Customized RBF kernel graph-cut for weak boundary image segmentation

Niazi, Mehrnaz; Rahbar, Kambiz; Sheikhan, Mansour; Khademi, Maryam

doi:10.1007/s11760-023-02546-7

Cited by 4 publications

(9 citation statements)

References 32 publications

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“…The results of the proposed method are compared with 11 kernel graph cut methods: RBF, 11 entropy-based, 19 Krawtchouk, 24 L2S, 37 LoGRSF, 38 LocalPrefitting, 39 AMOE, 28 edge reg, 32 curvature reg, 33 spatial reg, 34 and customized RBF. 27 It should be mentioned that, to make a fair comparison, the coefficient of the regularization term in the RBF and entropy algorithm is considered to be the middle limit and is equal to 0.5. To evaluate the performance of the above segmentation methods, six criteria are adopted: Dice similarity, Jaccard similarity, 40 accuracy, 41 peak-signal-to noise ratio (PSNR), 42 mean sum of squared distance (MSSD), 43 and elapsed time.…”

Section: Laboratory Resultsmentioning

confidence: 99%

“…It is also claimed that the above method can provide better results by analyzing the shape of objects. In addition, Niazi et al 27 involved fusing the first four terms from the MacLaurin series for sine and cosine functions based on image energy as a kernel function. This customization is specifically designed to address the challenge of segmenting weak boundary images, particularly in scenarios, such as cellular images, in which narrow and close boundaries are prevalent.…”

Section: Introductionmentioning

confidence: 99%

“…It is also claimed that the above method can provide better results by analyzing the shape of objects. In addition, Niazi et al 27 . involved fusing the first four terms from the MacLaurin series for sine and cosine functions based on image energy as a kernel function.…”

Section: Introductionmentioning

confidence: 99%

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Setting the regularization coefficient based on image energy in image segmentation using kernel graph cut algorithm

Niazi,

Rahbar

2024

J. Electron. Imag.

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The kernel graph cut method provides good performance in image segmentation. However, its efficiency strongly depends on the data term and regularization term in the objective function. The data term maps the standard deviation of the data of each region to the transformation space. The regularization term is responsible for smoothing the boundaries. The regularization term, based on a constant coefficient, is added to the data term. Allocating a fixed coefficient for all images leads to inappropriate image segmentation. In this regard, we deal with the automatic adjustment of the regularization term coefficient in the kernel graph cut method based on image energy. This approach is useful in image segmentation with thin structures. The laboratory results were taken on three datasets: fingerprint, coronary artery, and real. The efficiency evaluation of the algorithm, compared with other methods in the field of energy-based algorithms, shows a maximum score of 88.27 in Dice, 79.67 in Jaccard, 75.67 in accuracy, 61.06 in peak-signal-to noise ratio, and a minimum score of 0.06 in mean sum of squared distance on the mentioned datasets. The proposed method also takes advantage of appropriate computing load in the methods based on a graph cut.

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Section: Laboratory Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Setting the regularization coefficient based on image energy in image segmentation using kernel graph cut algorithm

Niazi,

Rahbar

2024

J. Electron. Imag.

Self Cite

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“…To evaluate the proposed algorithm, a comprehensive test has been conducted. The results of the proposed method with six energy-based algorithms, such as RBF kernel, 24 Krawtchouk, 25 TextureRBF, 26 L2S, 22 ABC-level set, 27 and customized RBF 28 and using four datasets Brodatz, 29 FABRIC 30 artificial images and real images has been compared. It should be noted that these datasets have a total of 120 images.…”

Section: Resultsmentioning

confidence: 99%

Local energy-based image region segmentation using Legendre polynomials

Mahmoudian

Niazi

2023

J. Electron. Imag.

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.Segmentation of images is a critical pre-processing step in image processing. The objective of segmentation is to divide an image into two homogeneous segments based on similar features. Recently, energy-based segmentation methods, such as the level set algorithm, have exhibited remarkable performance in segmenting various types of image data. However, such methods fail to produce satisfactory results on textured images, leading to inaccurate foreground and background segmentation. To address this issue, the proposed method utilizes a feature space based on image local energy, which is independent of image intensity. This feature space provides a more robust representation of texture in the image. By taking advantage of a unique feature space, images can be represented with a sum of Legendre polynomials. The reproduced image is injected into the L2S algorithm. The level set function is optimized from the average difference of Legendre polynomial coefficients to provide the best representation of the image. Consequently, the proposed method results in better segmentation accuracy, with an average increase of 19.33 compared to existing state-of-the-art methods.

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“…Graph cut is a semi-automatic segmentation technique that does not require good initialization. 27 The kernel regression is a non-parametric random conditional expectation prediction technique used in statistics to find the nonlinear relationship of a pair of random variables. 28 Drawing lines on the image, called scribbles, to identify what you want in the foreground and what you want in the background.…”

Section: Traditional Methodsmentioning

confidence: 99%

Stenosis Detection and Quantification of Coronary Artery Using Machine Learning and Deep Learning

Zhang

2023

Angiology

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The aim of this review is to introduce some applications of artificial intelligence (AI) algorithms for the detection and quantification of coronary stenosis using computed tomography angiography (CTA). The realization of automatic/semi-automatic stenosis detection and quantification includes the following steps: vessel central axis extraction, vessel segmentation, stenosis detection, and quantification. Many new AI techniques, such as machine learning and deep learning, have been widely used in medical image segmentation and stenosis detection. This review also summarizes the recent progress regarding coronary stenosis detection and quantification, and discusses the development trends in this field. Through evaluation and comparison, researchers can better understand the research frontier in related fields, compare the advantages and disadvantages of various methods, and better optimize the new technologies. Machine learning and deep learning will promote the process of automatic detection and quantification of coronary artery stenosis. However, the machine learning and the deep learning methods need a large amount of data, so they also face some challenges because of the lack of professional image annotations (manually add labels by experts).

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Customized RBF kernel graph-cut for weak boundary image segmentation

Cited by 4 publications

References 32 publications

Setting the regularization coefficient based on image energy in image segmentation using kernel graph cut algorithm

Setting the regularization coefficient based on image energy in image segmentation using kernel graph cut algorithm

Local energy-based image region segmentation using Legendre polynomials

Stenosis Detection and Quantification of Coronary Artery Using Machine Learning and Deep Learning

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