean brightness value in
different grayscale bands (obtained by 2-threshold binary decomposition). The features
were then used in support vector machine and random forests classifiers to differentiate
nodules into malignant and benign classes. Using leave-one-out cross-validation method,
the overall accuracy was 92.42% for random forests and 94.64% for support vector machine.
Results show that both methods are useful in practice; however, support vector machine
provides better results for this application. Proposed computer-aided diagnosis system can
provide support to radiologists in their current diagnosis of thyroid nodules, whereby it
can optimize the overall accuracy of ultrasound imaging.
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Affiliation(s)
- Antonin Prochazka
- 1 Institute of Biophysics and Informatics, 1st Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Sumeet Gulati
- 2 International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic
| | - Stepan Holinka
- 3 Third Department of Medicine, General University Hospital and 1st Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Daniel Smutek
- 1 Institute of Biophysics and Informatics, 1st Faculty of Medicine, Charles University, Prague, Czech Republic.,3 Third Department of Medicine, General University Hospital and 1st Faculty of Medicine, Charles University, Prague, Czech Republic
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Abbasian Ardakani A, Bitarafan-Rajabi A, Mohammadzadeh A, Mohammadi A, Riazi R, Abolghasemi J, Homayoun Jafari A, Bagher Shiran M. A Hybrid Multilayer Filtering Approach for Thyroid Nodule Segmentation on Ultrasound Images. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2019; 38:629-640. [PMID: 30171626 DOI: 10.1002/jum.14731] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Revised: 05/31/2018] [Accepted: 06/03/2018] [Indexed: 06/08/2023]
Abstract
OBJECTIVES Speckle noise is the main factor that degrades ultrasound image contrast and segmentation failure. Determining an effective filter can reduce speckle noise and improve segmentation performances. The aim of this study was to define a useful filter to improve the segmentation outcome. METHODS Twelve filters, including median, hybrid median (Hmed), Fourier Butterworth, Fourier ideal, wavelet (Wlet), homomorphic Fourier Butterworth, homomorphic Fourier ideal, homomorphic wavelet (Hmp_Wlet), frost, anisotropic diffusion, probabilistic patch-based (PPB), and homogeneous area filters, were used to find the best filter(s) to prepare thyroid nodule segmentation. A receiver operating characteristic (ROC) analysis was used for filter evaluation in the nodule segmentation process. Accordingly, 10 morphologic parameters were measured from segmented regions to find the best parameters that predict the segmentation performance. RESULTS The best segmentation performance was reached by using 4 hybrid filters that mainly contain contrast-limited adaptive histogram equalization, Wlet, Hmed, Hmp_Wlet, and PPB filters. The area under the ROC curve for these filters ranged from 0.900 to 0.943 in comparison with the original image, with an area under the curve of 0.685. From 10 morphologic parameters, the area, convex area, equivalent diameter, solidity, and extent can evaluate segmentation performance. CONCLUSIONS Hybrid filters that contain contrast-limited adaptive histogram equalization, Wlet, Hmed, Hmp_Wlet, and PPB filters have a high potential to provide good conditions for thyroid nodule segmentation in ultrasound images. In addition to an ROC analysis, morphometry of a segmented region can be used to evaluate segmentation performances.
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Affiliation(s)
- Ali Abbasian Ardakani
- Department of Medical Physics, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Ahmad Bitarafan-Rajabi
- Department of Medical Physics, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- School of Medicine, Department of Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Mohammadzadeh
- Department of Radiology, Tehran University of Medical Sciences, Tehran, Iran
| | - Afshin Mohammadi
- Department of Radiology, Faculty of Medicine, Imam Khomeini Hospital, Urmia University of Medical Sciences, Urmia, Iran
| | - Reza Riazi
- Department of Medical Physics, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Jamileh Abolghasemi
- Department of Rajaei Cardiovascular, Medical, and Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Department of Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir Homayoun Jafari
- Department of Medical Physics & Biomedical Engineering, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Mohammad Bagher Shiran
- Department of Medical Physics, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
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Prochazka A, Gulati S, Holinka S, Smutek D. Patch-based classification of thyroid nodules in ultrasound images using direction independent features extracted by two-threshold binary decomposition. Comput Med Imaging Graph 2018; 71:9-18. [PMID: 30453231 DOI: 10.1016/j.compmedimag.2018.10.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Revised: 09/27/2018] [Accepted: 10/02/2018] [Indexed: 01/18/2023]
Abstract
Ultrasound imaging of the thyroid gland is considered to be the best diagnostic choice for evaluating thyroid nodules in early stages, since it has been marked as cost-effective, non-invasive and risk-free. Computer aided diagnosis (CAD) systems can offer a second opinion to radiologists, thereby increasing the overall diagnostic accuracy of ultrasound imaging. Although current CAD systems exhibit promising results, their use in clinical practice is limited. Some of the main limitations are that the majority use direction dependent features so, they are only compatible with static images in just one plane (axial or longitudinal), requiring precise segmentation of a nodule. Our intention has been to design a CAD system which will use only direction independent features i.e., not dependent upon the orientation or inclination angle of the ultrasound probe when acquiring the image. In this study, 60 thyroid nodules (20 malignant, 40 benign) were divided into small patches of 17 × 17 pixels, which were then used to extract several direction independent features by employing Two-Threshold Binary Decomposition, a method that decomposes an image into the set of binary images. The features were then used in Random Forests (RF) and Support Vector Machine (SVM) classifiers to categorize nodules into malignant and benign classes. Classification was evaluated using group 10-fold cross-validation method. Performance on individual patches was then averaged to classify whole nodules with the following results: overall accuracy, sensitivity, specificity and area under receiver operating characteristics (ROC) curve: 95%, 95%, 95%, 0.971 for RF and; 91.6%, 95%, 90%, 0.965 for SVM respectively. The patch-based CAD system we present can provide support to radiologists in their current diagnosis of thyroid nodules, whereby it can increase the overall accuracy of ultrasound imaging.
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Affiliation(s)
- Antonin Prochazka
- Institute of Biophysics and Informatics, 1(st) Faculty of Medicine, Charles University, Salmovska 1, 120 00, Prague, Czech Republic.
| | - Sumeet Gulati
- International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, 656 91, Brno, Czech Republic
| | - Stepan Holinka
- 3(rd) Department of Medicine, 1(st) Faculty of Medicine, Charles University and General University Hospital in Prague, U Nemocnice 1, 128 08, Praha 2, Czech Republic
| | - Daniel Smutek
- Institute of Biophysics and Informatics, 1(st) Faculty of Medicine, Charles University, Salmovska 1, 120 00, Prague, Czech Republic; 3(rd) Department of Medicine, 1(st) Faculty of Medicine, Charles University and General University Hospital in Prague, U Nemocnice 1, 128 08, Praha 2, Czech Republic
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13
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Meiburger KM, Acharya UR, Molinari F. Automated localization and segmentation techniques for B-mode ultrasound images: A review. Comput Biol Med 2017; 92:210-235. [PMID: 29247890 DOI: 10.1016/j.compbiomed.2017.11.018] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Revised: 11/30/2017] [Accepted: 11/30/2017] [Indexed: 12/14/2022]
Abstract
B-mode ultrasound imaging is used extensively in medicine. Hence, there is a need to have efficient segmentation tools to aid in computer-aided diagnosis, image-guided interventions, and therapy. This paper presents a comprehensive review on automated localization and segmentation techniques for B-mode ultrasound images. The paper first describes the general characteristics of B-mode ultrasound images. Then insight on the localization and segmentation of tissues is provided, both in the case in which the organ/tissue localization provides the final segmentation and in the case in which a two-step segmentation process is needed, due to the desired boundaries being too fine to locate from within the entire ultrasound frame. Subsequenly, examples of some main techniques found in literature are shown, including but not limited to shape priors, superpixel and classification, local pixel statistics, active contours, edge-tracking, dynamic programming, and data mining. Ten selected applications (abdomen/kidney, breast, cardiology, thyroid, liver, vascular, musculoskeletal, obstetrics, gynecology, prostate) are then investigated in depth, and the performances of a few specific applications are compared. In conclusion, future perspectives for B-mode based segmentation, such as the integration of RF information, the employment of higher frequency probes when possible, the focus on completely automatic algorithms, and the increase in available data are discussed.
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Affiliation(s)
- Kristen M Meiburger
- Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | - U Rajendra Acharya
- Department of Electronic & Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Filippo Molinari
- Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy.
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14
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Ma J, Wu F, Zhu J, Xu D, Kong D. A pre-trained convolutional neural network based method for thyroid nodule diagnosis. ULTRASONICS 2017; 73:221-230. [PMID: 27668999 DOI: 10.1016/j.ultras.2016.09.011] [Citation(s) in RCA: 130] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Revised: 07/26/2016] [Accepted: 09/11/2016] [Indexed: 05/22/2023]
Abstract
In ultrasound images, most thyroid nodules are in heterogeneous appearances with various internal components and also have vague boundaries, so it is difficult for physicians to discriminate malignant thyroid nodules from benign ones. In this study, we propose a hybrid method for thyroid nodule diagnosis, which is a fusion of two pre-trained convolutional neural networks (CNNs) with different convolutional layers and fully-connected layers. Firstly, the two networks pre-trained with ImageNet database are separately trained. Secondly, we fuse feature maps learned by trained convolutional filters, pooling and normalization operations of the two CNNs. Finally, with the fused feature maps, a softmax classifier is used to diagnose thyroid nodules. The proposed method is validated on 15,000 ultrasound images collected from two local hospitals. Experiment results show that the proposed CNN based methods can accurately and effectively diagnose thyroid nodules. In addition, the fusion of the two CNN based models lead to significant performance improvement, with an accuracy of 83.02%±0.72%. These demonstrate the potential clinical applications of this method.
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Affiliation(s)
- Jinlian Ma
- School of Mathematical Sciences, Zhejiang University, Hangzhou 310027, China
| | - Fa Wu
- School of Mathematical Sciences, Zhejiang University, Hangzhou 310027, China
| | - Jiang Zhu
- Department of Ultrasound, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310020, China
| | - Dong Xu
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China
| | - Dexing Kong
- School of Mathematical Sciences, Zhejiang University, Hangzhou 310027, China.
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15
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Koundal D, Gupta S, Singh S. Automated delineation of thyroid nodules in ultrasound images using spatial neutrosophic clustering and level set. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2015.11.035] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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16
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Sudarshan VK, Mookiah MRK, Acharya UR, Chandran V, Molinari F, Fujita H, Ng KH. Application of wavelet techniques for cancer diagnosis using ultrasound images: A Review. Comput Biol Med 2015; 69:97-111. [PMID: 26761591 DOI: 10.1016/j.compbiomed.2015.12.006] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2015] [Revised: 11/12/2015] [Accepted: 12/11/2015] [Indexed: 02/01/2023]
Abstract
Ultrasound is an important and low cost imaging modality used to study the internal organs of human body and blood flow through blood vessels. It uses high frequency sound waves to acquire images of internal organs. It is used to screen normal, benign and malignant tissues of various organs. Healthy and malignant tissues generate different echoes for ultrasound. Hence, it provides useful information about the potential tumor tissues that can be analyzed for diagnostic purposes before therapeutic procedures. Ultrasound images are affected with speckle noise due to an air gap between the transducer probe and the body. The challenge is to design and develop robust image preprocessing, segmentation and feature extraction algorithms to locate the tumor region and to extract subtle information from isolated tumor region for diagnosis. This information can be revealed using a scale space technique such as the Discrete Wavelet Transform (DWT). It decomposes an image into images at different scales using low pass and high pass filters. These filters help to identify the detail or sudden changes in intensity in the image. These changes are reflected in the wavelet coefficients. Various texture, statistical and image based features can be extracted from these coefficients. The extracted features are subjected to statistical analysis to identify the significant features to discriminate normal and malignant ultrasound images using supervised classifiers. This paper presents a review of wavelet techniques used for preprocessing, segmentation and feature extraction of breast, thyroid, ovarian and prostate cancer using ultrasound images.
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Affiliation(s)
- Vidya K Sudarshan
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
| | | | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Malaysia; Department of Biomedical Engineering, School of Science and Technology, SIM University, 599491, Singapore
| | - Vinod Chandran
- School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane QLD 4000, Australia
| | - Filippo Molinari
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy
| | - Hamido Fujita
- Faculty of Software and Information Science, Iwate Prefectural University (IPU), Iwate 020-0693, Japan
| | - Kwan Hoong Ng
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, 50603, Malaysia
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17
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Chang CY, Huang HC, Chen SJ. AUTOMATIC THYROID NODULE SEGMENTATION AND COMPONENT ANALYSIS IN ULTRASOUND IMAGES. BIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONS 2012. [DOI: 10.4015/s1016237210001803] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Heterogeneous thyroid nodules have distinct components and vague boundaries in ultrasound (US) images. It is difficult for radiologists and physicians to manually draw the complete shape of a nodule, or distinguish what kind of components a nodule has. Hence, this article presents an automatic process for nodule segmentation and component classification. A decision-tree algorithm is used to segment the possible nodular area. A refinement process is then applied to recover the nodular shape. Finally, a hierarchical method based on support vector machines (SVMs) is used to identify the components in the nodular lesion. Experimental results of the proposed approach were compared with those of other methods.
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Affiliation(s)
- Chuan-Yu Chang
- Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Taiwan
| | - Hsin-Cheng Huang
- Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Taiwan
| | - Shao-Jer Chen
- Department of Radiology, Buddhist Dalin Tzu Chi, General Hospital, Chia-Yi, Taiwan
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18
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Su Y, Wang Y, Jiao J, Guo Y. Automatic detection and classification of breast tumors in ultrasonic images using texture and morphological features. Open Med Inform J 2011; 5:26-37. [PMID: 21892371 PMCID: PMC3158436 DOI: 10.2174/1874431101105010026] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2011] [Revised: 05/15/2011] [Accepted: 05/15/2011] [Indexed: 11/22/2022] Open
Abstract
Due to severe presence of speckle noise, poor image contrast and irregular lesion shape, it is challenging to build a fully automatic detection and classification system for breast ultrasonic images. In this paper, a novel and effective computer-aided method including generation of a region of interest (ROI), segmentation and classification of breast tumor is proposed without any manual intervention. By incorporating local features of texture and position, a ROI is firstly detected using a self-organizing map neural network. Then a modified Normalized Cut approach considering the weighted neighborhood gray values is proposed to partition the ROI into clusters and get the initial boundary. In addition, a regional-fitting active contour model is used to adjust the few inaccurate initial boundaries for the final segmentation. Finally, three textures and five morphologic features are extracted from each breast tumor; whereby a highly efficient Affinity Propagation clustering is used to fulfill the malignancy and benign classification for an existing database without any training process. The proposed system is validated by 132 cases (67 benignancies and 65 malignancies) with its performance compared to traditional methods such as level set segmentation, artificial neural network classifiers, and so forth. Experiment results show that the proposed system, which needs no training procedure or manual interference, performs best in detection and classification of ultrasonic breast tumors, while having the lowest computation complexity.
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Affiliation(s)
- Yanni Su
- Department of Electronic Engineering, Fudan University, Shanghai 200433, China
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Using Power Watersheds to Segment Benign Thyroid Nodules in Ultrasound Image Data. BILDVERARBEITUNG FÜR DIE MEDIZIN 2011 2011. [DOI: 10.1007/978-3-642-19335-4_27] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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20
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Savelonas M, Iakovidis D, Legakis I, Maroulis D. Active Contours Guided by Echogenicity and Texture for Delineation of Thyroid Nodules in Ultrasound Images. ACTA ACUST UNITED AC 2009; 13:519-27. [DOI: 10.1109/titb.2008.2007192] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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21
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Tsantis S, Dimitropoulos N, Cavouras D, Nikiforidis G. Morphological and wavelet features towards sonographic thyroid nodules evaluation. Comput Med Imaging Graph 2009; 33:91-9. [DOI: 10.1016/j.compmedimag.2008.10.010] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2008] [Revised: 10/14/2008] [Accepted: 10/22/2008] [Indexed: 10/21/2022]
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22
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