Adaptive multilayer level set method for segmenting images with intensity inhomogeneity

Huang, Guopeng; Ji, Hongbing; Zhang, Wenbo; Zhu, Zhigang

doi:10.1049/iet-ipr.2019.0315

Cited by 6 publications

(3 citation statements)

References 46 publications

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“…LSA represents a closed plane curve as a continuous functional surface implicitly [1]. The advantage of LSA is the evolution of curves and surfaces can be calculated in a fixed coordinate system without knowing the exact parameters of the curves and surfaces [18]. It is advantageous for processing images with weak edge features.…”

Section: Level Set Algorithmmentioning

confidence: 99%

A Novel Method for Peanut Seed Plumpness Detection in Soft X-ray Images Based on Level Set and Multi-Threshold OTSU Segmentation

Liu,

Qiu,

Wang

2024

Agriculture

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The accurate assessment of peanut seed plumpness is crucial for optimizing peanut production and quality. The current method is mainly manual and visual inspection, which is very time-consuming and causes seed deterioration. A novel imaging technique is used to enhance the detection of peanut seed fullness using a non-destructive soft X-ray, which is suitable for the analysis of the surface or a thin layer of a material. The overall grayscale of the peanut is similar to the background, and the edge of the peanut seed is blurred. The inaccuracy of peanut overall and peanut seed segmentation leads to low accuracy of seed plumpness detection. To improve accuracy in detecting the fullness of peanut seeds, a seed plumpness detection method based on level set and multi-threshold segmentation was proposed for peanut images. Firstly, the level set algorithm is used to extract the overall contour of peanuts. Secondly, the obtained binary image is processed by morphology to obtain the peanut pods (the peanut overall). Then, the multi-threshold OTSU algorithm is used for threshold segmentation. The threshold is selected to extract the peanut seed part. Finally, morphology is used to complete the cavity to achieve the segmentation of the peanut seed. Compared with optimization algorithms, in the segmentation of the peanut pods, average random index (RI), global consistency error (GCE) and variation of information (VI) were increased by 10.12% and decreased by 0.53% and 24.11%, respectively. Compared with existing algorithms, in the segmentation of the peanut seed, the average RI, VI and GCE were increased by 18.32% and decreased by 9.14% and 6.11%, respectively. The proposed method is stable, accurate and can meet the requirements of peanut image plumpness detection. It provides a feasible technical means and reference for scientific experimental breeding and testing grading service pricing.

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Section: Level Set Algorithmmentioning

confidence: 99%

A Novel Method for Peanut Seed Plumpness Detection in Soft X-ray Images Based on Level Set and Multi-Threshold OTSU Segmentation

Liu,

Qiu,

Wang

2024

Agriculture

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“…Wang et al [29] constructed the local and global intensity fitting (LGIF) energy model based on the C-V algorithm and the LBF algorithm, which effectively addresses the issue of intensity inhomogeneity in images [30]. In addition, Liu and He [31] introduced the C-V model into the coefficient of variation model and proposed a piecewise constant image segmentation method that utilizes ordinary differential evolution equation. This method requires less iterations, performs fast segmentation, and is robust to the position, size, and morphology of the initial contour curve [8].…”

Section: Introductionmentioning

confidence: 99%

Active contour model based on hybrid signed pressure force function

Meng

2023

IET Image Processing

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To segment noisy and multi‐target images, an active contour model based on a hybrid signed pressure force function that fuses global and local information of the image is proposed. Firstly, a local signed pressure force function is defined using the local area information of the image. Then, it is combined with the existing global grey density function to construct a new signed pressure force function. Finally, the selective binary and Gaussian filtering regularized level set are modified using the newly defined function. Extensive experiments and comparisons on both synthetic and real images demonstrate that the proposed method is robust to noise and can handle noisy and multi‐target images with high accuracy.

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“…In recent decades, the level set technique has become widespread and widely used in the field of image processing, especially in image segmentation. In fact, from different points of view, we can classify the use of the level set method in image segmentation into two main categories: region-based models [3,5,6,9,10,11,13,14,21,23,24,25,27,28] and edge-based models [1,2,4,8,15,18,22].…”

Section: Introductionmentioning

confidence: 99%

A fast level set image segmentation driven by a new region descriptor

Birane

Hamami

2021

IET Image Processing

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In order to deal with the intensities inhomogeneities and to overcome the effect of different types of noise in the image segmentation process, we have formulated a new level set function to implement a fast and robust active contour model. The proposed model was formulated by combining the SBGFRLS model and Legendre polynomials. With the aim of ensuring the segmentation accuracy and for dealing in the best way with the presence of noises and inhomogenous distribution of intensity, we define a local region descriptor for image intensities based on the Legendre polynomial. Instead of using the average intensity of the region, we regularised our level set function using a Gaussian filtering process. Experimental results on challenging images demonstrate the efficiency, robustness and the low cost and computational time of our model against the well‐known active contour models.

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Adaptive multilayer level set method for segmenting images with intensity inhomogeneity

Cited by 6 publications

References 46 publications

A Novel Method for Peanut Seed Plumpness Detection in Soft X-ray Images Based on Level Set and Multi-Threshold OTSU Segmentation

A Novel Method for Peanut Seed Plumpness Detection in Soft X-ray Images Based on Level Set and Multi-Threshold OTSU Segmentation

Active contour model based on hybrid signed pressure force function

A fast level set image segmentation driven by a new region descriptor

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