Brain MRI/CT Images Feature Extraction to Enhance Abnormalities Quantification

Snehkunj, Rupal; Jani, Ashish; Jani, Nalin N.

doi:10.17485/ijst/2018/v11i1/120361

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…To address this issue, some researchers have focused on increasing the speed of these methods while maintaining their accuracy. However, these methods still had their limitations, such as the need for parameter tuning [18].…”

Section: Fuzzy Clustering In Brain Mri Segmentationmentioning

confidence: 99%

A Systematic Review of Brain MRI Segmentation and Uncertainty Modeling Using Evidence Theory with Implementation of Fuzzy Clustering and Fuzzy Inference Systems Methods

Sedeeq,

Aljobouri,

Hasan

2023

RIA

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Image segmentation, the process of partitioning an image into its constituent parts, is a pivotal step in image processing, particularly with respect to brain MRI images. This operation's complexity is magnified due to inherent uncertainties, which may arise from factors such as noise and intensity non-uniformity. In this study, a systematic review of both non-fuzzy and fuzzy medical image segmentation methods, with a focus on brain MRI images, was undertaken. Practical application of fuzzy clustering and fuzzy inference systems were demonstrated using freely accessible simulated data. This paper presents an emphasis on uncertainty modeling techniques, highlighting the potential of belief structure integration as a significant approach for future hybrid medical image segmentation techniques. The fusion of diverse information sources through this combination is posited to enhance both the accuracy and robustness of uncertainty handling, critical aspects in medical image analysis.

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Section: Fuzzy Clustering In Brain Mri Segmentationmentioning

confidence: 99%

A Systematic Review of Brain MRI Segmentation and Uncertainty Modeling Using Evidence Theory with Implementation of Fuzzy Clustering and Fuzzy Inference Systems Methods

Sedeeq,

Aljobouri,

Hasan

2023

RIA

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“…In computed tomography, for example, of the abdominal cavity, it is difficult to separate the tumor from the organ, as well as to calculate its volume characteristics [2][3][4] To make the right decision about surgery on a tumor, it is useful to have a clear 3D model [5][6][7]. However, modern software for processing images obtained during CT and MRI is developed and delivered with the devices, but the functions of these programs are not enough to perform complex operations with the obtained data [8][9][10]. Creating 3D models of organs using these programs is possible and difficult, but it requires a lot of time and system resources of computers directly connected to the tomograph.…”

Section: Introductionmentioning

confidence: 99%

Prospects for Creating 3D Models of Internal Organs Based on Computer and Magnetic Resonance Imaging Images in Emergency Surgery and Resuscitation

Mezhidov¹,

Belyaeva²,

S-M.Bimarzaev³

et al. 2021

Pharmacophore

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The present article gives an analytical overview of the current state of processing problems and visualization of magnetic resonance imaging (MRI). It is presented the expediency of combining of instrumental means, presented in various open-source programs and scientific publications in recent years, into a single program-tool complex solving in full problems of pre-processing and filtering data, directed precision thematic segmentation and analysis of images, forming the feature space, recognition of the situation, 3D modeling, and visualization. The complex is designed for expert decision support of doctors-researchers in the process of carrying out scientific experiments. Support is based on the application of new methods of brain area texture analysis, effective metrics, and advanced means of cognitive computer graphics. An important feature of the complex must be the interactive operating mode of the doctor-researcher with MRI data, which can be provided by applying high-performance computing with software support of hardware accelerators.

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Novel Discrete Component Wavelet Transform for detection of cerebrovascular diseases

Pal

Jain

2022

Sādhanā

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Brain MRI/CT Images Feature Extraction to Enhance Abnormalities Quantification

Cited by 6 publications

References 4 publications

A Systematic Review of Brain MRI Segmentation and Uncertainty Modeling Using Evidence Theory with Implementation of Fuzzy Clustering and Fuzzy Inference Systems Methods

A Systematic Review of Brain MRI Segmentation and Uncertainty Modeling Using Evidence Theory with Implementation of Fuzzy Clustering and Fuzzy Inference Systems Methods

Prospects for Creating 3D Models of Internal Organs Based on Computer and Magnetic Resonance Imaging Images in Emergency Surgery and Resuscitation

Novel Discrete Component Wavelet Transform for detection of cerebrovascular diseases

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