Brightness preserving optimized weighted bi‐histogram equalization algorithm and its application to MR brain image segmentation

Veluchamy, Magudeeswaran; Mayathevar, Krishnamurthy; Subramani, Bharath

doi:10.1002/ima.22330

Cited by 14 publications

(9 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Contrast Improvement Index (CII) is an objective measurement index for the extent of contrast enhancement and it is calculated based on the local contrast of the original image and the enhanced image 25 . CII of an image is calculated by:

C ((), I) = \sum_{u, v = 0}^{L - 1} δ {(u, v)}^{2} P_{δ} ((), u, v)

italicCII = \frac{C ((), I^{'})}{C ((), I)}

where C ( I ′ ) is the local contrast of the enhanced image and C ( I ) is the local contrast of the original one.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy inference based contextual dissimilarity histogram equalization algorithm for image enhancement

Cheng

et al. 2020

Int J Imaging Syst Tech

View full text Add to dashboard Cite

In order to overcome the drawback of the existing image enhancement technologies and further consider the pixel intensity expression error caused by imaging, a novel fuzzy inference‐based contextual dissimilarity histogram equalization (FICDHE) algorithm is proposed. The proposed algorithm is composed of three modules. In the first module, according to the calculated probable intensity intervals, the membership functions of intensity are generated. In the second one, fuzzy inference systems are established and the contextual dissimilarity of each pixel is calculated. In the third module, the contextual dissimilarity histograms are clipped and equalized. The fuzzy system established in this paper not only fully considers the uncertainty source of pixel gray level expression, but also has adaptability. The parameter selection of fuzzy inference system membership function in this algorithm does not need human intervention, but is automatically obtained based on the statistical information of image pixel gray. Its adaptability makes the algorithm more widely used and convenient. Experiments are conducted using four typical medical images and 800 images from the KNIX dataset and BrainWeb dataset. The performance of the proposed method was compared to a series of enhancement algorithms based on both subjective judgment and image quality measurement indexes. Experimental results demonstrate that the proposed algorithm has a better contrast enhancement ability and yields better performance.

show abstract

C ((), I) = \sum_{u, v = 0}^{L - 1} δ {(u, v)}^{2} P_{δ} ((), u, v)

italicCII = \frac{C ((), I^{'})}{C ((), I)}

where C ( I ′ ) is the local contrast of the enhanced image and C ( I ) is the local contrast of the original one.…”

Section: Methodsmentioning

confidence: 99%

Section: Image Quality Assessmentmentioning

confidence: 99%

Fuzzy inference based contextual dissimilarity histogram equalization algorithm for image enhancement

Cheng

et al. 2020

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…It is the ratio between the maximum possible power of an image and the power of corrupting noise [52]. It was previously used to determine image reconstruction quality [24], [53]. Mathematically it is expressed as:…”

Section: Mean Square Error (Mse)mentioning

confidence: 99%

An Approach for Enhancement of MR Images of Brain Tumor

Mishra¹,

Kaur²

2022

View full text Add to dashboard Cite

Magnetic Resonance Imaging plays an important role in diagnosing the brain tumor accurately, but it requires the approach to enhance the magnetic resonance images to assist physicians in brain tumor detection and making the treatment plan precisely to reduce the mortality rate. Therefore, in this proposed work, a comprehensive learning-based elephant herding optimization technique has been introduced to select the optimal value of smoothness factor in Bi-Histogram Equalization with Adaptive Sigmoid Function that enhances the visual quality as well as the appearance of the suspicious regions in magnetic resonance images. Further, the enhancement performance has been evaluated by the enhancement quality metrics. The metrics used include mean square error, peak signal to noise ratio, mean absolute error, structural similarity index metric, feature similarity index metric, Riesz transformed based feature similarity index metric, spectral residual-based similarity index metric, and absolute mean brightness error. The outcomes of this proposed work have a remarkable impact on enhancing magnetic resonance images and providing visual assistance for diagnosing brain tumors. The performance of the evaluation metrics is verified with Friedman's mean rank test, which strongly indicates a statistical difference between the proposed method and state-of-the-art methods.

show abstract

“…Legal cases contain a large number of circumstances and circumstances, and each of these circumstances or circumstances will belong to a category of text classification. Model node management is a structure composed of key words such as cause of action, situation, and circumstance in law as nodes [17]. This structure makes it easy not only to manage categories but also to understand the relationships between categories of data.…”

Section: Image Segmentation Algorithm Text Classificationmentioning

confidence: 99%

Application of Image Segmentation Algorithm Based on Partial Differential Equation in Legal Case Text Classification

Sun

2021

Advances in Mathematical Physics

View full text Add to dashboard Cite

As a means of regulating people’s code of conduct, law has a close relationship with text, and text data has been growing exponentially. Managing and classifying huge text data have become a huge challenge. The PDES image segmentation algorithm is an effective natural language processing method for text classification management. Based on the study of image segmentation algorithm and legal case text classification theory, an image segmentation model based on partial differential equation is proposed, in which diffusion indirectly acts on level set function through auxiliary function. The software architecture of image segmentation algorithm text classification system is proposed by using computer technology and three-layer architecture model, which can improve the classification ability of text classification algorithm. The validity of pDE image segmentation model is verified by experiments. The experimental results show that the model completes the legal case text classification, the performance of each functional module of the legal case text classification system is good, and the efficiency and quality of the legal case text classification are improved.

show abstract

Brightness preserving optimized weighted bi‐histogram equalization algorithm and its application to MR brain image segmentation

Cited by 14 publications

References 27 publications

Fuzzy inference based contextual dissimilarity histogram equalization algorithm for image enhancement

Fuzzy inference based contextual dissimilarity histogram equalization algorithm for image enhancement

An Approach for Enhancement of MR Images of Brain Tumor

Application of Image Segmentation Algorithm Based on Partial Differential Equation in Legal Case Text Classification

Contact Info

Product

Resources

About