A dualistic sub-image histogram equalization based enhancement and segmentation techniques for medical images

Mohan, K. Raj; Thirugnanam, G.

doi:10.1109/iciip.2013.6707655

Cited by 16 publications

(6 citation statements)

References 8 publications

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“…A sub-image DSIHE approach improves the image quality. 33,34 DSIHE has a significant benefit compared to conventional histogram equalization since it yields better results for images with both bright and dark sections. To do this, histogram equalization is applied to each subpicture independently.…”

Section: Dualistic Sub-image Histogram Equalizationmentioning

confidence: 99%

Enhancing explainability in brain tumor detection: A novel DeepEBTDNet model with LIME on MRI images

Ullah,

Hassan,

Khan

et al. 2023

Int J Imaging Syst Tech

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Early detection of brain tumors is vital for improving patient survival rates, yet the manual analysis of the extensive 3D MRI images can be error‐prone and time‐consuming. This study introduces the Deep Explainable Brain Tumor Deep Network (DeepEBTDNet), a novel deep learning model for binary classification of brain MRIs as tumorous or normal. Employing sub‐image dualistic histogram equalization (DSIHE) for enhanced image quality, DeepEBTDNet utilizes 12 convolutional layers with leaky ReLU (LReLU) activation for feature extraction, followed by a fully connected classification layer. Transparency and interpretability are emphasized through the application of the Local Interpretable Model‐Agnostic Explanations (LIME) method to explain model predictions. Results demonstrate DeepEBTDNet's efficacy in brain tumor detection, even across datasets, achieving a validation accuracy of 98.96% and testing accuracy of 94.0%. This study underscores the importance of explainable AI in healthcare, facilitating precise diagnoses and transparent decision‐making for early brain tumor identification and improved patient outcomes.

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Section: Dualistic Sub-image Histogram Equalizationmentioning

confidence: 99%

Enhancing explainability in brain tumor detection: A novel DeepEBTDNet model with LIME on MRI images

Ullah,

Hassan,

Khan

et al. 2023

Int J Imaging Syst Tech

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“…A type of BBHE that sorts images into groups based on median values is called DSIHE. A method has been proposed to enhance and segment medical images based on DSIHE [7]. As described in [13], the proposed algorithm includes the following series of steps:…”

Section: Dualistic Sub Image Histogram Equalization (Dsihe)mentioning

confidence: 99%

Fusion based Image Enhancement Approach for Brain Tumor Detection

Deepa Abin

2023

IJRITCC

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Magnetic Resonance Imaging (MRI), is a crucial technology used in the processing of medical images that provides insights into the anatomy of soft organs in the human body and helps in detecting brain tumors and spinal tumors. Despite advances in technology, most images have intrinsic drawbacks such as reduced contrast and brightness, and noise. Several contrast enhancement techniques are used such as, HE, BBHE, DSIHE, CLAHE, RMSHE, and their fusion, have been deployed on different MRI images to handle these problems. Metrics such as, entropy, PIQE and BRISQUE are used in the assessment of the results. Through the different fusion combinations, most prominent results are obtained from CLAHE-RMSHE fusion with an entropy value of 6.2516 and BRISQUE value of 40.14.

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“…Finally, all subimages are merged into a single image on the global grayscale histogram. The improved algorithms include brightness-preserving bi-HE (BBHE) [13,14], recursive mean-separate HE (RMSHE) [15][16][17], dualistic subimage HE (DSIHE) [18,19], minimum mean brightness error bi-HE (MMBEBHE) [20,21], and weighting meanseparated sub-HE (WMSHE) [22,23].…”

Section: Introductionmentioning

confidence: 99%

Contrast Enhancement Method Based on Gray and Its Distance Double-Weighting Histogram Equalization for 3D CT Images of PCBs

Zeng

Yan

Wang³

2016

Mathematical Problems in Engineering

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Cone beam computed tomography (CBCT) is a new detection method for 3D nondestructive testing of printed circuit boards (PCBs). However, the obtained 3D image of PCBs exhibits low contrast because of several factors, such as the occurrence of metal artifacts and beam hardening, during the process of CBCT imaging. Histogram equalization (HE) algorithms cannot effectively extend the gray difference between a substrate and a metal in 3D CT images of PCBs, and the reinforcing effects are insignificant. To address this shortcoming, this study proposes an image enhancement algorithm based on gray and its distance double-weighting HE. Considering the characteristics of 3D CT images of PCBs, the proposed algorithm uses gray and its distance double-weighting strategy to change the form of the original image histogram distribution, suppresses the grayscale of a nonmetallic substrate, and expands the grayscale of wires and other metals. The proposed algorithm also enhances the gray difference between a substrate and a metal and highlights metallic materials. The proposed algorithm can enhance the gray value of wires and other metals in 3D CT images of PCBs. It applies enhancement strategies of changing gray and its distance double-weighting mechanism to adapt to this particular purpose. The flexibility and advantages of the proposed algorithm are confirmed by analyses and experimental results.

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A dualistic sub-image histogram equalization based enhancement and segmentation techniques for medical images

Cited by 16 publications

References 8 publications

Enhancing explainability in brain tumor detection: A novel DeepEBTDNet model with LIME on MRI images

Enhancing explainability in brain tumor detection: A novel DeepEBTDNet model with LIME on MRI images

Fusion based Image Enhancement Approach for Brain Tumor Detection

Contrast Enhancement Method Based on Gray and Its Distance Double-Weighting Histogram Equalization for 3D CT Images of PCBs

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