A Boosted Minimum Cross Entropy Thresholding for Medical Images Segmentation Based on Heterogeneous Mean Filters Approaches

Computer vision plays an important role in the accurate foreground detection of medical images. Diagnosing diseases in their early stages has effective life-saving potential, and this is every physician's goal. There is a positive relationship between improving image segmentation methods and pr...

Full description

Saved in:
Bibliographic Details
Published in:Journal of imaging 2022-02, Vol.8 (2), p.43
Main Authors: Jumiawi, Walaa Ali H, El-Zaart, Ali
Format: Article
Language:English
Subjects:
Algorithms
Alzheimer's disease
Brain cancer
Computer vision
Efficiency
Entropy
Feature extraction
heterogeneous mean filters
Image processing
Image segmentation
images segmentation
improving minimum cross entropy thresholding
Medical imaging
Melanoma
Methods
MRI Alzheimer
MRI brain tumor
Noise
Normal distribution
Outliers (statistics)
skin lesion
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Computer vision plays an important role in the accurate foreground detection of medical images. Diagnosing diseases in their early stages has effective life-saving potential, and this is every physician's goal. There is a positive relationship between improving image segmentation methods and precise diagnosis in medical images. This relation provides a profound indication for feature extraction in a segmented image, such that an accurate separation occurs between the foreground and the background. There are many thresholding-based segmentation methods found under the pure image processing approach. Minimum cross entropy thresholding (MCET) is one of the frequently used mean-based thresholding methods for medical image segmentation. In this paper, the aim was to boost the efficiency of MCET, based on heterogeneous mean filter approaches. The proposed model estimates an optimized mean by excluding the negative influence of noise, local outliers, and gray intensity levels; thus, obtaining new mean values for the MCET's objective function. The proposed model was examined compared to the original and related methods, using three types of medical image dataset. It was able to show accurate results based on the performance measures, using the benchmark of unsupervised and supervised evaluation.
ISSN:2313-433X
2313-433X
DOI:10.3390/jimaging8020043