A Selective Segmentation Model Using Dual-Level Set Functions and Local Spatial Distance

Rahman, Afzal; Ali, Haider; Badshah, Noor; Rada, Lavdie; Zakarya, Muhammad; Hussain, Hameed; Khan, Ayaz Ali; Ahmed, Aftab; Rahman, Izaz Ur; Raza, Mushtaq; Haleem, M.A.S. Abdel

doi:10.1109/access.2022.3152785

Cited by 11 publications

(11 citation statements)

References 46 publications

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“…The datasets and images used during the experimental study are publicly available in the kaggle repository, and can be accessed online at ( https://www.kaggle.com/datasets/mnavaidd/image-segmentation-dataset ). This should be noted that we measure the correctness of the suggested model through the factor of Jaccard similarity coefficient 51 and the Sørensen–Dice similarity index, as described in “ Sørensen–Dice similarity ” section. In other word, this means that we can assess and measure the similarities among the ground truth X and the obtained image Y using the Jaccard index.…”

Section: Resultsmentioning

confidence: 99%

Multi-scale-average-filter-assisted level set segmentation model with local region restoration achievements

Mabood

Badshah

Ali

et al. 2022

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Segmentation of noisy images having light in the background it is a challenging task for the existing segmentation approaches and methods. In this paper, we suggest a novel variational method for joint restoration and segmentation of noisy images which are having intensity and inhomogeneity in the existence of high contrast light in the background. The proposed model combines statistical local region information of circular regions centered at each pixel with a multi-phase segmentation technique enabling inhomogeneous image restoration. The proposed model is written in the fuzzy set framework and resolved through alternating direction minimization approach of multipliers. Through experiments, we have tested the performance of the suggested approach on diverse types of synthetic and real images in the existence of intensity and in-homogeneity; and evaluate the precision, as well as, the robustness of the suggested model. Furthermore, the outcomes are, then, compared with other state-of-the-art models including two-phase and multi-phase approaches and show that our method has superiority for images in the existence of noise and inhomogeneity. Our empirical evaluation and experiments, using real images, evaluate and assess the efficiency of the suggested model against several other closest rivals. We observed that the suggested model can precisely segment all the images having brightness, diffuse edges, high contrast light in the background, and inhomogeneity.

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Section: Resultsmentioning

confidence: 99%

Multi-scale-average-filter-assisted level set segmentation model with local region restoration achievements

Mabood

Badshah

Ali

et al. 2022

Sci Rep

Self Cite

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“…We evaluate the accuracy of the proposed model using the Jaccard similarity coefficient and Sørensen-Dice similarity index 38 . One can quantifying the similarities between the obtained image X and the ground truth Y using the Jaccard index that is mathematically defined by Eq.…”

Section: Resultsmentioning

confidence: 99%

Power mean based image segmentation in the presence of noise

Rahman

Ali

Badshah

et al. 2022

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In image segmentation and in general in image processing, noise and outliers distort contained information posing in this way a great challenge for accurate image segmentation results. To ensure a correct image segmentation in presence of noise and outliers, it is necessary to identify the outliers and isolate them during a denoising pre-processing or impose suitable constraints into a segmentation framework. In this paper, we impose suitable removing outliers constraints supported by a well-designed theory in a variational framework for accurate image segmentation. We investigate a novel approach based on the power mean function equipped with a well established theoretical base. The power mean function has the capability to distinguishes between true image pixels and outliers and, therefore, is robust against outliers. To deploy the novel image data term and to guaranteed unique segmentation results, a fuzzy-membership function is employed in the proposed energy functional. Based on qualitative and quantitative extensive analysis on various standard data sets, it has been observed that the proposed model works well in images having multi-objects with high noise and in images with intensity inhomogeneity in contrast with the latest and state-of-the-art models.

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“…In fact, each color having values of binary value. The depth of the bits per pixel in RGB colors model is 24 bits, which means every color is having an 8 bits' representation [26]. However, when we are working with the binary values, then the Most Significant Bit (MSB) and the Least Significant Bit (LSB) methods are used.…”

Section: Related Workmentioning

confidence: 99%

A Novel Steganography Technique for Digital Images Using the Least Significant Bit Substitution Method

et al. 2022

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Communication has become a lot easier in this era of technology, development of highspeed computer networks, and the inexpensive uses of Internet. Therefore, data transmission has become vulnerable to and unsafe from different external attacks. Every communication body wants to secure their data while communicating over the Internet. The internet has various benefits but the main demerit is the privacy and security and the transmission of data over insecure network or channel may happen. Various techniques used for secure communication in order to address these issues, steganography plays an important role. Steganography is the process of obfuscation that makes something incomprehensible and unclear. Different image steganography research methods are proposed recently but each has their advantages and disadvantages and still have necessity to develop some better image steganography mechanisms to achieve the reliability between the basics criteria of image steganography. Therefore, the proposed work, in this paper, is based on the Least Significant Bit (LSB) substitution method. The LSB substitution method can minimize the error rate in embedding process and can achieve greater reliability in criteria, using novel algorithm based on value difference. In this paper, we proposed a novel technique in steganography within the digital images such is RGB, Gray Scale, Texture, Aerial images to achieve higher security, imperceptibility, capacity, and robustness as compared with existing methods. The experimental outcomes of the suggested approach prove further developed strength and justify the feasibility of our research. Through numerical simulations, we observed that the proposed strategy outperformed the nextbest current methodology by 5.561 percent in terms of PSNR Correlation score. Additionally, the proposed approach achieved a 6.43 percent better score in PSNR with a variable measure of code inserted in similar images with distinct dimensions. Furthermore, encrypting the same amount of information in images of varying sizes resulted in approximately 6.77 percent improvements. Embedding different sizes of a particular secret message in a different image (such as Gray, Texture, Aerial and RGB images) came out with about 5.466 percent of better score.

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A Selective Segmentation Model Using Dual-Level Set Functions and Local Spatial Distance

Cited by 11 publications

References 46 publications

Multi-scale-average-filter-assisted level set segmentation model with local region restoration achievements

Multi-scale-average-filter-assisted level set segmentation model with local region restoration achievements

Power mean based image segmentation in the presence of noise

A Novel Steganography Technique for Digital Images Using the Least Significant Bit Substitution Method

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