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Ultrasound liver tumor segmentation using adaptively regularized kernel-based fuzzy C means with enhanced level set algorithm

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International Journal of Intelligent Computing and Cybernetics 2022年第3期15卷 438-453页

作者： Deepak S.Uplaonkar Virupakshappa Nagabhushan Patil Research Scholar in Electrical and Electronics Engineering Department Poojya Doddappa Appa College of EngineeringKalaburagiIndia Computer Science and Engineering Department Sharnbasva UniversityKalaburagiIndia Electrical and Electronics Engineering Department Poojya Doddappa Appa College of EngineeringKalaburagiIndia

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 level set ***-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 level set 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.

关键词： Adaptively regularized kernel-based fuzzy C means Contrast-limited adaptive histogram equalization level set algorithm Liver tumor segmentation Local ternary pattern

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Automatic Face Segmentation Using Adaptively Regularized Kernel-Based Fuzzy Clustering Means With level set algorithm

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INTERNATIONAL JOURNAL OF E-COLLABORATION 2022年第3期18卷 1-15页

作者： Rangayya Virupakshappa Patil, Nagabhushan Sharnbasva Univ Kalaburagi Dept Elect & Commun Engn Kalaburagi Karnataka India Sharnbasva Univ Kalaburagi Dept Comp Sci & Engn Kalaburagi Karnataka India Poojya Doddappa Appa Coll Engn Dept Elect & Elect Engn Kalaburgi Karnataka India

In this research, a new level set-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 level set, 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).

关键词： Adaptively Regularized Kernel-Based Fuzzy Clustering Means Contrast Limited Adaptive Histogram Equalization Face Segmentation level set algorithm

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An implicit level set algorithm for hydraulic fracturing with a stress-layer asymptote

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ENGINEERING FRACTURE MECHANICS 2023年 292卷

作者： Valov, A. V. Dontsov, E. V. Baykin, A. N. Golovin, S., V Lavrentyev Inst Hydrodynam SB RAS Novosibirsk 630090 Russia Novosibirsk State Univ Novosibirsk 630090 Russia ResFrac Corp Palo Alto CA USA

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.

关键词： Hydraulic fracturing Asymptotic solution level set algorithm Contact algorithm

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Investigation of the flow characteristics of spherical harmonic particles using the level set method

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POWDER TECHNOLOGY 2023年 413卷

作者： Wang, Siqiang Wei, Zhijun Ji, Shunying Dalian Univ Technol State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116024 Peoples R China

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.

关键词： Discrete element method Spherical harmonic function level set algorithm Concave particle Granular flow

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An unfitted finite element method using level set functions for extrapolation into deformable diffuse interfaces

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JOURNAL OF COMPUTATIONAL PHYSICS 2022年 461卷 1页

作者： Kuzmin, Dmitri Baecker, Jan-Phillip TU Dortmund Univ Inst Appl Math LS III Vogelpothsweg 87 D-44227 Dortmund Germany

We explore a new way to handle flux boundary conditions imposed on level sets. 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 level set 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.

关键词： Unfitted finite element method level set algorithm Diffuse interface Regularized delta function Ghost penalty Extension velocity

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Segmentation of lungs via hybridization of CA and level set algorithm

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Research Journal of Applied Sciences, Engineering and Technology 2014年第9期8卷 1117-1124页

作者： Anbu Megelin Star, A. Subburaj, P. Depertment of ECE The Tamizhan College of Engineering and Technology Chenbagaramanputhoor Tamil Nadu India National Engineering College Kovilpatti India

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 process. Hence, in this study, the lung image is segmented in order to identify the tumor sector. Initially, the input lung image is applied with the denoising process for removing noises with the aid of multi-wavelet transformation. After this process, the CA algorithm is applied for obtaining the background seeds and foreground seeds (tumor seeds) and then the level set algorithm is applied for acquiring the acute tumor tissues. As a result of the mentioned process, the tumor sector is segmented and the results are depicted. Studies on lung tumor datasets demonstrate 80-85% overlap performance of the proposed algorithm with less sensitivity to seed initialization, robustness with respect to heterogeneous tumor types and its efficiency in terms of computation time. © Maxwell Scientific Organization, 2014.

关键词： Background seeds CA (Cellular Automata) algorithm Foreground seeds level set algorithm Lungs Segmentation Tumor

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Developing an Improved Method to Remove Pectoral Muscle for Better Diagnosis of Breast Cancer in Mammography Images

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第11期14卷 659-666页

作者： Abaei, Golnoush Rezaei, Zahra Mian, Usama Qasim Abdalla, Yasir Azhari Abdalgadir Mathew, Nitin Gan, Leong Yi RMIT Univ Sch Comp Technol Melbourne Australia Islamic Azad Univ Dept Comp Engn Marvdasht Branch Marvdasht Iran Monash Univ Malaysia Sch Informat Technol Subang Jaya Malaysia

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, level set 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 level set considerably due to many optimizations and modifications. IRG can be used as part of the preprocessing unit of an automated cancer diagnosis system.

关键词： Breast cancer preprocessing pectoral muscle segmentation level set algorithm region growing algorithm

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level set algorithm for Shape Reconstruction of Non-Overlapping Three-Dimensional Penetrable Targets

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2012年第1期50卷 75-86页

作者： Hajihashemi, Mohammad Reza El-Shenawee, Magda Univ Florida Gainesville FL 32611 USA Univ Arkansas Fayetteville AR 72701 USA

This paper presents a 3-D shape reconstruction algorithm based on the level set 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.

关键词： Computational electromagnetics inverse electromagnetic scattering level set algorithm method of moments shape reconstruction

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Automated recognition of ROIs for breast thermograms of lateral view-a pilot study

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QUANTITATIVE INFRARED THERMOGRAPHY JOURNAL 2018年第2期15卷 194-213页

作者： Selle, J. Josephine Shenbagavalli, A. Sriraam, N. Venkatraman, B. Jayashree, M. Menaka, M. Natl Engn Coll Dept Elect & Commun Engn Kovilpatti India MS Ramaiah Inst Technol Ctr Med Elect & Comp Bangalore Karnataka India Indira Gandhi Ctr Atom Res Dept Qual Assurance Kalpakkam Tamil Nadu India Dept Atom Energy Hosp Kalpakkam Tamil Nadu India

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.

关键词： Breast thermal images lateral view anisotropic filter level set algorithm

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Acetowhite region segmentation in uterine cervix images using a registered ratio image

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COMPUTERS IN BIOLOGY AND MEDICINE 2018年 93卷 47-55页

作者： Liu, Jun Li, Ling Wang, Lei Nanchang Hangkong Univ Coll Informat Engn Nanchang Jiangxi Peoples R China Jiangxi Maternal & Child Hlth Hosp Dept Gynecol Oncol Nanchang Jiangxi Peoples R China

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.

关键词： Cervical cancer screening Cervix image level set algorithm AW segmentation

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