Purpose-The purpose of this study is to develop a hybrid algorithm for segmenting tumor from ultrasound images of the ***/methodology/approach-After collecting the ultrasound images,contrast-limited adaptive histogram...
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Purpose-The purpose of this study is to develop a hybrid algorithm for segmenting tumor from ultrasound images of the ***/methodology/approach-After collecting the ultrasound images,contrast-limited adaptive histogram equalization approach(CLAHE)is applied as preprocessing,in order to enhance the visual quality of the images that helps in better ***,adaptively regularized kernel-based fuzzy C means(ARKFCM)is used to segment tumor from the enhanced image along with local ternary pattern combined with selective levelset ***-The proposed segmentation algorithm precisely segments the tumor portions from the enhanced images with lower computation *** proposed segmentation algorithm is compared with the existing algorithms and ground truth values in terms of Jaccard coefficient,dice coefficient,precision,Matthews correlation coefficient,f-score and *** experimental analysis shows that the proposed algorithm achieved 99.18% of accuracy and 92.17% of f-score value,which is better than the existing *** implications-From the experimental analysis,the proposed ARKFCM with enhanced level set algorithm obtained better performance in ultrasound liver tumor segmentation related to graph-based ***,the proposed algorithm showed 3.11% improvement in dice coefficient compared to graph-based ***/value-The image preprocessing is carried out using CLAHE *** preprocessed image is segmented by employing selective levelset model and Local Ternary Pattern in ARKFCM *** this research,the proposed algorithm has advantages such as independence of clustering parameters,robustness in preserving the image details and optimal in finding the threshold value that effectively reduces the computational cost.
In this research, a new levelset-based segmentation algorithm was proposed for human face segmentation. At first, the human facial images were collected from face semantic segmentation (FASSEG) dataset. After collect...
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In this research, a new levelset-based segmentation algorithm was proposed for human face segmentation. At first, the human facial images were collected from face semantic segmentation (FASSEG) dataset. After collecting the images, pre-processing was accomplished by utilizing contrast limited adaptive histogram equalization (CLAHE). The undertaken methodology effectively improves the quality of facial images by removing the unwanted noise. Then, segmentation was done by using adaptively regularized kernel-based fuzzy clustering means (ARKFCM) clustering with levelset, which was a high-level machine learning algorithm for localizing the face parts in complex template. Simulation outcome shows that the proposed segmentation algorithm effectively segments the facial parts in light of precision, recall, Jaccard coefficient, dice coefficient, accuracy, and miss rate. The proposed segmentation algorithm enhanced the segmentation accuracy in face segmentation up to 4.5% compared to the existing methodology (pixel wise segmentation).
The capability to simulate a hydraulic fracturing process is an essential tool that can be used to optimize treatment design and increase the efficiency of field operations. In most practical cases, hydraulic fracture...
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The capability to simulate a hydraulic fracturing process is an essential tool that can be used to optimize treatment design and increase the efficiency of field operations. In most practical cases, hydraulic fractures propagate in a multi-layered rock formation. As a result, there is a need to incorporate the effect of such heterogeneities in fracturing models to achieve an accurate prediction. To capture the layered structure of rocks, a hydraulic fracture simulator typically requires a fine mesh, which leads to a drastic reduction in computational performance. An alternative is to use more sophisticated models that are capable of providing reasonably accurate predictions even on a relatively coarse mesh. In the case of fracture growth modeling, the pivotal component of the simulation is a fracture front tracking algorithm that accounts for the layered structure of the formation. Consequently, this paper aims to extend the established Implicit level set algorithm (ILSA) to account for the effect of multiple stress layers within the tip asymptote. The enhanced front tracking algorithm involves the stress-corrected asymptote that incorporates the influence of stress layers within the near-tip region. To further increase the validity region of the stress-corrected asymptote, the stress relaxation factor is introduced, and its accuracy is examined. The numerical algorithm is validated against the reference semi-analytical solutions as well as experimental observations. In addition, we investigate the sensitivity of the fracture geometry to mesh size to demonstrate that the front tracking algorithm based on the stress-corrected asymptote retains its accuracy on a coarse mesh.
Non-spherical granular flow is critical for hopper design and applications. Although particle morphology has an evident influence on the dynamic properties of granular materials, a large number of numerical simulation...
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Non-spherical granular flow is critical for hopper design and applications. Although particle morphology has an evident influence on the dynamic properties of granular materials, a large number of numerical simulations contribute to the dynamic behaviors of convex particles. In this work, concave particles are represented using the spherical harmonic functions, and the accurate collision forces between spherical harmonics particles are obtained by the level set algorithm. Subsequently, the influences of particle sphericity, shape parameters, and hopper angles on the mass flow rate, flow fluctuation, mass flow index, particle velocity, and inter-particle contact forces of spherical harmonics particles are investigated using the discrete element method. Results indicate that spherical harmonic particles with small sphericity and large shape parameters have dense clusters and strong interlocking, which cause larger normal contact forces between particles. As a result, spherical harmonic particles have lower flow rates and more significant flow fluctuations than spheres.
We explore a new way to handle flux boundary conditions imposed on levelsets. The proposed approach is a diffuse interface version of the shifted boundary method (SBM) for continuous Galerkin discretizations of conse...
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We explore a new way to handle flux boundary conditions imposed on levelsets. The proposed approach is a diffuse interface version of the shifted boundary method (SBM) for continuous Galerkin discretizations of conservation laws in embedded domains. We impose the interface conditions weakly and approximate surface integrals by volume integrals. The discretized weak form of the governing equation has the structure of an immersed boundary finite element method. That is, integration is performed over a fixed fictitious domain. Source terms are included to account for interface conditions and extend the boundary data into the complement of the embedded domain. The calculation of these extra terms requires (i) construction of an approximate delta function and (ii) extrapolation of embedded boundary data into quadrature points. We accomplish these tasks using a levelset function, which is given analytically or evolved numerically. A globally defined averaged gradient of this approximate signed distance function is used to construct a simple map to the closest point on the interface. The normal and tangential derivatives of the numerical solution at that point are calculated using the interface conditions and/or interpolation on uniform stencils. Similarly to SBM, extrapolation of data back to the quadrature points is performed using Taylor expansions. Computations that require extrapolation are restricted to a narrow band around the interface. Numerical results are presented for elliptic, parabolic, and hyperbolic test problems, which are specifically designed to assess the error caused by the numerical treatment of interface conditions on fixed and moving boundaries in 2D. (C)& nbsp;2022 Elsevier Inc. All rights reserved.
In this study, we present a fast and robust practical tool for segmentation of solid tumors with minimal user interaction. The lung image is segmented manually by the experts may includes risk and time consuming proce...
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Mammography is a non-invasive method to study breast tissues for abnormalities. Computer-aided diagnosis (CAD) can automate the process of diagnosing malignant and benign tumors accurately. However, accurate results c...
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Mammography is a non-invasive method to study breast tissues for abnormalities. Computer-aided diagnosis (CAD) can automate the process of diagnosing malignant and benign tumors accurately. However, accurate results can be hampered by the presence of the pectoral muscle, which has a similar opacity to the breast tissue area. Detecting and removing pectoral muscles is not trivial due to various factors, and there are artifacts present near the pectoral muscle that can hamper proper segmentation. Given the significance of the topic, it is crucial to devise an accurate method for automatically detecting the muscle area in a mammography image and eliminating it from the rest of the image. This process of removing the pectoral muscle from the breast image can aid in precise segmentation and diagnosis of the tumor area, ultimately leading to faster diagnosis and better outcomes for patients. This study examined two segmentation algorithms, levelset and Region Growing, for segmenting the pectoral muscle. An Improved Region Growing based (IRG) algorithm was also proposed and showed promising results in automatically segmenting the pectoral muscle. All algorithms were tested on the MIAS dataset, and radiologists evaluated the results, showing an accuracy rating of up to 83% for IRG. The results indicated that IRG outperformed levelset considerably due to many optimizations and modifications. IRG can be used as part of the preprocessing unit of an automated cancer diagnosis system.
This paper presents a 3-D shape reconstruction algorithm based on the levelset method. Multiple dielectric and non-overlapping objects are considered. The level set algorithm is a gradient-type optimization approach ...
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This paper presents a 3-D shape reconstruction algorithm based on the levelset method. Multiple dielectric and non-overlapping objects are considered. The level set algorithm is a gradient-type optimization approach that aims to minimize a cost function between measurements and computer-simulated data. The algorithm is capable of retrieving the shape and location of multiple targets made of two different and slightly lossy materials. An appropriate form of the deformation velocity based on the forward and adjoint fields is calculated. The method of moment surface integral equation is implemented to calculate the deformation velocity of the evolving objects. Two sets of Hamilton-Jacobi equations, associated with the two dielectric materials, are solved simultaneously to update the evolving objects. During the inversion scheme, the marching cubes method is employed to restore the surface meshes necessary for the forward solver. The algorithm is tested on corrupted synthetic data with signal-to-noise ratio of 10 dB.
This research study proposes an automated region of interest (ROI) for breast thermograms by considering lateral view as well as the frontal view of the breasts. The lateral view helps in contralateral comparison betw...
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This research study proposes an automated region of interest (ROI) for breast thermograms by considering lateral view as well as the frontal view of the breasts. The lateral view helps in contralateral comparison between the different quadrants of the breast and thus ensuring the analysis of the whole breast region including the lymph nodes and armpit areas. The proposed research involves in analysis of normal, lactating and fibro adenoma breast thermograms from a specific ethnic group located in the southern part of India. An optimized anisotropic diffusion filter is applied to preserve the vascular patterns inside the breast regions which appear to be significant edges in the thermal images. Then level set algorithm without re-initialization selects the contour boundary covering maximum area of the breast regions and segments along the canny detected edges which is enhanced through anisotropic diffusion filter. The resultant ROI helps the clinical specialist to conclude the nature of the biomarkers whether normal or abnormal at the preliminary screening level. Experimental simulation result shows maximum similarity ( > 0.05) within the 95% confidence interval of correlation between the segmented and reference images. It can be concluded that the proposed automated ROIs confirm the suitability for clinical validation.
Visual inspection with acetic acid (VIA) remains a main cervical cancer screening tool in developing countries. However, it depends on the operator's experience, and its utility is often limited by the lack of tra...
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Visual inspection with acetic acid (VIA) remains a main cervical cancer screening tool in developing countries. However, it depends on the operator's experience, and its utility is often limited by the lack of trained doctors. Smart colposcope devices to automatically detect the cervical intraepithelial neoplasia (ON, the early stage of cervical cancer) may provide a promising alternative. As the acetowhite (AW) region is the most important feature of CIN during VIA, its segmentation is considered an important procedure in the automatic detection of CIN. In this study, an automatic AW region segmentation algorithm based on the pre-acetic-acid and post-acetic-acid test images was developed. The cervix region was extracted according to a clustering algorithm from the pre-acetic acid test image. A ratio image was then obtained after registering the pre- and post-acetic-acid test images to facilitate the segmentation of the AW region using a modified level set algorithm. The results showed that although the developed algorithm yielded a mean sensitivity of 71.86%, which was lower than that of the fuzzy C-means (FCM) algorithm by 12.08% and the classical CV model-based level set algorithm (CV-LSA) by 4.04%, a high mean specificity (92.76%) was achieved that was greater than those of FCM and CV-LSA by 46.61% and 31.34%, respectively. Additionally, a high Jaccard index (JI) mean accuracy of 61.51% was achieved, which was greater than those of FCM and CV-LSA by 18.74% and 17.14%, respectively. This new algorithm, with an improved segmentation performance over traditional algorithms, may serve as a promising tool to advance the clinical prognosis of cervical cancer.
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