Fuzzy c-Means (FCM) for Optic Cup and Disc Segmentation with Morphological Operation

Khalid, Norlin; Noor, Noorhayati Mohamed; Ariff, Norharyati Md

doi:10.1016/j.procs.2014.11.060

Cited by 52 publications

(20 citation statements)

References 11 publications

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“…Fermentation engineering technology promotes the industrialization of fermentation products [9]. The accurate control strategy can greatly improve the fermentation products [10]. The differential algorithm is a random heuristic search algorithm.…”

Section: Discussionmentioning

confidence: 99%

The Feeding Rate Optimization Based on Differential Evolution Algorithm

Hua¹,

Lai²,

Zheng³

2016

Proceedings of the 2016 International Conference on Sensor Network and Computer Engineering

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Bacteria concentration is one of the important parameters throughout the fermentation process, and in the whole process of fermentation it is an important basis for judging the feeding. Bacteria concentration in the end is the important factor that affects product concentration, so the appropriate bacteria concentration is a top priority in the fermentation process. Pichia expression of recombinant protein fermentation process acts as the research background, and the differential evolution (DE) algorithm is applied to optimize the feeding. The propose is to get more bacteria concentration by optimizing glycerol and ammonia feeding rate in bacteria growth stage. The simulation results indicate that the method is effective in solving the optimization problem of feeding rate, and that the feasibility of the optimization algorithm in the fermentation process is verified.

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Section: Discussionmentioning

confidence: 99%

The Feeding Rate Optimization Based on Differential Evolution Algorithm

Hua¹,

Lai²,

Zheng³

2016

Proceedings of the 2016 International Conference on Sensor Network and Computer Engineering

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“…(2) FCM clustering algorithm FCM was proposed due to its accuracy in segmentation with the presence of intensity inhomogeneity. Pixels were grouped on the basis of membership assigned to pixel according to the Euclidean distance between cluster center and pixel [20] . The parameter C corresponded to the number of data set, set C=3 as we had done in the k-means clustering method.…”

Section: Segmentation Of Apple Image Using Clustering Algorithmmentioning

confidence: 99%

Detection of green apples in natural scenes based on saliency theory and Gaussian curve fitting

Song

2018

International Journal of Agricultural and Biological Engineering

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Green apple targets are difficult to identify for having similar color with backgrounds such as leaves. The primary goal of this study was to detect green apples in natural scenes by applying saliency detection and Gaussian curve fitting algorithm. Firstly, the image was represented as a close-loop graph with superpixels as nodes. These nodes were ranked based on the similarity to background and foreground queries to generate the final saliency map. Secondly, Gaussian curve fitting was carried out to fit the V-component in YUV color space in salient areas, and a threshold was selected to binarize the image. To verify the validity of the proposed algorithm, 55 images were selected and compared with the common used image segmentation algorithms such as k-means clustering algorithm and FCM (Fuzzy C-means clustering algorithm). Four parameters including recognition ratio, FPR (false positive rate), FNR (false negative rate) and FDR (false detection rate) were used to evaluate the results, which were 91.84%, 1.36%, 8.16% and 4.22%, respectively. The results indicated that it was effective and feasible to apply this method to the detection of green apples in nature scenes. Citation: Li B R, Long Y, Song H B. Detection of green apples in natural scenes based on saliency theory and Gaussian curve fitting. Int J Agric & Biol Eng, 2018; 11(1): 192-198. Li B R, et al. Detection of green apples in natural scenes based on saliency theory and Gaussian curve fitting Vol.

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“…O método atingiu, no cálculo do CDR, uma Acurácia de 89% e um Erro Médio Absoluto de 0,061. Khalid et al (2014) aplicaram operações morfológicas para apagar os vasos sanguíneos e suavizar as intensidades ao redor do centro do DO. Posteriormente foi aplicado o K-means Fuzzy no canal de cor verde, do sistema RGB, para segmentar as regiões de escavação e DO.…”

Section: Algoritmos De Agrupamentounclassified

Métodos computacionais para segmentação do disco óptico em imagens de retina: uma revisão

Claro

Veras

Santos

et al. 2018

RBCA

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A utilização de técnicas de processamento digital de imagens (PDI) é destaque no cenário médico para o diagnóstico automático de patologias. Na área oftalmológica o glaucoma é a segunda principal causa da perda de visão no mundo e não possui cura. Atualmente, existem tratamentos para prevenir a perda da visão, contudo a doença deve ser descoberta nos estágios iniciais. O objetivo principal deste artigo é revisar as metodologias e técnicas de segmentação dos limites do disco óptico e escavação. Essas regiões são utilizadas para o cálculo de métricas para classi cação do glaucoma e auxílio aos pro ssionais da área. Os trabalhos mais recentes publicados na área foram classi cados em cinco grupos de acordo com a principal técnica de PDI aplicada: agrupamento, superpixel, contorno ativo, morfologia matemática e redes neurais convolucionais. Além disso, foi realizado um levantamento das principais bases de imagens e métricas de avaliação utilizadas.Palavras-Chave: Algoritmos de Agrupamento; Contorno Ativo; Glaucoma; Morfologia matemática; Redes neurais convolucionais; Superpixel. Abstract The use of digital image processing techniques (DIP) is highlighted in the medical scenario for automatic diagnosis of pathologies. In the ophthalmologic area, glaucoma is the second leading cause of vision loss in the world

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Fuzzy c-Means (FCM) for Optic Cup and Disc Segmentation with Morphological Operation

Cited by 52 publications

References 11 publications

The Feeding Rate Optimization Based on Differential Evolution Algorithm

The Feeding Rate Optimization Based on Differential Evolution Algorithm

Detection of green apples in natural scenes based on saliency theory and Gaussian curve fitting

Métodos computacionais para segmentação do disco óptico em imagens de retina: uma revisão

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