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Automatic liver tumor segmentation in CT Modalities Using MAT-ACM

Computer Systems Science & Engineering 2022年第12期43卷 1057-1068页

作者： S.Priyadarsini Carlos Andrés Tavera Romero Abolfazl Mehbodniya P.Vidya Sagar Sudhakar Sengan Department of Computer Science and Engineering P.S.R.Engineering CollegeSivakasiTamil Nadu626123India COMBA R&D Laboratory Faculty of EngineeringUniversidad Santiago de CaliCali76001Colombia Department of Electronics and Communications Engineering Kuwait College of Science and TechnologyKuwait Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramAndhra Pradesh522502India Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveliTamil Nadu627152India

In the recent days, the segmentation of liver tumor (LT) has beendemanding and challenging. The process of segmenting the liver and accuratelyspotting the tumor is demanding due to the diversity of shape, texture, and intensity of the liver image. The intensity similarities of the neighboring organs of theliver create difficulties during liver segmentation. The manual segmentation doesnot provide an accurate segmentation because the results provided by differentmedical experts can vary. Also, this manual technique requires a large numberof image slices and time for segmentation. To solve these issues, the Fully Automatic segmentation (FAS) technique is proposed. In this proposed Multi-AngleTexture Active Contour Model (MAT-ACM) method, the input Computed Tomography (CT) image is preprocessed by Contrast Enhancement (CE) with Non-Linear Mapping Technique (NLMT), in which the liver is differentiated from itsneighbouring soft tissues with related strength. Then, the filtered images are givenas the input to Adaptive Edge Modeling (AEM) with Canny Edge Detection(CED) technique, which segments the liver Region (LR) from the given CTimages. An AEM with a CED model is implemented, which increases the convergence speed of the iterative process for decreasing the Volumetric Overlap Error(VOE) is 6.92% rates when compared with the traditional segmentation Techniques (ST). Finally, the liver tumor segmentation (LTS) is developed by applyingthe MAT-ACM, which accurately segments the LR from the segmented LRs. Theevaluation of the proposed method is compared with the existing LTS methodsusing various performance measures to prove the superiority of the proposedMAT-ACM method.

关键词： Computed tomography contrast enhancement adaptive edge modeling multi-angle texture active contour liver tumor segmentation

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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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Advancements in liver tumor Detection:A Comprehensive Review of Various Deep Learning Models

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 91-122页

作者： Shanmugasundaram Hariharan D.Anandan Murugaperumal Krishnamoorthy Vinay Kukreja Nitin Goyal Shih-Yu Chen Deptartment of Artificial Intelligence and Data Science Vardhaman College of EngineeringHyderabad501218India Deptartment of CSE V.S.B College of EngineeringKarur639111India Deptartment of Electrical and Electronics Engineering Vardhaman College of EngineeringHyderabad501218India Centre for Research Impact&Outcome Chitkara UniversityRajpura140401PunjabIndia Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendergarh123031HaryanaIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment *** tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and *** review examines recent advancements in liver segmentation(LS)and tumor segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and *** metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical *** review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging *** suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular *** paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

关键词： liver tumor detection liver tumor segmentation image processing liver tumor diagnosis feature extraction tumor classification deep learning machine learning

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