Segmenting images with gradient-based edge detection using Membrane Computing

Díaz-Pernil, Daniel; Alcaraz, Ainhoa Berciano; Peña-Cantillana, Francisco; Gutiérrez–Naranjo, Miguel A.

doi:10.1016/j.patrec.2012.10.014

Cited by 58 publications

(32 citation statements)

References 39 publications

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“…Along with this line, Peng, et al (2012) proposed a novel threshold segmentation approach using cell-like P system (membrane algorithm) to improve threshold segmentation. Daz-Pernil, et al (2013) presented a novel device architecture called CUDATM to implement tissue-like P system MC rules for segmentation of images with gradient-based edge detection. Furthermore, proposed a novel segmentation by adaptive traditional region-based color segmentation method using tissue-like P system (membrane algorithm).…”

Section: Related Workmentioning

confidence: 99%

Three-dimensional Image Segmentation using Tissue-like P System

Yahya¹,

Yahya

Alsalibi

et al. 2017

ARO

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Abstract-Membrane computing (MC), which abstracts computational models from the structure and functioning of biological cells or population of cells in tissues, has served as a rich framework for handling many problems. Various types of P systems have been proposed in the literature to perform edge-based and region-based segmentation of two-dimensional digital images. However, less attention has been paid to the segmentation of threedimensional (3D) medical images. Hence, the main contribution of this paper is to propose a tissue-like P system for segmenting 3D medical images. To the best of our knowledge, this is the first work that practically adapts MC for 3D images. Experimental results demonstrate the efficiency of the proposed approach in segmenting 3D images, and it has the potential to be used in realworld applications.Index Terms-Membrane computing, Region-based image segmentation, Three-dimensional images, Tissue-like P systems.

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Section: Related Workmentioning

confidence: 99%

Three-dimensional Image Segmentation using Tissue-like P System

Yahya¹,

Yahya

Alsalibi

et al. 2017

ARO

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“…The experimental results from various broadcasting problems show that the proposed algorithm has better search capability, efficiency, solution stability, and precision than other optimization methods reported in the literature. In addition, Membrane Computing can be used in many other fields, such as computer graphics, linguistics, economics, computer science, and cryptography [30, 33, 34]. …”

Section: Theories Overviewmentioning

confidence: 99%

Bioinspired Intelligent Algorithm and Its Applications for Mobile Robot Control: A Survey

Fan

et al. 2016

Computational Intelligence and Neuroscience

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Bioinspired intelligent algorithm (BIA) is a kind of intelligent computing method, which is with a more lifelike biological working mechanism than other types. BIAs have made significant progress in both understanding of the neuroscience and biological systems and applying to various fields. Mobile robot control is one of the main application fields of BIAs which has attracted more and more attention, because mobile robots can be used widely and general artificial intelligent algorithms meet a development bottleneck in this field, such as complex computing and the dependence on high-precision sensors. This paper presents a survey of recent research in BIAs, which focuses on the research in the realization of various BIAs based on different working mechanisms and the applications for mobile robot control, to help in understanding BIAs comprehensively and clearly. The survey has four primary parts: a classification of BIAs from the biomimetic mechanism, a summary of several typical BIAs from different levels, an overview of current applications of BIAs in mobile robot control, and a description of some possible future directions for research.

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“…In the current study, a 5 × 5 neighborhood is used in order to control the blur effect. A new improved algorithm has been developed for this paper from the previous ideas presented in [39] that we called Color AGP Segmentator. This new operator is based on the changes of the intensity of the color of the image.…”

Section: Algorithm Of the Detection Of The Optic Discmentioning

confidence: 99%

Fully automatized parallel segmentation of the optic disc in retinal fundus images

Díaz-Pernil

Fondón

Peña-Cantillana

et al. 2016

Pattern Recognition Letters

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a b s t r a c tThis paper presents a fully automatic parallel software for the localization of the optic disc (OD) in retinal fundus color images. A new method has been implemented with the Graphics Processing Units (GPU) technology. Image edges are extracted using a new operator, called AGP-color segmentator . The resulting image is binarized with Hamadani's technique and, finally, a new algorithm called Hough circle cloud is applied for the detection of the OD. The reliability of the tool has been tested with 129 images from the public databases DRIVE and DIARETDB1 obtaining an average accuracy of 99.6% and a mean consumed time per image of 7.6 and 16.3 s respectively. A comparison with several state-of-the-art algorithms shows that our algorithm represents a significant improvement in terms of accuracy and efficiency.

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Segmenting images with gradient-based edge detection using Membrane Computing

Cited by 58 publications

References 39 publications

Three-dimensional Image Segmentation using Tissue-like P System

Three-dimensional Image Segmentation using Tissue-like P System

Bioinspired Intelligent Algorithm and Its Applications for Mobile Robot Control: A Survey

Fully automatized parallel segmentation of the optic disc in retinal fundus images

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