An implementation of novel genetic based clustering algorithm for color image segmentation

Jaiswal, Varshali; Sharma, Varsha; Varma, Shambhu D.

doi:10.12928/telkomnika.v17i3.10072

Cited by 19 publications

(15 citation statements)

References 18 publications

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“…When the light is too close, it directs the spiral path towards the light which is shown in Figure 2 The proposed algorithm compared with GA and PSO algorithm. The GA [17,22] in which first read RGB color image, then for every cluster calculate L*a*b* factor for each color.…”

Section: The Proposed Algorithm For Region-based Rgb Color Image Segmmentioning

confidence: 99%

MMFO: modified moth flame optimization algorithm for region based RGB color image segmentation

Jaiswal¹,

Sharma²,

Varma³

2020

IJECE

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<span lang="EN-US">Region-based color image segmentation is elementary steps in image processing and computer vision. Color image segmentation is a region growing approach in which RGB color image is divided into the different cluster based on their pixel properties. The region-based color image segmentation has faced the problem of multidimensionality. The color image is considered in five-dimensional problems, in which three dimensions in color (RGB) and two dimensions in geometry (luminosity layer and chromaticity layer). In this paper, L*a*b color space conversion has been used to reduce the one dimension and geometrically it converts in the array hence the further one dimension has been reduced. This paper introduced an improved algorithm MMFO (Modified Moth Flame Optimization) Algorithm for RGB color image Segmentation which is based on bio-inspired techniques for color image segmentation. The simulation results of MMFO for region based color image segmentation are performed better as compared to PSO and GA, in terms of computation times for all the images. The experiment results of this method gives clear segments based on the different color and the different no. of clusters is used during the segmentation process.</span>

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Section: The Proposed Algorithm For Region-based Rgb Color Image Segmmentioning

confidence: 99%

MMFO: modified moth flame optimization algorithm for region based RGB color image segmentation

Jaiswal¹,

Sharma²,

Varma³

2020

IJECE

Self Cite

View full text Add to dashboard Cite

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“…The aim is to get the best model that could recognize TELKOMNIKA Telecommun Comput El Control  UNet-VGG16 with transfer learning for MRI-based brain tumor … (Anindya Apriliyanti Pravitasari) 1311 the tumor area more precisely. The previous studies use the clustering as the basis of segmentation are performed by [2] which uses the genetic algorithm and [3] which employ fuzzy clustering, Otsu method and K-means cluster to segment the vehicle image. The model-based clustering is performed by [4][5][6] in the form of a Finite mixture model to segment the MRI brain tumor image.…”

Section: Introductionmentioning

confidence: 99%

UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation

et al. 2020

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A brain tumor is one of a deadly disease that needs high accuracy in its medical surgery. Brain tumor detection can be done through magnetic resonance imaging (MRI). Image segmentation for the MRI brain tumor aims to separate the tumor area (as the region of interest or ROI) with a healthy brain and provide a clear boundary of the tumor. This study classifies the ROI and non-ROI using fully convolutional network with new architecture, namely UNet-VGG16. This model or architecture is a hybrid of U-Net and VGG16 with transfer Learning to simplify the U-Net architecture. This method has a high accuracy of about 96.1% in the learning dataset. The validation is done by calculating the correct classification ratio (CCR) to comparing the segmentation result with the ground truth. The CCR value shows that this UNet-VGG16 could recognize the brain tumor area with a mean of CCR value is about 95.69%.

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“…It has an important influence on the act of the segmentation algorithm. Numbers pre-processing stage, in biomedical image processing, contains of image cropping, filtering, segmentation, gradient operations and classification [10][11][12]. So, it is value to state that enhancing the segmentation process will advance the performance of a designated classification method [13].…”

Section: Introductionmentioning

confidence: 99%

A new swarm intelligence information technique for improving information balancedness on the skin lesions segmentation

Abd

Abdullah

Alkafaji

2020

IJECE

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Methods of image processing can recognize the images of melanoma lesions border in addition to the disease compared to a skilled dermatologist. New swarm intelligence technique depends on meta-heuristic that is industrialized to resolve composite real problems which are problematic to explain by the available deterministic approaches. For an accurate detection of all segmentation and classification of skin lesions, some dealings should be measured which contain, contrast broadening, irregularity quantity, choice of most optimal features, and so into the world. The price essential for the action of progressive disease cases is identical high and the survival percentage is low. Many electronic dermoscopy classifications are advanced depend on the grouping of form, surface and dye features to facilitate premature analysis of malignance. To overcome this problematic, an effective prototypical for accurate boundary detection and arrangement is obtainable. The projected classical recovers the optimization segment of accuracy in its pre-processing stage, applying contrast improvement of lesion area compared to the contextual. In conclusion, optimized features are future fed into of artifical bee colony (ABC) segmentation. Wide-ranging researches have been supported out on four databases named as, ISBI (2016, 2017, 2018) and PH2. Also, the selection technique outclasses and successfully indifferent the dismissed features. The paper shows a different process for lesions optimal segmentation that could be functional to a variation of images with changed possessions and insufficiencies is planned with multistep pre-processing stage.

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An implementation of novel genetic based clustering algorithm for color image segmentation

Cited by 19 publications

References 18 publications

MMFO: modified moth flame optimization algorithm for region based RGB color image segmentation

MMFO: modified moth flame optimization algorithm for region based RGB color image segmentation

UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation

A new swarm intelligence information technique for improving information balancedness on the skin lesions segmentation

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