A New Model for Image Segmentation Based on Deep Learning

Rafeek Mamdouh Tawfiq Yanni, Nashaat El-Khameesy El-Ghitany, Khaled Amer, Alaa Riad, Hazem El-Bakry


Image segmentation of the medical image and its conversion into anatomical models is an important technique and main point in computer vision (CV) and image processing (IP), training tools that are used routinely in the fields of medicine and surgery. Segmenting images and converting them into a model that depends on its work on the different algorithms and the extent of technological advancement and method of application. The advancement of segmentation algorithms has led to the possibility of creating three-dimensional models for the patient to study without endangering his life. This paper describes a combination of two fields of solving segmentation problem to convert through the workflow of a hybrid algorithm structure Convolutional neural network (CNN, Active Contour & Deep Multi-Planar) and seg3d2 to switch DICOM medical rays “Digital Imaging and Communications in Medicine” into a 3Dimintional model, using data from active contour to be the input of deep learning. This research will be using are human liver DICOM images and is divided into two stages (medical image segmentation - retinal model optimization). This is to help doctors and surgeons to study the patient’s condition with accuracy and efficiency through the use of mixed reality technology in liver surgery [living donor liver transplantation (LDLT)], all implement by Seg3D2 and Python.


HYBRID ALGORITHM; Convolutional neural network (CNN); Active Contour Model(ACM); Digital Imaging and Communications in Medicine (DICOM); living donor liver transplantation (LDLT); histogram equalization; Gaussian Equation Medical Image Processing; Image

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International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
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