Ultrasound image segmentation with multilevel threshold based on differential search algorithm

Shao, Dangguo; Xu, Chong; Xiang, Yan; Gui, Peng; Zhu, Xiaodong; Zhang, Chao; Yu, Zhengtao

doi:10.1049/iet-ipr.2018.6150

Cited by 31 publications

(9 citation statements)

References 34 publications

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“…If the pixel intensity value between the two modes is lower than the threshold, it forms the foreground, i.e., the impurities, also known as the white pixel. However, if the pixel intensity is higher than the threshold, it forms the background, that is the lotus root, with the dark pixel (Shao et al, 2019). Figure 10 show the lotus root after global thresholding analysis.…”

Section: Impurities Detection Analysismentioning

confidence: 99%

Detection of Lotus Root Contaminants Using Intelligent Visual Machine Vision Techniques

Yuan

Wilson

Asamoah

et al. 2021

JFI

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Lotus root, which is a water plant cherished by people in the Asian continent and some other parts of the world, is manually inspected for quality by experts to detect impurities. There is the need to update this inspection process in order to improve the quality and safety of lotus root. Machine vision systems and techniques are used for consistent, efficient, effective, and reliable inspection of images. The lotus root inspection system has been proposed to inspect the lotus roots for impurities. The detection algorithms use the size, shape, texture and color of the lotus root images as parameters to analyze the quality of lotus roots. The lotus root undergoes some processes before image acquisition and image processing. The camera and illumination used, in collaboration with the edge detection, and image segmentation techniques, efficiently and effectively exposed the impurities in the lotus root at a much faster rate. Also, it is less expensive compared to the traditional human inspections.

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Section: Impurities Detection Analysismentioning

confidence: 99%

Detection of Lotus Root Contaminants Using Intelligent Visual Machine Vision Techniques

Yuan

Wilson

Asamoah

et al. 2021

JFI

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“…Fitness function for the firefly algorithm that was used in this paper is Otsu's criterion that is used for finding the optimal threshold values based on the image histogram. Otsu's method was frequently used for image segmentation (Shao et al 2019;Wang & Cao, 2019). Quality of the clustering or segmentation is determined by the overall inter-cluster distance.…”

Section: Combined Firefly Algorithm With K-meansmentioning

confidence: 99%

Brain Image Segmentation Based on Firefly Algorithm Combined with K-means Clustering

Hrosik¹,

Tuba²,

Dolicanin³

et al. 2019

STUD INFORM CONTROL

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During the past few decades digital images have become an important part of numerous scientific fields. Digital images used in medicine enabled tremendous progress in the diagnostics, treatment determination process as well as in monitoring patient recovery. Detection of brain tumors represents one of the active research fields and an algorithm for brain image segmentation was developed with an aim to emphasize four different primary brain tumors: glioma, metastatic adenocarcinoma, metastatic bronchogenic carcinoma and sarcoma from PET, MRI and SPECT images. The proposed image segmentation method is based on the firefly algorithm whose solutions are improved by the k-means clustering algorithm when Otsu's criterion was used as the fitness function. The proposed combined algorithm was tested on commonly used images from Harvard Whole Brain Atlas and the results were compared to other method from literature. The method proposed in this paper achieved better segmentation considering standard segmentation quality metrics such as normalized root square mean error, peak signal to noise and structural similarity index metric.

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“…However, in the traditional OTSU algorithm, the search process of thresholds is done by exhausting all solutions in the gray space, so as the number of thresholds increases, the search dimension to be performed is also increasing, and the complexity is greatly increased, many unnecessary calculations are performed, the time is exponentially increasing, and the search efficiency is low [4]. Moreover, in general, the images acquired through various channels suffer from many random disturbances and conditions, so the acquired original images contain a lot of noise, which makes the features of things in the acquired original images change greatly, and if such images are analyzed directly, the understanding of the images will be greatly biased [5].…”

Section: Introductionmentioning

confidence: 99%

Otsu’s Image Segmentation Algorithm with Memory‐Based Fruit Fly Optimization Algorithm

Chai

2021

Complexity

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In this paper, the most common pepper noise in grayscale image noise is investigated in depth in the median filtering algorithm, and the improved median filtering algorithm, adaptive switching median filtering algorithm, and adaptive polar median filtering algorithm are applied to the OTSU algorithm. Two improved OTSU algorithms such as the adaptive switched median filter-based OTSU algorithm and the polar adaptive median filter-based OTSU algorithm are obtained. The experimental results show that the algorithm can better cope with grayscale images contaminated by pretzel noise, and the segmented grayscale images are not only clear but also can better retain the detailed features of grayscale images. A genetic algorithm is a kind of search algorithm with high adaptive, fast operation speed, and good global space finding ability, and it will have a good effect when applied to the threshold finding of the OTSU algorithm. However, the traditional genetic algorithm will fall into the local optimal solution in different degrees when finding the optimal threshold. The advantages of the two interpolation methods proposed in this paper are that one is the edge grayscale image interpolation algorithm using OTSU threshold adaptive segmentation and the other is the edge grayscale image interpolation algorithm using local adaptive threshold segmentation, which can accurately divide the grayscale image region according to the characteristics of different grayscale images and effectively improve the loss of grayscale image edge detail information and jagged blur caused by the classical interpolation algorithm. The visual effect of grayscale images is enhanced by selecting grayscale images from the standard grayscale image test set and interpolating them with bilinear interpolation, bucolic interpolation, NEDI interpolation, and FEOI interpolation for interpolation simulation validation. The subjective evaluation and objective evaluation, as well as the running time, are compared, respectively, showing that the method of this paper can effectively improve the quality of grayscale image interpolation.

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Ultrasound image segmentation with multilevel threshold based on differential search algorithm

Cited by 31 publications

References 34 publications

Detection of Lotus Root Contaminants Using Intelligent Visual Machine Vision Techniques

Detection of Lotus Root Contaminants Using Intelligent Visual Machine Vision Techniques

Brain Image Segmentation Based on Firefly Algorithm Combined with K-means Clustering

Otsu’s Image Segmentation Algorithm with Memory‐Based Fruit Fly Optimization Algorithm

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