scholarly journals Dependence of Shape-Based Descriptors and Mass Segmentation Areas on Initial Contour Placement Using the Chan-Vese Method on Digital Mammograms

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
S. N. Acho ◽  
W. I. D. Rae
Keyword(s):  
Level Set ◽  
Active Contours ◽  
Initial Level ◽  
Pearson Correlation ◽  
Region Of Interest ◽  
Initial Position ◽  
Initial Contour ◽  
Boundary Information ◽  
Mass Segmentation ◽  
Mass Lesions

Variation in signal intensity within mass lesions and missing boundary information are intensity inhomogeneities inherent in digital mammograms. These inhomogeneities render the performance of a deformable contour susceptible to the location of its initial position and may lead to poor segmentation results for these images. We investigate the dependence of shape-based descriptors and mass segmentation areas on initial contour placement with the Chan-Vese segmentation method and compare these results to the active contours with selective local or global segmentation model. For each mass lesion, final contours were obtained by propagation of a proposed initial level set contour and by propagation of a manually drawn contour enclosing the region of interest. Differences in shape-based descriptors were quantified using absolute percentage differences, Euclidean distances, and Bland-Altman analysis. Segmented areas were evaluated with the area overlap measure. Differences were dependent upon the characteristics of the mass margins. Boundary moments presented large percentage differences. Pearson correlation analysis showed statistically significant correlations between shape-based descriptors from both initial locations. In conclusion, boundary moments of digital mass lesions are sensitive to the placement of initial level set contours while shape-based descriptors such as Fourier descriptors, shape convexity, and shape rectangularity exhibit a certain degree of robustness to changes in the location of the initial level set contours for both segmentation algorithms.

scholarly journals Calcium Identification and Scoring Based on Echocardiography. An Exploratory Study on Aortic Valve Stenosis

2021 ◽  
Vol 11 (7) ◽  
pp. 598
Author(s):  
Luis B. Elvas ◽  
Ana G. Almeida ◽  
Luís Rosario ◽  
Miguel Sales Dias ◽  
João C. Ferreira
Keyword(s):  
Aortic Valve ◽  
Ct Scan ◽  
Pearson Correlation ◽  
Region Of Interest ◽  
Expert Assessment ◽  
Image Binarization ◽  
Real Patient ◽  
Margin Of Error ◽  
Pixel Value ◽  
Echocardiographic Images

Currently, an echocardiography expert is needed to identify calcium in the aortic valve, and a cardiac CT-Scan image is needed for calcium quantification. When performing a CT-scan, the patient is subject to radiation, and therefore the number of CT-scans that can be performed should be limited, restricting the patient’s monitoring. Computer Vision (CV) has opened new opportunities for improved efficiency when extracting knowledge from an image. Applying CV techniques on echocardiography imaging may reduce the medical workload for identifying the calcium and quantifying it, helping doctors to maintain a better tracking of their patients. In our approach, a simple technique to identify and extract the calcium pixel count from echocardiography imaging, was developed by using CV. Based on anonymized real patient echocardiographic images, this approach enables semi-automatic calcium identification. As the brightness of echocardiography images (with the highest intensity corresponding to calcium) vary depending on the acquisition settings, echocardiographic adaptive image binarization has been performed. Given that blood maintains the same intensity on echocardiographic images—being always the darker region—blood areas in the image were used to create an adaptive threshold for binarization. After binarization, the region of interest (ROI) with calcium, was interactively selected by an echocardiography expert and extracted, allowing us to compute a calcium pixel count, corresponding to the spatial amount of calcium. The results obtained from these experiments are encouraging. With this technique, from echocardiographic images collected for the same patient with different acquisition settings and different brightness, obtaining a calcium pixel count, where pixel values show an absolute pixel value margin of error of 3 (on a scale from 0 to 255), achieving a Pearson Correlation of 0.92 indicating a strong correlation with the human expert assessment of calcium area for the same images.

scholarly journals On the Relationship between Variational Level Set-Based and SOM-Based Active Contours

2015 ◽  
Vol 2015 ◽  
pp. 1-19 ◽  
Author(s):  
Mohammed M. Abdelsamea ◽  
Giorgio Gnecco ◽  
Mohamed Medhat Gaber ◽  
Eyad Elyan
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contours ◽  
Level Set Methods ◽  
Energy Functional ◽  
Self Organizing Maps ◽  
Complex Shapes ◽  
Variational Level Set ◽  
Preservation Property ◽  
The Relationship

Most Active Contour Models (ACMs) deal with the image segmentation problem as a functional optimization problem, as they work on dividing an image into several regions by optimizing a suitable functional. Among ACMs, variational level set methods have been used to build an active contour with the aim of modeling arbitrarily complex shapes. Moreover, they can handle also topological changes of the contours. Self-Organizing Maps (SOMs) have attracted the attention of many computer vision scientists, particularly in modeling an active contour based on the idea of utilizing the prototypes (weights) of a SOM to control the evolution of the contour. SOM-based models have been proposed in general with the aim of exploiting the specific ability of SOMs to learn the edge-map information via their topology preservation property and overcoming some drawbacks of other ACMs, such as trapping into local minima of the image energy functional to be minimized in such models. In this survey, we illustrate the main concepts of variational level set-based ACMs, SOM-based ACMs, and their relationship and review in a comprehensive fashion the development of their state-of-the-art models from a machine learning perspective, with a focus on their strengths and weaknesses.

A New Image Segmentation Method Based on Improved Fast Implicit Level Set Scheme in X/γ-Ray Inspection System

2011 ◽  
Vol 103 ◽  
pp. 705-710 ◽  
Author(s):  
Yu Jie Li ◽  
Hui Min Lu ◽  
Li Feng Zhang ◽  
Shi Yuan Yang ◽  
Serikawa Seiichi
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Active Contours ◽  
Operator Splitting ◽  
Inspection System ◽  
Segmentation Method ◽  
Piecewise Constant ◽  
Γ Ray ◽  
Luggage Inspection ◽  
Segmentation Of Images

Digital X/γ-ray imaging technology has been widely used to help people deliver effective and reliable security in airports, train stations, and public buildings. Nowadays, luggage inspection system with digital radiographic/computed tomography (DR/CT) represents a most advanced nondestructive inspection technology in aviation system, which is capable of automatically discerning interesting regions in the luggage objects with CT subsystem. In this paper, we propose a new model for active contours to detect luggage objects in the system, in order to facilitate people to identify the things in luggage. The proposed method is based on techniques of piecewise constant and piecewise smooths Chan-Vese Model, semi-implicit additive operator splitting (AOS) scheme for image segmentation. Different from traditional models, the fast implicit level set scheme (FILS) is ordinary differential equation (ODE). Characterized by no need of any pre-information of topology of images and efficient segmentation of images with complex topology, the FILS scheme is fast more than traditional level set scheme 30 times. At the same time, it performs well in image segmentation of DR images in our experiments.

scholarly journals Real-Time Runway Detection for Infrared Aerial Image Using Synthetic Vision and an ROI Based Level Set Method

2018 ◽  
Vol 10 (10) ◽  
pp. 1544 ◽  
Author(s):  
Changjiang Liu ◽  
Irene Cheng ◽  
Anup Basu
Keyword(s):  
Real Time ◽  
Level Set Method ◽  
Level Set ◽  
Infrared Image ◽  
Region Of Interest ◽  
Aerial Image ◽  
Real Time Processing ◽  
Synthetic Vision ◽  
Level Set Function ◽  
Rough Region

We present a new method for real-time runway detection embedded in synthetic vision and an ROI (Region of Interest) based level set method. A virtual runway from synthetic vision provides a rough region of an infrared runway. A three-thresholding segmentation is proposed following Otsu’s binarization method to extract a runway subset from this region, which is used to construct an initial level set function. The virtual runway also gives a reference area of the actual runway in an infrared image, which helps us design a stopping criterion for the level set method. In order to meet the needs of real-time processing, the ROI based level set evolution framework is implemented in this paper. Experimental results show that the proposed algorithm is efficient and accurate.

The Reliability of Measuring Early Brain Edema with Computed Tomography

2021 ◽  
Vol 17 ◽  
Author(s):  
Yi-Fang Fan ◽  
Mi Shen ◽  
Xin-Xin Wang ◽  
Xiao-Yuan Liu ◽  
Yu-Ming Peng ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Brain Edema ◽  
Early Stage ◽  
Pearson Correlation ◽  
Intraclass Correlation ◽  
Region Of Interest ◽  
High Grade Glioma ◽  
High Grade ◽  
Magnetic Resonance Imaging Mri ◽  
Ct And Mri

Background: Postoperative brain edema is a common complication in patients with high-grade glioma after craniotomy. Both computed tomography (CT) and Magnetic Resonance Imaging (MRI) are applied to diagnose brain edema. Usually, MRI is considered to be better than CT for identifying brain edema. However, MRI is not generally applied in diagnosing acute cerebral edema in the early postoperative stage. Whether CT is reliable in detecting postoperative brain edema in the early stage is unknown. Objective: To investigate the agreement and correlation between CT and MRI for measuring early postoperative brain edema. Methods: Patients with high-grade glioma who underwent craniotomy in Beijing Tiantan hospital from January 2017 to October 2018 were retrospectively analyzed. The region of interest and operative cavity were manually outlined, and the volume of postoperative brain edema was measured on CT and MRI. Pearson correlation testing and the intraclass correlation coefficient (ICC) were used to evaluate the association and agreement between CT and MRI for detecting the volume of postoperative brain edema. Results: Twenty patients were included in this study. The interrater agreement was perfect for detecting brain edema (CT: κ=1, ICC=0.977, P<0.001; MRI: κ=0.866, ICC=0.963, P<0.001). A significant positive correlation and excellent consistency between CT and MRI were found for measuring the volume of brain edema (rater 1: r=0.97, ICC=0.934, P<0.001; rater 2: r=0.97, ICC=0.957, P<0.001). Conclusion: Substantial comparability between CT and MRI is demonstrated for detecting postoperative brain edema. It is reliable to use CT for measuring brain edema volume in the early stage after surgery.

Level Set Breast Mass Segmentation in Contrast-Enhanced and Non-Contrast-Enhanced Breast CT

2012 ◽  
pp. 697-704 ◽  
Author(s):  
Hsien-Chi Kuo ◽  
Maryellen L. Giger ◽  
Ingrid Reiser ◽  
John M. Boone ◽  
Karen K. Lindfors ◽  
...  
Keyword(s):  
Level Set ◽  
Breast Mass ◽  
Breast Ct ◽  
Contrast Enhanced ◽  
Mass Segmentation

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.

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