Brain Tumor Diagnosis in MRI Images Using Image Processing Techniques and Pixel-Based Clustering

Katouli, Moosa; Rahmani, Akram Isavand

doi:10.18280/ts.370215

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…In Table 4, the SSIM value is higher than the other methods and is approaching 0.99. The proposed method's average SSIM value indicates improved enhancement outcomes of brain tumor MR images [57].…”

Section: Structural Similarity Index Metric (Ssim)mentioning

confidence: 94%

An Approach for Enhancement of MR Images of Brain Tumor

Mishra¹,

Kaur²

2022

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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.

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Section: Structural Similarity Index Metric (Ssim)mentioning

confidence: 94%

An Approach for Enhancement of MR Images of Brain Tumor

Mishra¹,

Kaur²

2022

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“…This edge finding method maintains the characteristics of the directional interpolation magnification algorithm, improves the efficiency of the algorithm, and also has good flexibility, i.e. :the threshold of the binarisation can be adjusted according to the image brightness distribution during the magnification operation to obtain a more accurate delineation of the image edges [15][16].…”

Section: Image Processing Techniquesmentioning

confidence: 99%

The Design of Art Design System Based on Image Processing Technology

2023

IJAID

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Nowadays, the design of digital image processing technology is developing in the direction of art and personalisation. In line with this development trend, it is necessary to combine computer art design to complete the art design system. The aim of this paper is to study the design of an art design system based on image processing technology. The interactivity of art and image processing technology are analysed, and a synthetic image display algorithm is proposed for application in the art design system. The system development tools and the architecture of the system are presented. We conduct environmental art design experiments, and the experiments yield good results.

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“…The accurate and prompt diagnosis will usually provide better intervention options for the treatment and increase the survival rate of the patients ( Abd-Ellah et al, 2019 ). Many researches were published regarding brain tumor diagnosis during various image processing techniques to improve tumor identification and precise location determination ( Katouli and Rahmani, 2020 , Alqudah and Al-Sharu, 2020 , Gulo et al, 2019 , Naceur et al, 2018 , Pandey et al, 2016 ). However, the challenge of accurate diagnosis still exists because various tumors have different patterns.…”

Section: Introductionmentioning

confidence: 99%

Recognition of brain tumors in MRI images using texture analysis

Elshaikh

Garelnabi

Omer

et al. 2021

Saudi Journal of Biological Sciences

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Objectives Brain neoplasms or intracranial tumors, which are more common in older adults, can affect individuals of any age including pediatric and children. Exposure to carcinogenic agents including ionizing radiation and family history is one of the main causes of the disease. Early diagnosis is crucial to avoid prolonged. patients' suffering. The aim of the study was to efficiently recognize the brain tumors from the other brain tissues which include grey and white matter as well as cerebrospinal fluid (CSF). Materials and methods This study was performed using axial, sagittal and coronal views for fifty brain tumor patients randomly selected from a set of 200 patients, with a “control” set consisting of images showing no sign of disease; and the “test” brain MRI images for patients diagnosed with brain tumor. The study includes both genders with age ranging from 18 years to 83 years old, (56.5 ± 17.2). The brain images were acquired using a standard head coil Philips Intera 1.5 Tesla machine (USA). The thickness of each section in the entire sequence was 8 mm. Acquisition of T2-weighted and T1-weighted were performed. Interactive Data Language software was used to analyze the data. Results The results of this study showed that: the overall accuracy of classification process was 94.8%, and for the tumor; the sensitivity was 97.3%. White matter and grey matter showed a classification accuracy of 95.7% and 89.7% and for CSF the accuracy was 94.3%. Conclusion The results showed that brain tumor can be classified successfully and delineated using texture analysis with minimum efforts and with high accuracy for brain tumors.

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Brain Tumor Diagnosis in MRI Images Using Image Processing Techniques and Pixel-Based Clustering

Cited by 6 publications

References 17 publications

An Approach for Enhancement of MR Images of Brain Tumor

An Approach for Enhancement of MR Images of Brain Tumor

The Design of Art Design System Based on Image Processing Technology

Recognition of brain tumors in MRI images using texture analysis

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