brain injury
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2022 ◽  
Vol 166 ◽  
pp. 106547
Gongxun Deng ◽  
Fang Wang ◽  
Chao Yu ◽  
Yong Peng ◽  
Hongzhen Xu ◽  

2022 ◽  
Vol 272 ◽  
pp. 117-124
Dominick Vitale ◽  
Phillip Kemp Bohan ◽  
Remealle How ◽  
Travis Arnold-Lloyd ◽  
James K Aden ◽  

Ahmad Yahya Dawod ◽  
Aniwat Phaphuangwittayakul ◽  
Salita Angkurawaranon

<span>Traumatic brain injuries are significant effects of disability and loss of life. Physicians employ computed tomography (CT) images to observe the trauma and measure its severity for diagnosis and treatment. Due to the overlap of hemorrhage and normal brain tissues, segmentation methods sometimes lead to false results. The study is more challenging to unitize the AI field to collect brain hemorrhage by involving patient datasets employing CT scans images. We propose a novel technique free-form object model for brain injury CT image segmentation based on superpixel image processing that uses CT to analyzing brain injuries, quite challenging to create a high outstanding simple linear iterative clustering (SLIC) method. The maintains a strategic distance of the segmentation image to reduced intensity boundaries. The segmentation image contains marked red hemorrhage to modify the free-form object model. The contour labelled by the red mark is the output from our free-form object model. We proposed a hybrid image segmentation approach based on the combined edge detection and dilation technique features. The approach diminishes computational costs, and the show accomplished 96.68% accuracy. The segmenting brain hemorrhage images are achieved in the clustered region to construct a free-form object model. The study also presents further directions on future research in this domain.</span>

2022 ◽  
Vol 103 ◽  
pp. 108219
Yin Yuxiong ◽  
Xiang Xujin ◽  
Tang Yi ◽  
Chen Ya ◽  
Li Yujuan ◽  

2022 ◽  
Vol 270 ◽  
pp. 495-502
Navpreet K Dhillon ◽  
Norair Adjamian ◽  
Nicole M Fierro ◽  
Geena Conde ◽  
Galinos Barmparas ◽  

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