scholarly journals Segmentation of thyroid nodules using Improvised U-Net Architecture

2020 ◽  
Vol 9 (8) ◽  
pp. 56-60
Keyword(s):  
Thyroid Nodules ◽  
Performance Metrics ◽  
Automatic Segmentation ◽  
Research Work ◽  
Ultrasound Image ◽  
True Positive Rate ◽  
Medical Image Segmentation ◽  
Common Disease ◽  
Low Resolution ◽  
Feature Map

Thyroid nodules are considered as most common disease found in adults and thyroid cancer has increased over the years rapidly. Further automatic segmentation for ultrasound image is quite difficult due to the image poor quality, hence several researcher have focused and observed that U-Net achieves significant performance in medical image segmentation. However U-net faces the problem of low resolution which causes smoothness in image, hence in this research work we have proposed improvised U-Net which helps in achieving the better performance. The main aim of this research work is to achieve the probable Region of Interest through segmentation with better efficiency. In order to achieve that Improvised U-Net develops two distinctive feature map i.e. High level feature Map and low level feature map to avoid the problem of low resolution. Further proposed model is evaluated considering the standard dataset based on performance metrics such as Dice Coefficient and True positive Rate. Moreover our model achieves better performance than the existing model.

scholarly journals Performance Evaluation of Contour Based Segmentation Methods for Ultrasound Images

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
R. J. Hemalatha ◽  
V. Vijaybaskar ◽  
T. R. Thamizhvani
Keyword(s):  
Active Contour ◽  
Performance Metrics ◽  
Ultrasound Image ◽  
Medical Image Segmentation ◽  
Similarity Criteria ◽  
Support Vector ◽  
Svm Classifier ◽  
Ultrasound Images ◽  
Segmentation Method ◽  
Region Detection

Active contour methods are widely used for medical image segmentation. Using level set algorithms the applications of active contour methods have become flexible and convenient. This paper describes the evaluation of the performance of the active contour models using performance metrics and statistical analysis. We have implemented five different methods for segmenting the synovial region in arthritis affected ultrasound image. A comparative analysis between the methods of segmentation was performed and the best segmentation method was identified using similarity criteria, standard error, and F-test. For further analysis, classification of the segmentation techniques using support vector machine (SVM) classifier is performed to determine the absolute method for synovial region detection. With these results, localized region based active contour named Lankton method is defined to be the best segmentation method.

Automated location of thyroid nodules in ultrasound images with improved YOLOV3 network

2020 ◽  
pp. 1-16
Author(s):  
Ling Zhang ◽  
Yan Zhuang ◽  
Zhan Hua ◽  
Lin Han ◽  
Cheng Li ◽  
...  
Keyword(s):  
Thyroid Nodule ◽  
Thyroid Nodules ◽  
Automatic Segmentation ◽  
Ultrasound Image ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Ultrasound Images ◽  
Semantic Features ◽  
Low Contrast ◽  
Automated Method

BACKGROUND: Thyroid ultrasonography is widely used to diagnose thyroid nodules in clinics. Automatic localization of nodules can promote the development of intelligent thyroid diagnosis and reduce workload of radiologists. However, besides the ultrasound image has low contrast and high noise, the thyroid nodules are diverse in shape and vary greatly in size. Thus, thyroid nodule detection in ultrasound images is still a challenging task. OBJECTIVE: This study proposes an automatic detection algorithm to locate nodules in B ultrasound images and Doppler ultrasound images. This method can be used to screen thyroid nodules and provide a basis for subsequent automatic segmentation and intelligent diagnosis. METHODS: We develop and optimize an improved YOLOV3 model for detecting thyroid nodules in ultrasound images with B-mode and Doppler mode. Improvements include (1) using the high-resolution network (HRNet) as the basic network for gradually extracting high-level semantic features to reduce the missed detection and misdetection, (2) optimizing the loss function for single target detection like nodules, and (3) obtaining the anchor boxes by clustering the candidate frames of real nodules in the dataset. RESULTS: The experimental results of applying to 8000 clinical ultrasound images show that the new method developed and tested in this study can effectively detect thyroid nodules. The method achieves 94.53% mean precision and 95.00% mean recall. CONCLUTIONS: The study demonstrates a new automated method that enables to achieve high detection accuracy and effectively locate thyroid nodules in various ultrasound images without any user interaction, which indicates its potential clinical application value for the thyroid nodule screening.

scholarly journals A Self-Spatial Adaptive Weighting Based U-Net for Image Segmentation

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 348
Author(s):  
Choongsang Cho ◽  
Young Han Lee ◽  
Jongyoul Park ◽  
Sangkeun Lee
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Feature Maps ◽  
Data Set ◽  
Feature Map ◽  
Adaptive Weighting ◽  
Spatially Adaptive ◽  
Wide Range ◽  
Decoder Architecture

Semantic image segmentation has a wide range of applications. When it comes to medical image segmentation, its accuracy is even more important than those of other areas because the performance gives useful information directly applicable to disease diagnosis, surgical planning, and history monitoring. The state-of-the-art models in medical image segmentation are variants of encoder-decoder architecture, which is called U-Net. To effectively reflect the spatial features in feature maps in encoder-decoder architecture, we propose a spatially adaptive weighting scheme for medical image segmentation. Specifically, the spatial feature is estimated from the feature maps, and the learned weighting parameters are obtained from the computed map, since segmentation results are predicted from the feature map through a convolutional layer. Especially in the proposed networks, the convolutional block for extracting the feature map is replaced with the widely used convolutional frameworks: VGG, ResNet, and Bottleneck Resent structures. In addition, a bilinear up-sampling method replaces the up-convolutional layer to increase the resolution of the feature map. For the performance evaluation of the proposed architecture, we used three data sets covering different medical imaging modalities. Experimental results show that the network with the proposed self-spatial adaptive weighting block based on the ResNet framework gave the highest IoU and DICE scores in the three tasks compared to other methods. In particular, the segmentation network combining the proposed self-spatially adaptive block and ResNet framework recorded the highest 3.01% and 2.89% improvements in IoU and DICE scores, respectively, in the Nerve data set. Therefore, we believe that the proposed scheme can be a useful tool for image segmentation tasks based on the encoder-decoder architecture.

scholarly journals Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques

Medicina ◽  
2021 ◽  
Vol 57 (6) ◽  
pp. 527
Author(s):  
Vijay Vyas Vadhiraj ◽  
Andrew Simpkin ◽  
James O’Connell ◽  
Naykky Singh Singh Ospina ◽  
Spyridoula Maraka ◽  
...  
Keyword(s):  
Decision Making ◽  
Sensitivity And Specificity ◽  
Thyroid Nodules ◽  
Median Filter ◽  
Ultrasound Image ◽  
Multiple Instance Learning ◽  
Image Features ◽  
Classification Model ◽  
Support Vector ◽  
Accuracy Score

Background and Objectives: Thyroid nodules are lumps of solid or liquid-filled tumors that form inside the thyroid gland, which can be malignant or benign. Our aim was to test whether the described features of the Thyroid Imaging Reporting and Data System (TI-RADS) could improve radiologists’ decision making when integrated into a computer system. In this study, we developed a computer-aided diagnosis system integrated into multiple-instance learning (MIL) that would focus on benign–malignant classification. Data were available from the Universidad Nacional de Colombia. Materials and Methods: There were 99 cases (33 Benign and 66 malignant). In this study, the median filter and image binarization were used for image pre-processing and segmentation. The grey level co-occurrence matrix (GLCM) was used to extract seven ultrasound image features. These data were divided into 87% training and 13% validation sets. We compared the support vector machine (SVM) and artificial neural network (ANN) classification algorithms based on their accuracy score, sensitivity, and specificity. The outcome measure was whether the thyroid nodule was benign or malignant. We also developed a graphic user interface (GUI) to display the image features that would help radiologists with decision making. Results: ANN and SVM achieved an accuracy of 75% and 96% respectively. SVM outperformed all the other models on all performance metrics, achieving higher accuracy, sensitivity, and specificity score. Conclusions: Our study suggests promising results from MIL in thyroid cancer detection. Further testing with external data is required before our classification model can be employed in practice.

scholarly journals Choosing the best algorithm among five thyroid nodule ultrasound scores: from performance to cytology sparing—a single-center retrospective study in a large cohort

2021 ◽  
Author(s):  
Clotilde Sparano ◽  
Valentina Verdiani ◽  
Cinzia Pupilli ◽  
Giuliano Perigli ◽  
Benedetta Badii ◽  
...  
Keyword(s):  
Thyroid Nodule ◽  
Thyroid Nodules ◽  
Real Life ◽  
Scoring Systems ◽  
Diagnostic Procedures ◽  
Needle Aspiration ◽  
Common Disease ◽  
Single Center ◽  
Thyroid Microcarcinoma ◽  
Thyroid Malignancies

Abstract Objective Incidental diagnosis of thyroid nodules, and therefore of thyroid cancer, has definitely increased in recent years, but the mortality rate for thyroid malignancies remains very low. Within this landscape of overdiagnosis, several nodule ultrasound scores (NUS) have been proposed to reduce unnecessary diagnostic procedures. Our aim was to verify the suitability of five main NUS. Methods This single-center, retrospective, observational study analyzed a total number of 6474 valid cytologies. A full clinical and US description of the thyroid gland and nodules was performed. We retrospectively applied five available NUS: KTIRADS, ATA, AACE/ACE-AME, EUTIRADS, and ACRTIRADS. Thereafter, we calculated the sensitivity, specificity, PPV, and NPV, along with the number of possible fine-needle aspiration (FNA) sparing, according to each NUS algorithm and to clustering risk classes within three macro-groups (low, intermediate, and high risk). Results In a real-life setting of thyroid nodule management, available NUS scoring systems show good accuracy at ROC analysis (AUC up to 0.647) and higher NPV (up to 96%). The ability in FNA sparing ranges from 10 to 38% and reaches 44.2% of potential FNA economization in the low-risk macro-group. Considering our cohort, ACRTIRADS and AACE/ACE-AME scores provide the best compromise in terms of accuracy and spared cytology. Conclusions Despite several limitations, available NUS do appear to assist physicians in clinical practice. In the context of a common disease, such as thyroid nodules, higher accuracy and NPV are desirable NUS features. Further improvements in NUS sensitivity and specificity are attainable future goals to optimize nodule management. Key Points • Thyroid nodule ultrasound scores do assist clinicians in real practice. • Ultrasound scores reduce unnecessary diagnostic procedures, containing indolent thyroid microcarcinoma overdiagnosis. • The variable malignancy risk of the “indeterminate” category negatively influences score’s performance in real-life management of thyroid lesions.

scholarly journals Curvelet based automatic segmentation of supraspinatus tendon from ultrasound image: a focused assistive diagnostic method

2014 ◽  
Vol 13 (1) ◽  
pp. 157 ◽  
Author(s):  
Rishu Gupta ◽  
Irraivan Elamvazuthi ◽  
Sarat Dass ◽  
Ibrahima Faye ◽  
Pandian Vasant ◽  
...  
Keyword(s):  
Diagnostic Method ◽  
Automatic Segmentation ◽  
Ultrasound Image ◽  
Supraspinatus Tendon

scholarly journals Performance evaluation and comparison study of adaptive MANET service location and discovery protocols for highly dynamic environments

2022 ◽  
Vol 2022 (1) ◽  
Author(s):  
Ghassen Ben Brahim ◽  
Nazeeruddin Mohammad ◽  
Wassim El-Hajj ◽  
Gerard Parr ◽  
Bryan Scotney
Keyword(s):  
Service Discovery ◽  
Ad Hoc ◽  
Performance Metrics ◽  
Research Work ◽  
Communication Overhead ◽  
Multimedia Networks ◽  
Service Availability ◽  
Critical Requirement ◽  
Hoc Networks ◽  
Service Location

AbstractA critical requirement in Mobile Ad Hoc Networks (MANETs) is its ability to automatically discover existing services as well as their locations. Several solutions have been proposed in various communication domains which could be classified into two categories: (1) directory based, and (2) directory-less. The former is efficient but suffers from the amount of control messages being exchanged to maintain all directories in an agile environment. However, the latter approach attempts to reduce the amount of control messages to update directories, by simply sending broadcast messages to discover services; which is also a non-desirable approach in MANETs. This research work builds on top of our prior work (Nazeeruddin et al. in IFIP/IEEE international conference on management of multimedia networks and services, Springer, Berlin, 2006)) where we introduced a new efficient protocol for service discovery in MANETs (MSLD); a lightweight, robust, scalable, and flexible protocol which supports node heterogeneity and dynamically adapts to network changes while not flooding the network with extra protocol messages—a major challenge in today’s network environments, such as Internet of Things (IoT). Extensive simulations study was conducted on MSLD to: (1) initially evaluate its performance in terms of latency, service availability, and overhead messages, then (2) compare its performance to Dir-Based, Dir-less, and PDP protocols under various network conditions. For most performance metrics, simulation results show that MSLD outperforms Dir-Based, Dir-less, and PDP by either matching or achieving high service availability, low service discovery latency, and considerably less communication overhead.

scholarly journals Identification of Micro-calcification in Mammogram for Breast Cancer Analysis using SVM Classifier

2018 ◽  
Vol 7 (2.16) ◽  
pp. 29
Author(s):  
Gaurav Makwana ◽  
Lalita Gupta
Keyword(s):  
Breast Cancer ◽  
Clustering Algorithm ◽  
Automatic Segmentation ◽  
Breast Cancer Diagnosis ◽  
The State ◽  
Svm Classifier ◽  
Common Disease ◽  
Test Technique ◽  
Novel Approach ◽  
Breast Calcification

Breast cancer is most common disease in women of all ages. To identify & confirm the state of tumor in breast cancer diagnosis, patients are undergo biopsy number of times to identify malignancy. Early detection of cancer can save the patient. In this paper a novel approach for automatic segmentation & classification of breast calcification is proposed. The diagnostic test technique for detection of breast condition is very costly & requires human expertise whereas proposed method can help in automatically identifying the disease by comparing the data with the standard database. In proposed method a database has been created to define various stage of breast calcification & testing images are pre-processed to resize, enhance & filtered to remove background noise. Clustering is performed by using k-means clustering algorithm. GLCM is used to extract out statistical feature like area, mean, variance, standard deviation, homogeneity, skewness etc. to classify the state of tumor. SVM classifier is used for the classification using extracted feature. 

scholarly journals Region Filling with Super Resolution Algorithm

2021 ◽  
Vol 9 (VI) ◽  
pp. 3557-3564
Author(s):  
Darakhshan R. Khan
Keyword(s):  
Patch Size ◽  
Research Work ◽  
Super Resolution ◽  
Input Image ◽  
Computational Time ◽  
Low Resolution ◽  
Resolution Image ◽  
Resolution Algorithm ◽  
Region Filling

Region filling which has another name inpainting, is an approach to find the values of missing pixels from data available in the remaining portion of the image. The missing information must be recalculated in a distinctly convincing manner, such that, image look seamless. This research work has built a methodology for completely automating patch priority based region filling process. To reduce the computational time, low resolution image is constructed from input image. Based on texel of an image, patch size is determined. Several low resolution image with missing region filled is generated using region filling algorithm. Pixel information from these low resolution images is consolidated to produce single low resolution region filled image. Finally, super resolution algorithm is applied to enhance the quality of image and regain all specifics of image. This methodology of identifying patch size based on input fed has an advantage over filling algorithms which in true sense automate the process of region filling, to deal with sensitivity in region filling, algorithm different parameter settings are used and functioning with coarse version of image will notably reduce the computational time.

scholarly journals Financial Control Systems and Financial Systems Theory: Free Cash Flow and Profitability Nexus: a Comparative Study of Manufacturing Firms in Nigeria and Ghana

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Cash Flow ◽  
Net Present Value ◽  
Performance Metrics ◽  
Positive Impact ◽  
Research Work ◽  
Manufacturing Firms ◽  
Free Cash Flow ◽  
Estimation Model ◽  
Financial Systems ◽  
The Impact

In Financial Systems, the impact of Free Cash Flow (FCF) on the performance of a company has been in the center of academic discourse in recent years. Several studies have tried to ascertain the nature and magnitude of the relationship between free cash flow and firm profitability with conflicting results coming from different scholars. The main objective of this research work was to examine the impact of FCF on the profitability of quoted manufacturing firms in the Nigerian and Ghana stock exchanges. Data were pooled from twenty (20) different companies (ten each from Nigeria and Ghana) for a period of six years (2012 – 2017). A panel data estimation model was used to measure the impact of FCF and other performance metrics on the Return on Assets (ROA), which is our chosen profitability measure. The results show a positive but insignificant relationship between FCF and ROA both for Ghana and Nigerian manufacturing firms. Also, sales growth showed a positive impact on profitability of both countries while leverage negatively impacted on profitability. with Ghana being significant at 5%. The implication of the findings of the study is that it makes no business sense for companies to keep piling up excess funds beyond that which is needed for transactional purposes. The similarity between the results from Ghana and Nigeria in most of the variables shows that the findings of this study can be generalized to other countries. Based on the findings of the study, we recommend that the management of companies should strive to keep only the minimum needed free cash flow while the rest should be invested in other projects with positive net present value

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