An evaluation of particle swarm optimization techniques in segmentation of biomedical images

Lahmiri, Salim; Boukadoum, Mounir

doi:10.1145/2598394.2609855

Cited by 9 publications

(9 citation statements)

References 16 publications

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“…PSO technique has shown its superiority over conventional thresholding algorithms including fuzzy C-means and Otsu techniques for multidimensional biomedical images, especially retinal blood vessel segmentation. 31,32…”

Section: En-face CVI Estimationmentioning

confidence: 99%

En-face choroidal vascularity map of the macula in healthy eyes

Singh

Rasheed

Parveen

et al. 2019

European Journal of Ophthalmology

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Purpose: To report the en-face choroidal vascularity index in healthy eyes. Methods: Thirty eyes of 30 healthy individuals were studied. Multiple high-density cross-sectional swept source optical coherence tomography scans were obtained to create a volume scan. The choroid was segmented for the whole volume scan and choroidal inner boundaries were flattened. Subsequently, multiple en-face scans separated by 25 µm were obtained and binarized. Choroidal vascularity index was calculated at level of choriocapillaris, medium, and large choroidal vessels. Results: The mean age of the study cohort was 35.6 ± 8.8 years. The overall mean en-face choroidal vascularity index was 54.25 ± 0.55%. There was a statistically significant difference of choroidal vascularity index in choriocapillaris (53.16 ± 0.43%), medium choroidal vessel (51.38 ± 0.27%), and large choroidal vessel (55.69 ± 0.87%) (p < 0.01). Choroidal vascularity index analysis in three subgroups based on subfoveal choroidal thickness (low: <300 µm, medium: 300–400 µm, high: >400 µm) showed a statistically significant difference (p = 0.001). Choroidal vascularity index showed a significant correlation with subfoveal choroidal thickness (r = 0.441; p = 0.015), whereas there was no significant correlation of age (p = 0.21), refraction (p = 0.20), and gender (p = 0.67) with en-face choroidal vascularity index. Conclusion: En-face choroidal vascularity index shows a significant variation at the level of choriocapillaris, medium choroidal vessel, and large choroidal vessel in normal eyes. Choroidal vascularity index reaches a nadir at the level of medium choroidal vessel and reaches the maximum value at large choroidal vessel near choroidoscleral interface. En-face choroidal vascularity index shows a significant physiological variation and appears to increase with increase in subfoveal choroidal thickness.

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Section: En-face CVI Estimationmentioning

confidence: 99%

En-face choroidal vascularity map of the macula in healthy eyes

Singh

Rasheed

Parveen

et al. 2019

European Journal of Ophthalmology

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“…They also compared the segmentation performance of PSO, DPSO, and FODPSO as parametric approaches to existing methods, namely the parametric fuzzy c-means (FCM) algorithm and the non-parametric Otsu segmentation technique, by applying the different techniques to five biomedical images. The obtained experimental results showed that particle swarm-based algorithms outperformed both FCM and Otsu segmentation techniques [5]. However, these methods easily fall into a local optimum due to the lack of dynamic adjustment of speed, which leads to low convergence accuracy and difficulty in convergence.…”

Section: Related Workmentioning

confidence: 99%

Image Segmentation of Brain MRI Based on LTriDP and Superpixels of Improved SLIC

Wang

Shen

2020

Brain Sciences

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Non-uniform gray distribution and blurred edges often result in bias during the superpixel segmentation of medical images of magnetic resonance imaging (MRI). To this end, we propose a novel superpixel segmentation algorithm by integrating texture features and improved simple linear iterative clustering (SLIC). First, a 3D histogram reconstruction model is used to reconstruct the input image, which is further enhanced by gamma transformation. Next, the local tri-directional pattern descriptor is used to extract texture features of the image; this is followed by an improved SLIC superpixel segmentation. Finally, a novel clustering-center updating rule is proposed, using pixels with gray difference with original clustering centers smaller than a predefined threshold. The experiments on the Whole Brain Atlas (WBA) image database showed that, compared to existing state-of-the-art methods, our superpixel segmentation algorithm generated significantly more uniform superpixels, and demonstrated the performance accuracy of the superpixel segmentation in both fuzzy boundaries and fuzzy regions.

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“…In the image segmentation framework, the pixel set matches to the lookup space, along with the optimum solution matches to exploiting the between-class deviation of the distribution of intensity degrees in the image. Following the notation in [27] notation, at time t each particle moves in a search space with position and velocity which are dependent on local best position neighborhood best and global best information as follows:…”

Section: Particle Swarm Optimization Techniquesmentioning

confidence: 99%

“…(2). The fitness function  used to evaluate the performance of the particles is given by [27] [28]:…”

Section: Particle Swarm Optimization Techniquesmentioning

confidence: 99%

Neuroendoscopy Adapter Module Development for Better Brain Tumor Image Visualization

Bangare

Pradeepini

Patil³

2017

IJECE

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The issue of brain magnetic resonance image exploration together with classification receives a significant awareness in recent years. Indeed, various computer-aided-diagnosis solutions were suggested to support radiologist in decision-making. In this circumstance, adequate image classification is extremely required as it is the most common critical brain tumors which often develop from subdural hematoma cells, which might be common type in adults. In healthcare milieu, brain MRIs are intended for identification of tumor. In this regard, various computerized diagnosis systems were suggested to help medical professionals in clinical decision-making. As per recent problems, Neuroendoscopy is the gold standard intended for discovering brain tumors; nevertheless, typical Neuroendoscopy can certainly overlook ripped growths. Neuroendoscopy is a minimally-invasive surgical procedure in which the neurosurgeon removes the tumor through small holes in the skull or through the mouth or nose. Neuroendoscopy enables neurosurgeons to access areas of the brain that cannot be reached with traditional surgery to remove the tumor without cutting or harming other parts of the skull. We focused on finding out whether or not visual images of tumor ripped lesions ended up being much better by auto fluorescence image resolution as well as narrow-band image resolution graphic evaluation jointly with the latest neuroendoscopy technique. Also, within the last several years, pathology labs began to proceed in the direction of an entirely digital workflow, using the electronic slides currently being the key element of this technique. Besides lots of benefits regarding storage as well as exploring capabilities with the image information, among the benefits of electronic slides is that they can help the application of image analysis approaches which seek to develop quantitative attributes to assist pathologists in their work. However, systems also have some difficulties in execution and handling. Hence, such conventional method needs automation. We developed and employed to look for the targeted importance along with uncovering the best-focused graphic position by way of aliasing search method incorporated with new Neuroendoscopy Adapter Module (NAM) technique.

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An evaluation of particle swarm optimization techniques in segmentation of biomedical images

Cited by 9 publications

References 16 publications

En-face choroidal vascularity map of the macula in healthy eyes

En-face choroidal vascularity map of the macula in healthy eyes

Image Segmentation of Brain MRI Based on LTriDP and Superpixels of Improved SLIC

Neuroendoscopy Adapter Module Development for Better Brain Tumor Image Visualization

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