Estimation of window width setting for CT scan brain images using mean of greyscale level to standard deviation ratio

Ee, Chung; Sim, K. S.; Teh, V.; Ting, Fung Fung

doi:10.1109/icoras.2016.7872600

Cited by 5 publications

(1 citation statement)

References 7 publications

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“…The pre-processing operation starts with converting the 16bit of DICOM image into 8-bit of grayscale range using Equation 1 and 2 for visualisation purpose. In order to enhance the visibility of the normal and abnormal tissues, the window setting of window width and window center must be in the range of +40 to +80 Hounsefield unit (HU) [32]. It is important to note that both and of 40 HU, recommended by the radiologist provides a solution that satisfies the visibility of acute ischemic in CT image [33].…”

Section: B Pre-processing Dicom Image Conversionmentioning

confidence: 99%

Deep Transfer Learning Application for Automated Ischemic Classification in Posterior Fossa CT Images

Suberi¹,

Nurshazwani²,

Tomari³

et al. 2019

IJACSA

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Computed Tomography (CT) imaging is one of the conventional tools used to diagnose ischemic in Posterior Fossa (PF). Radiologist commonly diagnoses ischemic in PF through CT imaging manually. However, such a procedure could be strenuous and time consuming for large scale images, depending on the expertise and ischemic visibility. With the rapid development of computer technology, automatic image classification based on Machine Learning (ML) is widely been developed as a second opinion to the ischemic diagnosis. The practical performance of ML is challenged by the emergence of deep learning applications in healthcare. In this study, we evaluate the performance of deep transfer learning models of Convolutional Neural Network (CNN); VGG-16, GoogleNet and ResNet-50 to classify the normal and abnormal (ischemic) brain CT images of PF. This is the first study that intensively studies the application of deep transfer learning for automated ischemic classification in the posterior part of brain CT images. The experimental results show that ResNet-50 is capable to achieve the highest accuracy performance in comparison to other proposed models. Overall, this automatic classification provides a convenient and time-saving tool for improving medical diagnosis.

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Section: B Pre-processing Dicom Image Conversionmentioning

confidence: 99%