Brain hematoma computational segmentation

Sáenz, Frank; Vera, Miguel; Huérfano, Yoleidy; Molina, Valentín; Martínez, Luis Javier; Vera, Miguel; Salazar, Williams; Gelvez, Elkin; Salazar, Juan; Valbuena, Oscar; Robles, Horderlin Vrangel; Bautista, Miguel Ángel; Arango, Jaime

doi:10.1088/1742-6596/1126/1/012071

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…Pre-processing is employed to remove the irrelevant information in the brain CT images, such as from the skull, head holder, soft tissue edema, and background, which can significantly introduce noise and degrade the performance of the CAD system. Techniques, such as intensity based thresholding, morphological operation, and connected component analysis, are used individually or combined to obtain the enhanced CT images [23,44,[49][50][51]. Commonly used noise reduction techniques include median filtering and gradient magnitude filtering [23,44,50,51].…”

Section: Pre-processingmentioning

confidence: 99%

“…Techniques, such as intensity based thresholding, morphological operation, and connected component analysis, are used individually or combined to obtain the enhanced CT images [23,44,[49][50][51]. Commonly used noise reduction techniques include median filtering and gradient magnitude filtering [23,44,50,51]. Contrast limited adaptive histogram equalisation (CLAHE) is also utilised as a technique for enhancing image quality [51].…”

Section: Pre-processingmentioning

confidence: 99%

“…Gautam and Raman [53] evaluated the performance of a segmentation model based on white matter FCM clustering (WMFCM) and wavelet-based thresholding. Saenz et al [50] reported a nonlinear technique that uses region growing to segment hematoma in 3D CT datasets. Bhadauria et al [55] showed that a combination of FCM clustering and active contour modelling can lead to effective demarcation of hematoma, with an accuracy of 99.10%.…”

Section: Segmentationmentioning

confidence: 99%

“…Scherer et al [68] quantified the hematoma volume by summing the volume of individual voxels present in the segmented ICH. Saenz et al [50] estimated the 3D volume of three different types of hematoma by considering the voxel size and the number of voxels present in the segmented hematoma regions. Sun and Sun [49] constructed Gengon and truncated pyramid approximation models to calculate the 3D volume of hematoma in a single patient.…”

Section: Hematoma Volume Estimationmentioning

confidence: 99%

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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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Section: Pre-processingmentioning

confidence: 99%

Section: Pre-processingmentioning

confidence: 99%

Section: Segmentationmentioning

confidence: 99%

Section: Hematoma Volume Estimationmentioning

confidence: 99%

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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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Volumetric quantification in ovarian pathology using abdomino-pelvic computed tomography

Valbuena

Vera

Gelvez-Almeida

et al. 2019

J. Phys.: Conf. Ser.

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Pathological ovary is categorized into cystic tumors, solid tumors and mixed, according to the content of the affected ovary. Accordingly, the degree of benignity or malignity thereof is established. The imaging study for the preliminary morphological assessment of PO is ultrasound, in its pelvic and transvaginal modalities, for which well-established criteria are available. Once the ultrasound findings suggest malignancy, complementary studies such as abdominal-pelvic tomography images and tumor markers are requested. This type of images has challenging problems called noise, artifacts and low contrast. In this paper, in order to address these problems, a computational technique is proposed to characterize a pathological ovary. To do this, a thresholding and the median and gradient magnitude filters are applied, preliminarily, to complete the preprocessing stage. Then, during the segmentation, the algorithm of region growing is used to extract the three-dimensional morphology of the pathological ovary. Using this morphology, the volume of the pathological ovary is calculated and it allows selecting the surgical-medical behavior to approach this kind of ovary. The validation of the proposed technique indicates that the results are promising. This technique can be useful in the detection and monitoring the diseases linked to pathological ovary.

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Semi-automatic detection of hepatic tumor in computed tomography images

Sáenz

Vera

López

et al. 2019

J. Phys.: Conf. Ser.

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In this work, the main purpose is develop a computational segmentation strategy for liver tumor semiautomatic detection. This strategy considers three-dimensional computed tomography images and it consists of techniques application that, on the one hand, diminish the noise and detect the edges of the objects present in those images and, on the other hand, generate the liver tumor morphology. For this, the sequence of techniques composed of gaussian smoothing, gradient magnitude, median filter, region growing and binary morphological dilation are used. The value obtained, for the metric called Dice score, show a good correlation between manual segmentation, performed by a hepatologist, and the tumor segmentation obtained using the proposed technique. This type of segmentation is the extreme utility for the characterization of hepatic tumors and the planning of the clinical behavior to be followed in the treatment of this human liver disease.

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Brain hematoma computational segmentation

Cited by 7 publications

References 7 publications

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

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

Volumetric quantification in ovarian pathology using abdomino-pelvic computed tomography

Semi-automatic detection of hepatic tumor in computed tomography images

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