Differential Evolution Algorithm For Segmentation Of Wound Images

Aslantaş, Veysel; Tunckanat, Mehmet

doi:10.1109/wisp.2007.4447606

Cited by 24 publications

(5 citation statements)

References 16 publications

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“…Several studies proved its suitability for solving numerical optimization problems, having a good global convergence and robustness. DE has been applied fruitfully in constrained image classification [ 73 ], image segmentation [ 74 ], neural networks [ 75 ], linear array [ 76 ], global optimization problems [ 77 ], and other areas [ [78] , [79] , [80] , [81] ].…”

Section: The Modelmentioning

confidence: 99%

Learning deep neural networks' architectures using differential evolution. Case study: Medical imaging processing

Belciug

2022

Computers in Biology and Medicine

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Section: The Modelmentioning

confidence: 99%

Learning deep neural networks' architectures using differential evolution. Case study: Medical imaging processing

Belciug

2022

Computers in Biology and Medicine

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“…This method uses the K-Nearest Neighbor (KNN) algorithm to classify the wound healing. This method includes many phases, there are (Aslantas and Tunckanat, 2007): read image, detect entire wound, fill gaps, dilate the image, fill interior gaps, remove connected objects on border and smooth the object. This method obtains good results during the validation tests and it obtains very low errors.…”

Section: Related Workmentioning

confidence: 99%

Wound Area Assessment using Mobile Application

Pires

García

2015

Proceedings of the International Conference on Biomedical Electronics and Devices

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This research aims to discover methods for the detection of the area of a wound using mobile devices. These devices have low memory and low processing capacity and they need the use of low complexity operations to identify a wound. The calculation of the wounded area consists of three phases, there are: image acquisition, image processing, surface reconstruction and calculations. This research is related to the use a mobile device to identify wound contours and area in a captured image. This image can be captured with a camera in a smartphone and the wound area is calculated based on the distance of the surface area and the resolution of the image captured. The main study in this research is the image processing in a mobile device, due to the limitations of these devices. However, the application developed during this research was developed for desktop, using the OpenCV library that is compatible with the Android platform and Java desktop technologies. During this research, the developed code written in Java will be easily adapted to the Android platform. The desktop application developed is available in a free repository for testing.

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“…However, more recently, biologically inspired methods have been used as computationally efficient alternatives to analytical methods to solve optimization problems [27], [23], [28]. In this paper, we used the ABC [14] algorithm to solve the aforementioned optimization problem.…”

Section: Problem Descriptionmentioning

confidence: 99%

Artificial bee colony algorithm for automatic leukocytes segmentation in histopathological images

Sharma

Arya

Saraswat

2014

2014 9th International Conference on Industrial and Information Systems (ICIIS)

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An automatic segmentation of leukocytes can assist pharmaceutical companies to take decisions in the discovery of drug and encourages for development of automated leukocytes recognition system. Segmentation of leukocytes in tissue images is a complex process due to the presence of various noise effects, large variability in the images, and shape of the nuclei. Surprisingly, rare efforts have been done to automate the segmentation of leukocytes in various disease models on Hematoxylin and Eosin stained tissue images. The present work proposes a novel method based on artificial bee colony algorithm to segment the leukocytes from the images of mice skin sections stained with Hematoxylin and Eosin staining and acquired at 40 x magnification. The resultsshow that the proposed method outperforms when compared with the well known segmentation methods.

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Differential Evolution Algorithm For Segmentation Of Wound Images

Cited by 24 publications

References 16 publications

Learning deep neural networks' architectures using differential evolution. Case study: Medical imaging processing

Learning deep neural networks' architectures using differential evolution. Case study: Medical imaging processing

Wound Area Assessment using Mobile Application

Artificial bee colony algorithm for automatic leukocytes segmentation in histopathological images

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