Otsu Thresholding Model Using Heterogeneous Mean Filters for Precise Images Segmentation

Jumiawi, Walaa Ali H.; El-Zaart, Ali

doi:10.1109/icateee57445.2022.10093097

Cited by 2 publications

(1 citation statement)

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“…Thresholdingbased image segmentation is a process that tends to separate intensity levels into two regions in a given image. Some thresholding methods, e.g., the Otsu method and Minimum Cross-Entropy Thresholding, are considered mean-based techniques [2][3][4], and the original versions of these methods rely on the definition of Gaussian distribution for the mean estimation for their objective functions; this approach deals with images as they have symmetric intensity levels.…”

Section: Introductionmentioning

confidence: 99%

Gumbel (EVI)-Based Minimum Cross-Entropy Thresholding for the Segmentation of Images with Skewed Histograms

Jumiawi,

El-Zaart

2023

ASI

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In this study, we delve into the realm of image segmentation, a field characterized by a multitude of approaches; one frequently used technique is thresholding-based image segmentation. This process divides intensity levels into different regions based on a specified threshold value. Minimum Cross-Entropy Thresholding (MCET) stands out as an independent objective function that can be applied with any distribution and is regarded as a mean-based thresholding method. In certain cases, images exhibit diverse structures that result in different histogram distributions. Some images possess symmetric histograms, while others feature asymmetric ones. Traditional mean-based thresholding methods are well-suited for symmetric image histograms, relying on Gaussian distribution definitions for mean estimations. However, in situations involving asymmetric distributions, such as left and right-skewed histograms, a different approach is required. In this paper, we propose the utilization of a Maximum Likelihood Estimation (MLE) of Gumbel’s distribution or Extreme Value Type I (EVI) distribution for the objective function of an MCET. Our goal is to introduce a dedicated image-thresholding model designed to enhance the accuracy and efficiency of image-segmentation tasks. This model determines optimal thresholds for image segmentation, facilitating precise data analysis for specific image types and yielding improved segmentation results by considering the impact of mean values on thresholding objective functions. We compare our proposed model with original methods and related studies in the literature. Our model demonstrates better performance in terms of segmentation accuracy, as assessed through both unsupervised and supervised evaluations for image segmentation.

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Section: Introductionmentioning

confidence: 99%