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

Abstract

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.

Improvement in the Between-Class Variance Based on Lognormal Distribution for Accurate Image Segmentation

“…The MRI brain tumor images are part of the public archives from the database BRATS2012, 2015, and the Harvard Medical School website [ 7 ]. They were applied for segmentation using Minimum Cross Entropy Thresholding (MCET) with heterogeneous mean filters [ 20 ]. We will not compare the results with that study because they use different notions.…”

“…Nonetheless, selecting the window size is required, depending on the desired object [ 18 ]. The optimal threshold of Otsu’s algorithm and some other mean-based thresholding algorithms can completely rely on the estimated mean for their objective functions [ 19 , 20 ]. As a relevant work, the Otsu method was modified using Gamma and lognormal distribution, although the usage of lognormal distribution does not satisfy the lognormal definition as it only tends to replace the mean value of lognormal with the mean of Gaussian in the Otsu method [ 3 ].…”

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

“…Furthermore, the method was extended to multi-level threshold selection through an enhanced human mental search algorithm [23]. Concerning mean estimation for objective functions, mean-based thresholding methods, including MCET, have been enhanced through the integration of homogeneous and heterogeneous mean filter approaches [9,24]. MCET has been further developed by incorporating hybrid distributions in a parallel algorithm for image segmentation, including the utilization of Gaussian and Gamma distributions for skin lesion segmentation [25].…”

“…The computational effort required to determine the value denoted as t* follows a time complexity of O (L 2 ) [9]. Nevertheless, when dealing with diverse distributions, the pursuit of an optimal threshold can prove to be a rather time-consuming endeavor.…”

“…Moreover, Computed Tomography (CT) scan images can indeed vary depending on multiple factors, including the stage and type of infection, while the proportion of infected pixels in CT images is small [8]. Images of these kinds often present similar segmentation challenges, particularly when employing mean-based thresholding techniques while adhering to the Gaussian definition for a mean estimation [9]. The performance of mean-based thresholding methods in achieving superior segmentation results can be influenced by the choice of the probability distribution.…”