Automatic Tissue Image Segmentation Based on Image Processing and Deep Learning

Journal of Healthcare Engineering
Zhenglun KongShengpu Xu

Abstract

Image segmentation plays an important role in multimodality imaging, especially in fusion structural images offered by CT, MRI with functional images collected by optical technologies, or other novel imaging technologies. In addition, image segmentation also provides detailed structural description for quantitative visualization of treating light distribution in the human body when incorporated with 3D light transport simulation methods. Here, we first use some preprocessing methods such as wavelet denoising to extract the accurate contours of different tissues such as skull, cerebrospinal fluid (CSF), grey matter (GM), and white matter (WM) on 5 MRI head image datasets. We then realize automatic image segmentation with deep learning by using convolutional neural network. We also introduce parallel computing. Such approaches greatly reduced the processing time compared to manual and semiautomatic segmentation and are of great importance in improving the speed and accuracy as more and more samples are being learned. The segmented data of grey and white matter are counted by computer in volume, which indicates the potential of this segmentation technology in diagnosing cerebral atrophy quantitatively. We demonstrate the great pot...Continue Reading

References

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Citations

Jan 10, 2020·Journal of Healthcare Engineering·Santiago Tello-MijaresFrancisco Flores

Related Concepts

Cerebrospinal Fluid
Diagnostic Imaging
Gray Matter
Learning
Magnetic Resonance Imaging
Microscopy
Neurology Speciality
Cerebral Atrophy
Cardiac CT
Three-dimensional

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