MRI Medical Images Enhancement based on Histogram Equalization and Adaptive Histogram Equalization

Jbara, Wurood A.; Jaafar, Rafah Amer

doi:10.14445/22312803/ijctt-v50p116

Cited by 4 publications

(1 citation statement)

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“…The main target of histogram alignment is to obtain histogram equalization so that the degree of gray has a relatively equal number of pixels. The histogram alignment equation can be seen in equation 5 [17], [18].…”

Section: Image Repairmentioning

confidence: 99%

Improvement of Hybrid Image Enhancement for Detection and Classification of Malaria Disease Types and Stages with Artificial Intelligence

Rosnelly¹,

Riza²,

Wahyuni³

et al. 2022

TEM Journal

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Malaria is an infectious disease throughout the world where the disease is transmitted by infected female Anopheles mosquitoes. Malaria has some symptoms that are almost like COVID-19. Malaria has several other symptoms, characterized by chills, anemia, cold sweats, nausea and vomiting, and a sudden drop in blood pressure. Identification of the type of malaria begins with preprocessing, feature extraction, and classification for identification. Image improvement is part of the preprocessing stage to improve image quality so that the malaria parasite object in the image can be seen clearly. This study tries to improve the algorithm with hybrid dark and contrast stretching. Performance evaluation of malaria parasite image improvement using Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR). The results obtained with the improvement of dark hybrids and contrast stretching can improve the image quality of malaria parasite objects with MSE value = 0.0095 and PSNR value = 22.8404, compared with dark stretching, contrast stretching, histogram equalization.

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Section: Image Repairmentioning

confidence: 99%

Improvement of Hybrid Image Enhancement for Detection and Classification of Malaria Disease Types and Stages with Artificial Intelligence

Rosnelly¹,

Riza²,

Wahyuni³

et al. 2022

TEM Journal

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A Comprehensive Review of Brain Tumour Detection Mechanisms

Ramtekkar

Pandey

Pawar

2023

The Computer Journal

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The brain is regarded as the central part of the human body and has a very complicated structure. The abnormal growth of tissue inside the brain is called a brain tumour. Tumour detection at an early stage is the most difficult task in the discipline of health. In this review article, the authors have deeply analysed and reviewed the brain tumour detection mechanisms which include manual, semi- and fully automated techniques. Today, fully automated mechanisms apply deep learning (DL) methods for tumour detection in brain magnetic resonance images (MRIs). This paper deals with previously published research articles relevant to various brain tumour detection techniques. Review of various types of tumours, MRI modalities, datasets, filters, segmentation methods and DL techniques like long short-term memory, gated recurrent unit network, convolution neural network, auto encoder, deep belief network, recurrent neural network, generative adverse network and deep stacking networks have been included in this paper. It has been observed from the analysis that the use of DL techniques in the detection of brain tumours improves accuracy. Finally, this paper reveals research gaps, limitations of existing methods, challenges in tumour detection and contributions of the proposed article.

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Contrast Enhancement of MRI Images using Histogram Equalization Techniques

Kalyani

Chakraborty

2020

2020 International Conference on Computer, Electrical &Amp; Communication Engineering (ICCECE)

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MRI Medical Images Enhancement based on Histogram Equalization and Adaptive Histogram Equalization

Cited by 4 publications

References 0 publications

Improvement of Hybrid Image Enhancement for Detection and Classification of Malaria Disease Types and Stages with Artificial Intelligence

Improvement of Hybrid Image Enhancement for Detection and Classification of Malaria Disease Types and Stages with Artificial Intelligence

A Comprehensive Review of Brain Tumour Detection Mechanisms

Contrast Enhancement of MRI Images using Histogram Equalization Techniques

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