Intracranial pressure (ICP) level estimation using textural features of brain CT images

Aghazadeh, Babak Seyed; Khaleghi, Morteza; Pidaparti, Ramana M.; Najarian, Kayvan

doi:10.1080/21681163.2013.773651

Cited by 3 publications

(2 citation statements)

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“…A combination of features extracted from Fourier analysis, Gray Level Run Length Matrix (GLRLM), Dual Tree Complex Wavelet Transform (DT-CWT), and histogram analysis have been used for ICP level classification in TBI patients [77][78][79][80][81]. It was demonstrated that the energy of different sub-band images of 2D fully anisotropic Morlet wavelet transformations could be used to determine the dominant textural orientation of the brain tissue in TBI patients and was later shown to be more competent than DT-CWT in ICP prediction [82,83].…”

Section: Intracranial Pressurementioning

confidence: 99%

AI-Based Decision Support System for Traumatic Brain Injury: A Survey

Rajaei,

Cheng,

Williamson

et al. 2023

Diagnostics

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Traumatic brain injury (TBI) is one of the major causes of disability and mortality worldwide. Rapid and precise clinical assessment and decision-making are essential to improve the outcome and the resulting complications. Due to the size and complexity of the data analyzed in TBI cases, computer-aided data processing, analysis, and decision support systems could play an important role. However, developing such systems is challenging due to the heterogeneity of symptoms, varying data quality caused by different spatio-temporal resolutions, and the inherent noise associated with image and signal acquisition. The purpose of this article is to review current advances in developing artificial intelligence-based decision support systems for the diagnosis, severity assessment, and long-term prognosis of TBI complications.

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Section: Intracranial Pressurementioning

confidence: 99%

AI-Based Decision Support System for Traumatic Brain Injury: A Survey

Rajaei,

Cheng,

Williamson

et al. 2023

Diagnostics

Self Cite

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“…Pappu et al [ 126 ] designed a novel semi-automated method that computes the ratio of CSF volume to whole intracranial volume as a measure to co-relate CT features and ICP. Aghazadeh et al [ 127 ] applied the Morlet wavelet transform to acquire textural features, and used a genetic algorithm with KNN as optimized feature selectors to label ICP as mild or severe. Qi et al [ 128 ] developed another machine learning technique that utilized multiple features along with demographic information to categorize ICP.…”

Section: Generics Of Computer Aided Diagnosismentioning

confidence: 99%

Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives

Vidhya

Gudigar

Raghavendra

et al. 2021

IJERPH

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Traumatic brain injury (TBI) occurs due to the disruption in the normal functioning of the brain by sudden external forces. The primary and secondary injuries due to TBI include intracranial hematoma (ICH), raised intracranial pressure (ICP), and midline shift (MLS), which can result in significant lifetime disabilities and death. Hence, early diagnosis of TBI is crucial to improve patient outcome. Computed tomography (CT) is the preferred modality of choice to assess the severity of TBI. However, manual visualization and inspection of hematoma and its complications from CT scans is a highly operator-dependent and time-consuming task, which can lead to an inappropriate or delayed prognosis. The development of computer aided diagnosis (CAD) systems could be helpful for accurate, early management of TBI. In this paper, a systematic review of prevailing CAD systems for the detection of hematoma, raised ICP, and MLS in non-contrast axial CT brain images is presented. We also suggest future research to enhance the performance of CAD for early and accurate TBI diagnosis.

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Tavares

2014

Computer Methods in Biomechanics and Biomedical Engineering: Im

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Intracranial pressure (ICP) level estimation using textural features of brain CT images

Cited by 3 publications

References 10 publications

AI-Based Decision Support System for Traumatic Brain Injury: A Survey

AI-Based Decision Support System for Traumatic Brain Injury: A Survey

Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives

Editorial message from the Editor-in-Chief

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