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Improving Minimum Cross-Entropy Thresholding for Segmentation of Infected Foregrounds in Medical Images Based on Mean
Walaa Ali H Jumiawi1, Ali El-Zaart1
1Department of Mathematics and Computer Science, Faculty of Science, Beirut Arab University, Beirut, Lebanon.
Abstract:
Mean-based thresholding methods are among the most popular techniques that are used for images segmentation. Thresholding is a fundamental process for many applications since it provides a good degree of intensity separation of given images. Minimum cross-entropy thresholding (MCET) is one of the widely used mean-based methods for images segmentation; it is based on a classical mean that remains steady and limited value. In this paper, to improve the efficiency of MCET, dedicated mean estimation approaches are proposed to be used with MCET, instead of using the classical mean. The proposed mean estimation approaches, for example, alpha trim, harmonic, contraharmonic, and geometric, tend to exclude the negative impact of the undesired parts from the mean computation process, such as noises, local outliers, and gray intensity levels, and then provide an improvement for the thresholding process that can reflect good segmentation results. The proposed technique adds a profound impact on accurate images segmentation. It can be extended to other applications in object detection. Three data sets of medical images were applied for segmentation in this paper, including magnetic resonance imaging (MRI) Alzheimer's, MRI brain tumor, and skin lesion. The unsupervised and supervised evaluations were used to conduct the efficiency of the proposed method.
Insights
This study enhances image segmentation by improving Minimum Cross-Entropy Thresholding (MCET) with novel mean estimation methods. These new approaches reduce noise and outliers for more accurate medical image segmentation.
Area of Science:
- Computer Vision
- Medical Image Analysis
- Image Processing
Background:
- Mean-based thresholding is crucial for image segmentation, separating image intensities effectively.
- Minimum Cross-Entropy Thresholding (MCET) is a popular mean-based method but relies on a classical mean.
- Classical means can be sensitive to noise and outliers, potentially limiting segmentation accuracy.
Purpose of the Study:
- To enhance the Minimum Cross-Entropy Thresholding (MCET) method for improved image segmentation.
- To introduce and evaluate novel mean estimation approaches for MCET.
- To demonstrate the effectiveness of the proposed methods on medical imaging datasets.
Main Methods:
- Proposed alternative mean estimation techniques including alpha trim, harmonic, contraharmonic, and geometric means.
- Integrated these dedicated mean estimators with the MCET algorithm.
- Applied the enhanced MCET to segment three medical image datasets: Alzheimer's MRI, brain tumor MRI, and skin lesions.
- Evaluated segmentation performance using both unsupervised and supervised metrics.
Main Results:
- The proposed mean estimation approaches effectively mitigate the negative impact of noise and outliers in mean computation.
- The enhanced MCET method demonstrated improved accuracy in segmenting medical images compared to the classical MCET.
- Accurate segmentation results were achieved across diverse medical imaging datasets, including MRI and skin lesion images.
Conclusions:
- Dedicated mean estimation approaches significantly improve the performance of Minimum Cross-Entropy Thresholding for image segmentation.
- The proposed technique offers a robust solution for accurate medical image segmentation.
- The method shows potential for extension to other applications like object detection.
