HCET-G<sup>2</sup>: Dermoscopic Skin Lesion Segmentation via Hybrid Cross Entropy Thresholding using Gaussian and Gamma Distributions

Rawas, Soha; El-Zaart, Ali

doi:10.1109/icds47004.2019.8942339

Cited by 4 publications

(14 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Minimum cross-entropy-based thresholding (MCET) describes the optimum threshold by minimizing the variance between two class entropies [ 22 ]; thus, the result of optimal threshold t ∗ is as follows:

where D ( I , I t ) can be written as D ( t ) and is determined as follows:

where h ( i ) refers to the histogram of the gray level i for the range [1, L ] and μ 1 ( t ) and μ 2 ( t ) are the mean values of the first and the second region, respectively. Since the first term is a constant for a given image [ 8 ]; thus, the objective function can be written as follows:

Therefore, this function can be rewritten as follows:

where C 1 ( t ) and C 2 ( t ) are as follows:

…”

Section: Related Workmentioning

confidence: 99%

“…Also, it was proposed for color image segmentation based on exchange market algorithm [ 25 ]. MCET also improved by using gamma, Gaussian distribution in [ 8 ]. Moreover, it was developed using hybrid distribution including Gaussian and lognormal [ 6 ].…”

Section: Related Workmentioning

confidence: 99%

“…Computer-aided diagnosis (CAD) systems have significant potential for diagnosing fatal diseases. Segmentation of infected foreground in medical images has an important role in detecting malignant disease, or vice versa [ 8 ]. Alzheimer's disease (AD) is a gradual neurologic disease in the brain that produces irreversible loss of neurons.…”

Section: Introductionmentioning

confidence: 99%

“…Image segmentation is an important technique that detects infected skin lesions and separates the lesion from the skin region. To provide reliable diagnosis, CAD systems tend to observe the early symmetric shape [ 8 , 14 ]. Moreover, MRI brain tumor segmentation is a hard task for many reasons, such as the non-homogeneous intensities around the tumor, the presence of noises in the background, the complicated shapes and the fuzzy boundaries, and less contrast between neighboring brain tissues [ 15 – 17 ].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improving Minimum Cross‐Entropy Thresholding for Segmentation of Infected Foregrounds in Medical Images Based on Mean Filters Approaches

Jumiawi

El-Zaart

2022

Contrast Media & Molecular Imaging

Self Cite

View full text Add to dashboard Cite

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.

show abstract

where D ( I , I t ) can be written as D ( t ) and is determined as follows:

Therefore, this function can be rewritten as follows:

where C 1 ( t ) and C 2 ( t ) are as follows:

…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving Minimum Cross‐Entropy Thresholding for Segmentation of Infected Foregrounds in Medical Images Based on Mean Filters Approaches

Jumiawi

El-Zaart

2022

Contrast Media & Molecular Imaging

Self Cite

View full text Add to dashboard Cite

show abstract

“…Due to the complexity surrounding the acquisition of lesion images, digital images can have undesirable quality. Gamma correction has been applied in this study to obtain the desired contrast features for a given image and assist in enhancing lesion thresholding [62,63].…”

Section: Gamma Correctionmentioning

confidence: 99%

Segmentation of Melanocytic Lesion Images Using Gamma Correction with Clustering of Keypoint Descriptors

Okuboyejo

Olugbara

2021

Diagnostics

View full text Add to dashboard Cite

The early detection of skin cancer, especially through the examination of lesions with malignant characteristics, has been reported to significantly decrease the potential fatalities. Segmentation of the regions that contain the actual lesions is one of the most widely used steps for achieving an automated diagnostic process of skin lesions. However, accurate segmentation of skin lesions has proven to be a challenging task in medical imaging because of the intrinsic factors such as the existence of undesirable artifacts and the complexity surrounding the seamless acquisition of lesion images. In this paper, we have introduced a novel algorithm based on gamma correction with clustering of keypoint descriptors for accurate segmentation of lesion areas in dermoscopy images. The algorithm was tested on dermoscopy images acquired from the publicly available dataset of Pedro Hispano hospital to achieve compelling equidistant sensitivity, specificity, and accuracy scores of 87.29%, 99.54%, and 96.02%, respectively. Moreover, the validation of the algorithm on a subset of heavily noised skin lesion images collected from the public dataset of International Skin Imaging Collaboration has yielded the equidistant sensitivity, specificity, and accuracy scores of 80.59%, 100.00%, and 94.98%, respectively. The performance results are propitious when compared to those obtained with existing modern algorithms using the same standard benchmark datasets and performance evaluation indices.

show abstract

Next-gen breast cancer diagnosis: iembc as an iomt-enabled cloud computing solution

Rawas,

Tafran

2024

Multimed Tools Appl

View full text Add to dashboard Cite

HCET-G²: Dermoscopic Skin Lesion Segmentation via Hybrid Cross Entropy Thresholding using Gaussian and Gamma Distributions

Cited by 4 publications

References 14 publications

Improving Minimum Cross‐Entropy Thresholding for Segmentation of Infected Foregrounds in Medical Images Based on Mean Filters Approaches

Improving Minimum Cross‐Entropy Thresholding for Segmentation of Infected Foregrounds in Medical Images Based on Mean Filters Approaches

Segmentation of Melanocytic Lesion Images Using Gamma Correction with Clustering of Keypoint Descriptors

Next-gen breast cancer diagnosis: iembc as an iomt-enabled cloud computing solution

Contact Info

Product

Resources

About

HCET-G2: Dermoscopic Skin Lesion Segmentation via Hybrid Cross Entropy Thresholding using Gaussian and Gamma Distributions

Cited by 4 publications

References 14 publications

Improving Minimum Cross‐Entropy Thresholding for Segmentation of Infected Foregrounds in Medical Images Based on Mean Filters Approaches

Improving Minimum Cross‐Entropy Thresholding for Segmentation of Infected Foregrounds in Medical Images Based on Mean Filters Approaches

Segmentation of Melanocytic Lesion Images Using Gamma Correction with Clustering of Keypoint Descriptors

Next-gen breast cancer diagnosis: iembc as an iomt-enabled cloud computing solution

Contact Info

Product

Resources

About

HCET-G²: Dermoscopic Skin Lesion Segmentation via Hybrid Cross Entropy Thresholding using Gaussian and Gamma Distributions