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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]