scholarly journals Swarm Intelligence Integrated Graph-Cut for Liver Segmentation from 3D-CT Volumes

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Maya Eapen ◽  
Reeba Korah ◽  
G. Geetha
Keyword(s):  
Swarm Intelligence ◽  
Liver Volume ◽  
Graph Cut ◽  
Experimental Result ◽  
Liver Segmentation ◽  
Large Variability ◽  
Contrast Enhanced ◽  
Adaptive Weight ◽  
Efficiency And Effectiveness ◽  
Information Intensity

The segmentation of organs in CT volumes is a prerequisite for diagnosis and treatment planning. In this paper, we focus on liver segmentation from contrast-enhanced abdominal CT volumes, a challenging task due to intensity overlapping, blurred edges, large variability in liver shape, and complex background with cluttered features. The algorithm integrates multidiscriminative cues (i.e., prior domain information, intensity model, and regional characteristics of liver in a graph-cut image segmentation framework). The paper proposes a swarm intelligence inspired edge-adaptive weight function for regulating the energy minimization of the traditional graph-cut model. The model is validated both qualitatively (by clinicians and radiologists) and quantitatively on publically available computed tomography (CT) datasets (MICCAI 2007 liver segmentation challenge, 3D-IRCAD). Quantitative evaluation of segmentation results is performed using liver volume calculations and a mean score of 80.8% and 82.5% on MICCAI and IRCAD dataset, respectively, is obtained. The experimental result illustrates the efficiency and effectiveness of the proposed method.

scholarly journals Semi-automatic liver segmentation based on probabilistic models and anatomical constraints

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Doan Cong Le ◽  
Krisana Chinnasarn ◽  
Jirapa Chansangrat ◽  
Nattawut Keeratibharat ◽  
Paramate Horkaew
Keyword(s):  
Probabilistic Models ◽  
Contextual Information ◽  
Automatic Segmentation ◽  
Anatomical Variations ◽  
Graph Cut ◽  
Computing Methods ◽  
Liver Segmentation ◽  
Seed Point ◽  
Tissue Separation ◽  
Human Interactions

AbstractSegmenting a liver and its peripherals from abdominal computed tomography is a crucial step toward computer aided diagnosis and therapeutic intervention. Despite the recent advances in computing methods, faithfully segmenting the liver has remained a challenging task, due to indefinite boundary, intensity inhomogeneity, and anatomical variations across subjects. In this paper, a semi-automatic segmentation method based on multivariable normal distribution of liver tissues and graph-cut sub-division is presented. Although it is not fully automated, the method minimally involves human interactions. Specifically, it consists of three main stages. Firstly, a subject specific probabilistic model was built from an interior patch, surrounding a seed point specified by the user. Secondly, an iterative assignment of pixel labels was applied to gradually update the probabilistic map of the tissues based on spatio-contextual information. Finally, the graph-cut model was optimized to extract the 3D liver from the image. During post-processing, overly segmented nodal regions due to fuzzy tissue separation were removed, maintaining its correct anatomy by using robust bottleneck detection with adjacent contour constraint. The proposed system was implemented and validated on the MICCAI SLIVER07 dataset. The experimental results were benchmarked against the state-of-the-art methods, based on major clinically relevant metrics. Both visual and numerical assessments reported herein indicated that the proposed system could improve the accuracy and reliability of asymptomatic liver segmentation.

Studies of Computational Intelligence Based on the Behaviour of Cockroaches

2015 ◽  
pp. 99-146
Author(s):  
Amartya Neogi
Keyword(s):  
Literature Review ◽  
Swarm Intelligence ◽  
Computational Intelligence ◽  
Research Area ◽  
Experimental Result ◽  
Swarm Optimization ◽  
Collective Decisions ◽  
Shortest Distance ◽  
In The Beginning ◽  
Statistical Results

In this chapter, the author expands the notion of computational intelligence using the behavior of cockroaches. An introduction to cockroach as swarm intelligence emerging research area and literature review of its growing concept is explained in the beginning. The chapter also covers the ideas of hybrid cockroach optimization system. Next, the author studies the applicability of cockroach swarm optimization. Thereafter, the author presents the details of theoretical algorithm and an experimental result of integration of robot to some cockroaches to make collective decisions. Then, the author proposes his algorithm for traversing the shortest distance of city warehouses. Then, a few comparative statistical results of the progress of the present work on cockroach intelligence are shown. Finally, conclusive remarks are given. At last, the author hopes that even researchers with little experience in swarm intelligence will be enabled to apply the proposed algorithm in their own application areas.

A New Bio-Inspired Social Spider Algorithm

2021 ◽  
Vol 12 (1) ◽  
pp. 79-93
Author(s):  
Dharmpal Singh
Keyword(s):  
Swarm Intelligence ◽  
Real World ◽  
Optimization Problems ◽  
Fitness Function ◽  
Foraging Strategy ◽  
Superior Performance ◽  
Experimental Result ◽  
Social Spider ◽  
Spider Web ◽  
Social Spider Algorithm

The concept of bio-inspired algorithms is used in real-world problems to search the efficient problem-solving methods. Evolutionary computation and swarm intelligence are outstanding examples of nature-inspired solution techniques of metahuristics. In this paper, an effort has been made to propose a modified social spider algorithm to solve global optimization problems in the real world. Social spiders used the foraging strategy, vibrations on the spider web to determine the positions of prey. The selection of vibration, estimated new position and calculation of the fitness function, has been furnished in details way as compared to different previously proposed swarm intelligence algorithms. Moreover, experimental result has been carried out by modified social spider on series of widely-used benchmark problem with four benchmark algorithms. Furthermore, a modified form of the proposed algorithm has superior performance as compared to other state-of-the-art metaheuristics algorithms.

Studies of Computational Intelligence Based on the Behaviour of Cockroaches

2016 ◽  
pp. 1039-1086
Author(s):  
Amartya Neogi
Keyword(s):  
Literature Review ◽  
Swarm Intelligence ◽  
Computational Intelligence ◽  
Research Area ◽  
Experimental Result ◽  
Swarm Optimization ◽  
Collective Decisions ◽  
Shortest Distance ◽  
In The Beginning ◽  
Statistical Results

In this chapter, the author expands the notion of computational intelligence using the behavior of cockroaches. An introduction to cockroach as swarm intelligence emerging research area and literature review of its growing concept is explained in the beginning. The chapter also covers the ideas of hybrid cockroach optimization system. Next, the author studies the applicability of cockroach swarm optimization. Thereafter, the author presents the details of theoretical algorithm and an experimental result of integration of robot to some cockroaches to make collective decisions. Then, the author proposes his algorithm for traversing the shortest distance of city warehouses. Then, a few comparative statistical results of the progress of the present work on cockroach intelligence are shown. Finally, conclusive remarks are given. At last, the author hopes that even researchers with little experience in swarm intelligence will be enabled to apply the proposed algorithm in their own application areas.

scholarly journals Automatic Liver Segmentation on Volumetric CT Images Using Supervoxel-Based Graph Cuts

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Weiwei Wu ◽  
Zhuhuang Zhou ◽  
Shuicai Wu ◽  
Yanhua Zhang
Keyword(s):  
Liver Volume ◽  
Maximum Intensity Projection ◽  
Liver Slice ◽  
Graph Cuts ◽  
Ct Images ◽  
Patient Specific ◽  
Computer Assisted ◽  
Morphological Operations ◽  
Liver Segmentation ◽  
Abdominal Ct

Accurate segmentation of liver from abdominal CT scans is critical for computer-assisted diagnosis and therapy. Despite many years of research, automatic liver segmentation remains a challenging task. In this paper, a novel method was proposed for automatic delineation of liver on CT volume images using supervoxel-based graph cuts. To extract the liver volume of interest (VOI), the region of abdomen was firstly determined based on maximum intensity projection (MIP) and thresholding methods. Then, the patient-specific liver VOI was extracted from the region of abdomen by using a histogram-based adaptive thresholding method and morphological operations. The supervoxels of the liver VOI were generated using the simple linear iterative clustering (SLIC) method. The foreground/background seeds for graph cuts were generated on the largest liver slice, and the graph cuts algorithm was applied to the VOI supervoxels. Thirty abdominal CT images were used to evaluate the accuracy and efficiency of the proposed algorithm. Experimental results show that the proposed method can detect the liver accurately with significant reduction of processing time, especially when dealing with diseased liver cases.

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