scholarly journals Automatic Localization Algorithm for Ultrasound Breast Tumors Based on Human Visual Mechanism

2017 ◽  
Author(s):  
Yuting Xie ◽  
Ke Chen ◽  
Jiangli Lin
Keyword(s):  
Level Set ◽  
Automatic Segmentation ◽  
Breast Tumors ◽  
Input Image ◽  
Object Localization ◽  
Initial Contour ◽  
Natural Image ◽  
Localization Algorithm ◽  
Localization Method ◽  
Automatic Localization

Human visual system (HVM) can quickly localize the most salient object in scenes, which has been widely applied on natural image segmentation [15]-[19]. In ultrasound (US) breast images, compared with background areas, tumor is more salient because of its higher contrast. In this paper, we develop a novel automatic localization method based on HVM for automatic segmentation of ultrasound (US) breast tumors. First, the input image is smoothed by convolution with a linearly separable Gaussian filter and then subsampled into a 9-layer Gaussian pyramid. Then intensity, blackness ratio, and superpixel contrast features are combined to compute saliency map, in which Winner Take All algorithm is used to localize the most salient region, presenting with a circle on the localized target. Finally the circle is taken as the initial contour of CV level set to finish the extraction of breast tumor. The localization method has been tested on 400 US beast images, among which 378 images have higher saliency than background areas and succeed in localization, with high accuracy 92.00%. The HVM localization method can be used to localize the tumors, combined with this method, CV level set can achieve the fully automatic segmentation of US breast tumors. By combing intensity, blackness ratio and superpixel contrast features, the proposed localization method can successfully avoid the interference caused by background areas with low echo and high intensity. Moreover, multi-object localization of US breast images can be considered in future employment.

Automatic Segmentation of the Skull in MRI Sequences Using Level Set Method

2011 ◽  
Vol 58-60 ◽  
pp. 2370-2375
Author(s):  
Wei Li Ding ◽  
Feng Jiang ◽  
Jia Qing Yan
Keyword(s):  
Level Set ◽  
Medical Image ◽  
Automatic Segmentation ◽  
Image Sequences ◽  
Initial Contour ◽  
Single Image ◽  
Mri Sequences ◽  
Magnetic Resonance Imaging Mri ◽  
Mri Image

Magnetic Resonance Imaging (MRI) has been widely used in clinical diagnose. Segmentation of these images obtained by MRI is a necessary procedure in medical image processing. In this paper, an improved level set algorithm was proposed to optimize the segmentation of MRI image sequences based on article [1]. Firstly, we add an area term and the edge indicator function to the total energy function for single image segmentation. Secondly, we presented a new method which uses the circumscribed polygon of the previous segmentation result as the initial contour of the next image to achieve automatic segmentation of image sequences. The algorithm was tested on MRI image sequences provided by Chuiyanliu Hospital, Chaoyang District of Beijing; the results have indicated that the proposed algorithm can effectively enhance the segmentation speed and quality of MRI sequences.

scholarly journals Fractional Differentiation-Based Active Contour Model Driven by Local Intensity Fitting Energy

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Ming Gu ◽  
Renfang Wang
Keyword(s):  
Fractional Order ◽  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Input Image ◽  
Intensity Inhomogeneity ◽  
Initial Contour ◽  
Level Set Function ◽  
Contour Model ◽  
Proposed Model

A novel active contour model is proposed for segmentation images with inhomogeneity. Firstly, fractional order filter is defined by eight convolution masks corresponding to the image orientation in the eight compass directions. Then, the fractional order differentiation image is obtained and applied to the level set method. Secondly, we defined a new energy functional based on local image information and fractional order differentiation image; the proposed model not only can describe the input image more accurately but also can deal with intensity inhomogeneity. Local fitting term can enhance the ability of the model to deal with intensity inhomogeneity. The defined penalty term is used to reduce the occurrence of false boundaries. Finally, in order to eliminate the time-consuming step of reinitialization and ensure stable evolution of level set function, the Gaussian filtering method is used. Experiments on synthetic and real images show that the proposed model is efficient for images with intensity inhomogeneity and flexible to initial contour.

Superquadrics Model-based 3D Object Localization Algorithm

ROBOT ◽  
2013 ◽  
Vol 35 (4) ◽  
pp. 439 ◽  
Author(s):  
Lin WANG ◽  
Jianfu CAO ◽  
Chongzhao HAN
Keyword(s):  
Object Localization ◽  
Localization Algorithm ◽  
3D Object ◽  
Model Based

scholarly journals Detection of liver cirrhosis in standard T2-weighted MRI using deep transfer learning

2021 ◽  
Author(s):  
Sebastian Nowak ◽  
Narine Mesropyan ◽  
Anton Faron ◽  
Wolfgang Block ◽  
Martin Reuter ◽  
...  
Keyword(s):  
Neural Networks ◽  
Liver Cirrhosis ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Classification Accuracy ◽  
Automatic Segmentation ◽  
Analysis Data ◽  
Caudate Lobe ◽  
Natural Image ◽  
Liver Segmentation

Abstract Objectives To investigate the diagnostic performance of deep transfer learning (DTL) to detect liver cirrhosis from clinical MRI. Methods The dataset for this retrospective analysis consisted of 713 (343 female) patients who underwent liver MRI between 2017 and 2019. In total, 553 of these subjects had a confirmed diagnosis of liver cirrhosis, while the remainder had no history of liver disease. T2-weighted MRI slices at the level of the caudate lobe were manually exported for DTL analysis. Data were randomly split into training, validation, and test sets (70%/15%/15%). A ResNet50 convolutional neural network (CNN) pre-trained on the ImageNet archive was used for cirrhosis detection with and without upstream liver segmentation. Classification performance for detection of liver cirrhosis was compared to two radiologists with different levels of experience (4th-year resident, board-certified radiologist). Segmentation was performed using a U-Net architecture built on a pre-trained ResNet34 encoder. Differences in classification accuracy were assessed by the χ2-test. Results Dice coefficients for automatic segmentation were above 0.98 for both validation and test data. The classification accuracy of liver cirrhosis on validation (vACC) and test (tACC) data for the DTL pipeline with upstream liver segmentation (vACC = 0.99, tACC = 0.96) was significantly higher compared to the resident (vACC = 0.88, p < 0.01; tACC = 0.91, p = 0.01) and to the board-certified radiologist (vACC = 0.96, p < 0.01; tACC = 0.90, p < 0.01). Conclusion This proof-of-principle study demonstrates the potential of DTL for detecting cirrhosis based on standard T2-weighted MRI. The presented method for image-based diagnosis of liver cirrhosis demonstrated expert-level classification accuracy. Key Points • A pipeline consisting of two convolutional neural networks (CNNs) pre-trained on an extensive natural image database (ImageNet archive) enables detection of liver cirrhosis on standard T2-weighted MRI. • High classification accuracy can be achieved even without altering the pre-trained parameters of the convolutional neural networks. • Other abdominal structures apart from the liver were relevant for detection when the network was trained on unsegmented images.

A versatile algorithm for the automatic segmentation of hippocampus based on level set

2011 ◽  
Vol 7 (3) ◽  
pp. 213 ◽  
Author(s):  
J. Rajeesh ◽  
R.S. Moni ◽  
S. Palanikumar ◽  
T. Gopalakrishnan
Keyword(s):  
Level Set ◽  
Automatic Segmentation

Video Nodes Based Collaborative Object Localization Framework in Wireless Multimedia Sensor Networks

2011 ◽  
Vol 317-319 ◽  
pp. 1078-1083 ◽  
Author(s):  
Qing Tao Lin ◽  
Xiang Bing Zeng ◽  
Xiao Feng Jiang ◽  
Xin Yu Jin
Keyword(s):  
Cluster Head ◽  
Computation Time ◽  
Least Square Method ◽  
Least Square ◽  
Object Localization ◽  
Wireless Multimedia ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Multimedia Sensor Networks ◽  
Localization Model

This paper establishes a 3-D localization model and based on this model, it proposes a collaborative localization framework. In this framework, node that observes the object sends its attitude information and the relative position of the object's projection in its camera to the cluster head. The cluster head adopts an algorithm proposed in this paper to select some nodes to participate localization. The localization algorithm is based on least square method. Because the localization framework is based on a 3-D model, the size of the object or other prerequisites is not necessary. At the end of this paper, a simulation is taken on the numbers of nodes selected to locate and the localization accuracy. The result implies that selecting 3~4 nodes is proper. The theoretical analysis and the simulation result also imply that a const computation time cost is paid in this framework with a high localization accuracy (in our simulation environment, a 0.01 meter error).

Medical Imaging Importance in the Real World

2020 ◽  
pp. 136-162
Author(s):  
Ramgopal Kashyap
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Level Set ◽  
Real World ◽  
Medical Image ◽  
Automatic Segmentation ◽  
Medical Image Segmentation ◽  
Image Resolution ◽  
Variational Model ◽  
Medical Applications

In the medical image resolution, automatic segmentation is a challenging task, and it's still an unsolved problem for most medical applications due to the wide variety connected with image modalities, encoding parameters, and organic variability. In this chapter, a review and critique of medical image segmentation using clustering, compression, histogram, edge detection, parametric, variational model. and level set-based methods is presented. Modes of segmentation like manual, semi-automatic, interactive, and automatic are also discussed. To present current challenges, aim and motivation for doing fast, interactive and correct segmentation, the medical image modalities X-ray, CT, MRI, and PET are discussed in this chapter.

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