Image Segmentation using FCM-Darwinian Particle Swarm Optimization

Rawat, Sachin; Gupta, Bhumika

doi:10.1109/icrieece44171.2018.9009349

Cited by 3 publications

(3 citation statements)

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“…Their simulations show that in terms of visual effects or eliminating too much segmentation, the position and contour of the area of noise and other properties. Rawat and Gupta (2018) proposed a method that combines fuzzy C means and Darwinian particle swarm optimization (PSO). Among fuzzy-based clustering algorithms, the FCM algorithm is the most popular, butit can have a locally optimal solution because of a random centroid onset.…”

Section: Objectivesmentioning

confidence: 99%

“…With the proposed method, lesion parts from different medical images can be segmented, and multiple images can be obtained with high accuracy by using FCM-Darwinian PSO to detect the outer lines of images. The search results of the algorithm are evaluated by various parameters, such as sensitivity, specification, Jaccard index and dice coefficient (Rawat and Gupta 2018). Saravanan and Sathiamoorthy (2018) developed a computerized segmentation technique based on active contours without outline techniques for an effective PCOS classification of 3D ultrasound images.…”

Section: Objectivesmentioning

confidence: 99%

“…Image Segmentation Using FCM-Darwinian Particle Swarm Optimization (Rawat and Gupta 2018) Darwinian PSO and fuzzy C means are combined to solve image segmentation problems.…”

Section: Literature Reviewmentioning

confidence: 99%

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Ultrasound Image Segmentation using a Combination of Edge Enhancement and Kirsch’s template Method to detecting Follicles in Ovaries

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Polycystic ovary syndrome is a common medical problem affecting women worldwide. It is attributed to excessive hormone production that causes an increase in the number of follicles in the ovaries. To detect this condition, specialist doctors examine ultrasound images by calculating the number and size of follicles in the ovaries. However, this procedure can yield inaccurate findings and endanger patients. Another tool used in this field is an ultrasonogram, but it is considered inaccurate because the noise from application processing can produce blurred images that appear to be clear contact segments. In this study, a segmentation method for detecting follicles in the ovaries is proposed to analyse the image quality of an ovarian segment. The watershed method and a combination of the edge enhancement method and Kirsch's template are implemented to create and efficiently examine a segmented image of an ovary. Results show that the proposed method with the addition of a histogram on the threshold image can be effectively used to extract the morphological closing image of follicles. Follicles can be detected more clearly, and segmentation can be performed more efficiently. The mean square error and the peak signal-to-noise ratio of the combined methods and the watershed method have no significant differences. Thus, the combined method can produce clear images and reveal the number of follicles more quickly and easily than the watershed method.

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Section: Objectivesmentioning

confidence: 99%

Section: Objectivesmentioning

confidence: 99%