scholarly journals Ultrasound image segmentation through deep learning based improvised U-Net

2021 ◽  
Vol 21 (3) ◽  
pp. 1424
Author(s):  
Nayana R. Shenoy ◽  
Anand Jatti
Keyword(s):  
Image Segmentation ◽  
Thyroid Cancer ◽  
Thyroid Gland ◽  
Thyroid Nodule ◽  
Research Work ◽  
Ultrasound Image ◽  
Region Of Interest ◽  
Great Accuracy ◽  
Proposed Model ◽  
High Level

<p><span id="docs-internal-guid-cea63826-7fff-8080-83de-ad2ba4604953"><span>Thyroid nodule are fluid or solid lump that are formed within human’s gland and most thyroid nodule doesn’t show any symptom or any sign; moreover there are certain percentage of thyroid gland are cancerous and which could lead human into critical situation up to death. Hence, it is one of the important type of cancer and also it is important for detection of cancer. Ultrasound imaging is widely popular and frequently used tool for diagnosing thyroid cancer, however considering the wide application in clinical area such estimating size, shape and position of thyroid cancer. Further, it is important to design automatic and absolute segmentation for better detection and efficient diagnosis based on US-image. Segmentation of thyroid gland from the ultrasound image is quiet challenging task due to inhomogeneous structure and similar existence of intestine. Thyroid nodule can appear anywhere and have any kind of contrast, shape and size, hence segmentation process needs to designed carefully; several researcher have worked in designing the segmentation mechanism, however most of them were either semi-automatic or lack with performance metric, however it was suggested that U-Net possesses great accuracy. Hence, in this paper, we proposed improvised U-Net which focuses on shortcoming of U-Net, the main aim of this research work is to find the probable Region of interest and segment further. Furthermore, we develop High level and low-level feature map to avoid the low-resolution problem and information; later we develop dropout layer for further optimization. Moreover proposed model is evaluated considering the important metrics such as accuracy, Dice Coefficient, AUC, F1-measure and true positive; our proposed model performs better than the existing model. </span></span></p>

scholarly journals REGION OF INTEREST DETECTION BASED ON HISTOGRAM SEGMENTATION FOR SATELLITE IMAGE

2016 ◽  
Vol XLI-B7 ◽  
pp. 249-255
Author(s):  
Warinthorn Kiadtikornthaweeyot ◽  
Adrian R. L. Tatnall
Keyword(s):  
Image Segmentation ◽  
High Resolution ◽  
Satellite Images ◽  
Satellite Image ◽  
Region Of Interest ◽  
Satellite Imaging ◽  
Proposed Model ◽  
High Resolution Satellite Images ◽  
Image Area ◽  
Interest Detection

High resolution satellite imaging is considered as the outstanding applicant to extract the Earth’s surface information. Extraction of a feature of an image is very difficult due to having to find the appropriate image segmentation techniques and combine different methods to detect the Region of Interest (ROI) most effectively. This paper proposes techniques to classify objects in the satellite image by using image processing methods on high-resolution satellite images. The systems to identify the ROI focus on forests, urban and agriculture areas. The proposed system is based on histograms of the image to classify objects using thresholding. The thresholding is performed by considering the behaviour of the histogram mapping to a particular region in the satellite image. The proposed model is based on histogram segmentation and morphology techniques. There are five main steps supporting each other; Histogram classification, Histogram segmentation, Morphological dilation, Morphological fill image area and holes and ROI management. The methods to detect the ROI of the satellite images based on histogram classification have been studied, implemented and tested. The algorithm is be able to detect the area of forests, urban and agriculture separately. The image segmentation methods can detect the ROI and reduce the size of the original image by discarding the unnecessary parts.

scholarly journals Objectness Supervised Merging Algorithm for Color Image Segmentation

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Haifeng Sima ◽  
Aizhong Mi ◽  
Zhiheng Wang ◽  
Youfeng Zou
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Region Growing ◽  
Color Image Segmentation ◽  
Semantic Features ◽  
Merging Procedure ◽  
Proposed Model ◽  
Merging Process ◽  
High Level ◽  
Color Texture

Ideal color image segmentation needs both low-level cues and high-level semantic features. This paper proposes a two-hierarchy segmentation model based on merging homogeneous superpixels. First, a region growing strategy is designed for producing homogenous and compact superpixels in different partitions. Total variation smoothing features are adopted in the growing procedure for locating real boundaries. Before merging, we define a combined color-texture histogram feature for superpixels description and, meanwhile, a novel objectness feature is proposed to supervise the region merging procedure for reliable segmentation. Both color-texture histograms and objectness are computed to measure regional similarities between region pairs, and the mixed standard deviation of the union features is exploited to make stop criteria for merging process. Experimental results on the popular benchmark dataset demonstrate the better segmentation performance of the proposed model compared to other well-known segmentation algorithms.

Combination of Grinding and Wet Welding to Repair Localized Cracking in T-Welded Connections

2014 ◽  
Vol 793 ◽  
pp. 51-58 ◽  
Author(s):  
Gerardo Terán Méndez ◽  
Rubén Cuamatzi-Meléndez ◽  
Apolinar Albiter Hernández
Keyword(s):  
Research Work ◽  
Welding Process ◽  
Region Of Interest ◽  
Offshore Structures ◽  
Hardness Tests ◽  
Weld Toe ◽  
Welded Connection ◽  
High Level ◽  
Water Depths ◽  
Repair Techniques

This paper presents experimental research work on the combination of grinding and wet welding techniques to repair T-welded connections employed in the construction of offshore structures. A longitudinal rectangular grinding profile was performed at the weld toe of T-welded connections for localized cracking material removal. Two different grinding depths of 6 mm and 10 mm were performed in the welded connections to eliminate two different level of damage depth. Subsequent wet welding was applied in the grinded region to repair the grinded material. The wet welding was performed in a hyperbaric chamber simulating three different water depths: 50 m, 70 m and 100 m (shallow water). Once the combined repair techniques were performed, further experimental work was done to characterize the mechanical behavior of the repaired structures. The mechanical characterization was done with tensile, Charpy tests and Vickers Hardness tests. The region of interest from the structures was the weld toes the grinded-wet welding repair of the T-welded connections. Subsequent scanning electron microscopy (SEM) was also performed to examine the developed microstructures in the T-welded connection. The results showed that the combination of the repair techniques can restore the mechanical properties of the damaged structures. This was demonstrated by the measurement of the ultimate tensile strength, which were similar to those measured with no repair applied techniques. But the Charpy energy values were quite lower to those previously measured. This behavior was attributed to the level of porosity formed by the high level of gases created during the welding process for the simulated water depths, which were more severe at the higher water depth resulting in pore formation

scholarly journals A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images

2020 ◽  
Vol 1 (3) ◽  
pp. 78-91
Author(s):  
Muhammad Muhammad ◽  
Diyar Zeebaree ◽  
Adnan Mohsin Abdulazeez Brifcani ◽  
Jwan Saeed ◽  
Dilovan Asaad Zebari
Keyword(s):  
Breast Cancer ◽  
Image Segmentation ◽  
Signal To Noise Ratio ◽  
Ultrasound Image ◽  
Region Of Interest ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Clustering Methods ◽  
Advantages And Disadvantages ◽  
Ultrasound Image Segmentation

The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.

scholarly journals A Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images: A Review

2020 ◽  
Vol 1 (3) ◽  
pp. 78-91
Author(s):  
Muhammad Muhammad ◽  
Diyar Zeebaree ◽  
Adnan Mohsin Abdulazeez Brifcani ◽  
Jwan Saeed ◽  
Dilovan Asaad Zebari
Keyword(s):  
Breast Cancer ◽  
Image Segmentation ◽  
Signal To Noise Ratio ◽  
Ultrasound Image ◽  
Region Of Interest ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Clustering Methods ◽  
Advantages And Disadvantages ◽  
Ultrasound Image Segmentation

The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.

scholarly journals A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images

2020 ◽  
Vol 1 (3) ◽  
pp. 78-91
Author(s):  
Muhammad Muhammad ◽  
Diyar Zeebaree ◽  
Adnan Mohsin Abdulazeez Brifcani ◽  
Jwan Saeed ◽  
Dilovan Asaad Zebari
Keyword(s):  
Breast Cancer ◽  
Image Segmentation ◽  
Signal To Noise Ratio ◽  
Ultrasound Image ◽  
Region Of Interest ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Clustering Methods ◽  
Advantages And Disadvantages ◽  
Ultrasound Image Segmentation

The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.

scholarly journals Breast Segmentation and Probable Region Identification for Breast Cancer using DL-CNN

2019 ◽  
Vol 9 (2) ◽  
pp. 16-22
Keyword(s):  
Breast Cancer ◽  
False Positive ◽  
Research Work ◽  
Region Of Interest ◽  
Layered Architecture ◽  
Proposed Model ◽  
False Positive Reduction ◽  
Breast Segmentation ◽  
Novel Method ◽  
Result Analysis

Mammography is one of the key method used for detecting the breast cancer, several researcher has proposed the detection and segmentation method, however absolute solution has not developed till now and they have certain limitation and still it is one of the major challenge for finding the region in masses. Hence in this research work we have developed and design a novel method named as DL-CNN (Dual Layered) architecture CNN. The main intention of the model is segmentation and probable region identification. DL-CNN is based on the Convolution Neural Network. It has two layer first layer is applied for identifying the probable region whereas the second layer is used for segmentation and minimizing the false positive Reduction. In order to evaluate the DL-CNN algorithm by using the In Breast Dataset. Moreover the proposed model is compared against the various model in terms of ROI(Region of Interest), Dice_Index and False positive per Image. Result analysis shows that our model outperforms the existing

scholarly journals REGION OF INTEREST DETECTION BASED ON HISTOGRAM SEGMENTATION FOR SATELLITE IMAGE

2016 ◽  
Vol XLI-B7 ◽  
pp. 249-255 ◽  
Author(s):  
Warinthorn Kiadtikornthaweeyot ◽  
Adrian R. L. Tatnall
Keyword(s):  
Image Segmentation ◽  
High Resolution ◽  
Satellite Images ◽  
Satellite Image ◽  
Region Of Interest ◽  
Satellite Imaging ◽  
Proposed Model ◽  
High Resolution Satellite Images ◽  
Image Area ◽  
Interest Detection

High resolution satellite imaging is considered as the outstanding applicant to extract the Earth’s surface information. Extraction of a feature of an image is very difficult due to having to find the appropriate image segmentation techniques and combine different methods to detect the Region of Interest (ROI) most effectively. This paper proposes techniques to classify objects in the satellite image by using image processing methods on high-resolution satellite images. The systems to identify the ROI focus on forests, urban and agriculture areas. The proposed system is based on histograms of the image to classify objects using thresholding. The thresholding is performed by considering the behaviour of the histogram mapping to a particular region in the satellite image. The proposed model is based on histogram segmentation and morphology techniques. There are five main steps supporting each other; Histogram classification, Histogram segmentation, Morphological dilation, Morphological fill image area and holes and ROI management. The methods to detect the ROI of the satellite images based on histogram classification have been studied, implemented and tested. The algorithm is be able to detect the area of forests, urban and agriculture separately. The image segmentation methods can detect the ROI and reduce the size of the original image by discarding the unnecessary parts.

Sign in / Sign up

Export Citation Format

Share Document