Automatic Ultrasound Image Segmentation Framework Based on Darwinian Particle Swarm Optimization

Singh, Vedpal; Elamvazuthi, Irraivan; Jeoti, Varun; George, John

doi:10.1007/978-3-319-13359-1_18

Cited by 8 publications

(3 citation statements)

References 18 publications

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“…There are other variants of the (4) listing such as the Darwinian particle swarm optimization (DPSO) [17,56], fractional order particle swarm optimization (FO-PSO) [48,57] fractional order Darwinian particle swarm optimization (FO-DPSO) [43].…”

Section: H Other Variants Of Pso Algorithmsmentioning

confidence: 99%

A Comprehensive Review of Particle Swarm Optimization

Benuwa

Ghansah

Wornyo

et al. 2016

JERA

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Particle swarm optimization (PSO) is a heuristic global optimization method. PSO was motivated by the social behavior of organisms, such as bird flocking, fish schooling and human social relations. Its properties of low constraint on the continuity of objective function and the ability to adapt various dynamic environments, makes PSO one of the most important swarm intelligence algorithms and ostensibly the most commonly used optimization technique. This survey presents a comprehensive investigation of PSO and in particular, a proposed theoretical framework to improve its implementation. We hope that this survey would be beneficial to researchers studying PSO algorithms and would also serve as the substratum for future research in the study area, particularly those pursuing their career in artificial intelligence. In the end, some important conclusions and possible research directions of PSO that need to be studied in the future are proposed.

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Section: H Other Variants Of Pso Algorithmsmentioning

confidence: 99%

A Comprehensive Review of Particle Swarm Optimization

Benuwa

Ghansah

Wornyo

et al. 2016

JERA

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“…Pedram Ghamisi used Darwinian Particle Swarm Optimization (DPSO) for image segmentation in remote sensing applications; the DPSO method has better efficiency in terms of CPU time, selection of optimal threshold value and fitness value [9]. The DPSO find its application in the segmentation of ultrasound images also; the region extraction is done initially, that may be rectangle, circle or random shape and DPSO is applied to obtain more optimized region than the traditional PSO; time and computational complexity is much reduced when compared with the PSO and Genetic algorithm [10]. Vijay et al [11] proposed enhanced DPSO for the segmentation of brain tumor and Adaptive Neurofuzzy inference system for the classification and detection of a tumor in the brain; DPSO gives better accuracy than PSO with less computation time and iteration.…”

Section: Introductionmentioning

confidence: 99%

Segmentation by Fractional Order Darwinian Particle Swarm Optimization Based Multilevel Thresholding and Improved Lossless Prediction Based Compression Algorithm for Medical Images

Ahilan¹,

et al. 2019

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The image segmentation refers to the extraction of region of interest and it plays a vital role in medical image processing. This work proposes multilevel thresholding based on optimization technique for the extraction of region of interest and compression of DICOM images by an improved prediction lossless algorithm for telemedicine applications. The role of compression algorithm is inevitable in data storage and transfer. Compared to the conventional thresholding, multilevel thresholding technique plays an efficient role in image analysis. In this paper, the Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO), and Fractional Order Darwinian Particle Swarm Optimization (FODPSO) are employed in the estimation of the threshold value. The simulation results reveal that the FODPSO-based multilevel level thresholding generate superior results. The fractional coefficient in FODPSO algorithm makes it effective optimization with fast convergence rate. The classification and blending prediction-based lossless compression algorithm generates efficient results when compared with the JPEG lossy and JPEG lossless approaches. The algorithms are tested for various threshold values and higher value of PSNR indicates the proficiency of the proposed segmentation approach. The performance of the compression algorithms was validated by metrics and was found to be appropriate for data transfer in telemedicine. The algorithms are developed in Matlab2010a and tested on DICOM CT images.

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“…The driver drowsiness is then a significant contributory factor in road mishaps. By monitoring the fatigue of the driver, the vehicle safety can be enhanced [10,11]. After the detection of drowsiness the driver associate system can stimulate the driver with some sound-related signs or in basic circumstance may even stop the vehicle.…”

Section: Introductionmentioning

confidence: 99%

A System on Intelligent Driver Drowsiness Detection Method

Kumar¹,

Singh²,

Jugran³

et al. 2018

IJET

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We actualized a fatigue driver recognition framework utilizing a mix of driver's state and driving conduct pointers. For driver's express, the framework observed the eyes' blinking rate and the flickering span. Fatigue drivers have these qualities higher than ordinary levels. We utilized a camera with machine vision procedures to find out and watch driver's blinking behavior. Harr's feature classifier was utilized to first find the eye's range, and once found, a layout coordinating was utilized to track the eye for fast preparing. For driving conduct, we gained the vehicle's state from inertial measurement unit and gas pedal sensors. The principle component analysis was utilized to choose the components that have high change. The difference esteems were utilized to separate weakness drivers, which are accepted to have higher driving exercises, from typical drivers.

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Automatic Ultrasound Image Segmentation Framework Based on Darwinian Particle Swarm Optimization

Cited by 8 publications

References 18 publications

A Comprehensive Review of Particle Swarm Optimization

A Comprehensive Review of Particle Swarm Optimization

Segmentation by Fractional Order Darwinian Particle Swarm Optimization Based Multilevel Thresholding and Improved Lossless Prediction Based Compression Algorithm for Medical Images

A System on Intelligent Driver Drowsiness Detection Method

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