Region growing based segmentation with automatic seed selection using threshold techniques on X-radiography images

Malarvel, Muthukumaran; Sethumadhavan, Gopalakrishnan; Bhagi, Purna Chandra Rao; Thangavel, Saravanan; Krishnan, Arunmuthu

doi:10.1109/iccic.2016.7919673

Cited by 5 publications

(1 citation statement)

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“…[14], utilized the RG division to distinguish whether the pixels are a piece of the edges or the textures. Malarvel et al [15] Presented a new segmentation scheme based on RG method without implication of preprocessing techniques in the input image. Happ et al [16] presents another method for distributed RG picture segmentation dependent on the MapReduce model (the most used programming models to process a tremendous volume of data in the cloud).…”

Section: Related Workmentioning

confidence: 99%

Karınca Aslanı Optimizasyon Algoritması Kullanarak Görüntü Segmentasyonu İçin Modifiye Edilmiş Bölge Genişletme Yöntemi

Jama¹,

Baykan²

2020

European Journal of Science and Technology

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Image segmentation is a significant step in image processing that applies to various fields. These fields include machine vision, object detection, astronomy, biometric recognition systems (face, fingerprint, plate, and eye), medical imaging, video surveillance, and many other image-based technologies. Efficient image segmentation is one of the most important tasks and critical roles in automatic image processing. Especially in engineering studies, finding the most suitable solutions for problems is one of the important research topics. Bio-inspired algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Bat Algorithm (BAT), etc. are used to find the optimal solutions in search spaces and Ant Lion Optimization (ALO) is one of these algorithms. In recent years, bio-inspired algorithms are used to optimize the segmentation parameters of the images. This research proposes a modified region growing (RG) image segmentation approach using bio-inspired ALO. Region growing (RG) has three main problems as the selection of the right seeds, the number of seeds, and the region growing strategy. Therefore, ALO was used to solve seed selection problems in RG. In this study, firstly, the median filter was applied to the inputs to improve the quality of the images. Subsequently, the region growing segmentation was carried out using optimal seed points obtained from the ALO. For obtaining the optimal seeds, ALO was used to solve the limitations of RG during the segmentation process. The success of the proposed approach was tested using some images taken from the BSDS300 (Berkeley) dataset. The experimental results show that the proposed method segments almost all the images.

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Section: Related Workmentioning

confidence: 99%