Evaluation of Window Parameters of CT Brain Images With Statistical Central Moments

Sim, K. S.; Nia, M. E.; Ta, Calinog; Tso, C.P.; Kho, T.K.; Ee, Chung

doi:10.1016/b978-0-12-804203-8.00032-8

Cited by 1 publication

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, this procedure is not always applicable to the images of the early stage. Sim et al [ 40 ] applies the central moment, mean, variance, kurtosis, and skewness to find a suitable setting. Lailatul Mugniroq [ 41 ] experiments with the different window size in the framework of detecting the subacute ischemic stroke.…”

Section: Previous Workmentioning

confidence: 99%

Artificial intelligence for localization of the acute ischemic stroke by non-contrast computed tomography

et al. 2022

View full text Add to dashboard Cite

A non-contrast cranial computer tomography (ncCT) is often employed for the diagnosis of the early stage of the ischemic stroke. However, the number of false negatives is high. More accurate results are obtained by an MRI. However, the MRI is not available in every hospital. Moreover, even if it is available in the clinic for the routine tests, emergency often does not have it. Therefore, this paper proposes an end-to-end framework for detection and segmentation of the brain infarct on the ncCT. The computer tomography perfusion (CTp) is used as the ground truth. The proposed ensemble model employs three deep convolution neural networks (CNNs) to process three end-to-end feature maps and a hand-craft features characterized by specific contra-lateral features. To improve the accuracy of the detected infarct area, the spatial dependencies between neighboring slices are employed at the postprocessing step. The numerical experiments have been performed on 18 ncCT-CTp paired stroke cases (804 image-pairs). The leave-one-out approach is applied for evaluating the proposed method. The model achieves 91.16% accuracy, 65.15% precision, 77.44% recall, 69.97% F1 score, and 0.4536 IoU.

show abstract

Section: Previous Workmentioning

confidence: 99%