A New Model for Image Segmentation Based on Deep Learning

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

doi:10.3991/ijoe.v17i07.21241

Abstract

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 &amp; 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.

Highlights

1.1 Digital Imaging and Communications in Medicine (DICOM) & Big data classificationsDICOM files contain a lot of information storage known as BIG DATA, most of which is not required
We propose a process to implement liver DICOM image segmentation, and use active contour technique as a method for segmentation as input data where use seg3D2 and Convolutional neural network (CNN) network of deep learning Figure 3
We show the result of the pre-processing from DICOM raw images

Summary

Introduction

DICOM files contain a lot of information storage known as BIG DATA, most of which is not required. DICOM consists of multi-layers images that are combined through a specific system to show radiograph results. BD is Bulky information, contains a Geographic Information System (GIS) 3D model, medical image information, audio, video, DICOM documents, networking documents, and online history [3]. Despite the huge volume of information stored on the electronic cloud that can reach a petabyte or and Exabyte of information, it is related to the process of stability and stability of information, the extent of its exchange, and the implementation of important activities on time, and the extent to which it is possible to analyze and display information in "DICOM" files with huge parameters and stored inside several Layers [4][5][6]

Methods

Results

Conclusion