scholarly journals MITK and 3DMed : An Integrated Platform Applicable for the Development of Computer Assisted Intervention Systems

2008 ◽  
Author(s):  
Jie Tian ◽  
Yakang Dai ◽  
Kexin Deng ◽  
Jian Zheng ◽  
Xiaoqian Dai
Keyword(s):  
Image Processing ◽  
Medical Imaging ◽  
Software Development ◽  
Medical Image ◽  
Medical Image Processing ◽  
Computer Assisted ◽  
Software Platform ◽  
Application System ◽  
3 Dimensional ◽  
Integrated Platform

This paper introduces an integrated 3D medical image processing and analyzing software platform which is open interface and freely available. The platform consists of the Medical Imaging Toolkit (MITK) and the 3-Dimensional Medical Image Processing and Analyzing System (3DMed). MITK is an algorithm toolkit for research and software development, while 3DMed is a MITK based application system with a plug-in framework. The overall architecture and main capabilities of the platform are described in detail. Presented evaluations demonstrate that the platform can benefit the development of computer assisted intervention systems.

Recent Advances in Medical Image Processing

2020 ◽  
pp. 1-14
Author(s):  
Zhen Huang ◽  
Qiang Li ◽  
Ju Lu ◽  
Junlin Feng ◽  
Jiajia Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Processing ◽  
Medical Imaging ◽  
Medical Image ◽  
Medical Image Processing ◽  
Convolution Neural Network ◽  
Artificial Intelligence Technology ◽  
Deep Convolution Neural Network

<b><i>Background:</i></b> Application and development of the artificial intelligence technology have generated a profound impact in the field of medical imaging. It helps medical personnel to make an early and more accurate diagnosis. Recently, the deep convolution neural network is emerging as a principal machine learning method in computer vision and has received significant attention in medical imaging. <b><i>Key Message:</i></b> In this paper, we will review recent advances in artificial intelligence, machine learning, and deep convolution neural network, focusing on their applications in medical image processing. To illustrate with a concrete example, we discuss in detail the architecture of a convolution neural network through visualization to help understand its internal working mechanism. <b><i>Summary:</i></b> This review discusses several open questions, current trends, and critical challenges faced by medical image processing and artificial intelligence technology.

A Novel Software Platform for Medical Image Processing and Analyzing

2008 ◽  
Vol 12 (6) ◽  
pp. 800-812 ◽  
Author(s):  
Jie Tian ◽  
Jian Xue ◽  
Yakang Dai ◽  
Jian Chen ◽  
Jian Zheng
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Software Platform

scholarly journals Medical Image Processing and Analysis Techniques for Detecting Giant Cell Arteritis

2021 ◽  
Author(s):  
Radwan Qasrawi ◽  
Diala Abu Al-Halawa ◽  
Omar Daraghmeh ◽  
Mohammad Hjouj ◽  
Rania Abu Seir
Keyword(s):  
Image Processing ◽  
Medical Imaging ◽  
Giant Cell Arteritis ◽  
Giant Cell ◽  
Case Studies ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Processing ◽  
Image Processing And Analysis ◽  
Detection And Diagnosis

Medical image segmentation and classification algorithms are commonly used in clinical applications. Several automatic and semiautomatic segmentation methods were used for extracting veins and arteries on transverse and longitudinal medical images. Recently, the use of medical image processing and analysis tools improved giant cell arteries (GCA) detection and diagnosis using patient specific medical imaging. In this chapter, we proposed several image processing and analysis algorithms for detecting and quantifying the GCA from patient medical images. The chapter introduced the connected threshold and region growing segmentation approaches on two case studies with temporal arteritis using ultrasound (US) and magnetic resonance imaging (MRI) imaging modalities extracted from the Radiopedia Dataset. The GCA detection procedure was developed using the 3D Slicer Medical Imaging Interaction software as a fast prototyping open-source framework. GCA detection passes through two main procedures: The pre-processing phase, in which we improve and enhances the quality of an image after removing the noise, irrelevant and unwanted parts of the scanned image by the use of filtering techniques, and contrast enhancement methods; and the processing phase which includes all the steps of processing, which are used for identification, segmentation, measurement, and quantification of GCA. The semi-automatic interaction is involved in the entire segmentation process for finding the segmentation parameters. The results of the two case studies show that the proposed approach managed to detect and quantify the GCA region of interest. Hence, the proposed algorithm is efficient to perform complete, and accurate extraction of temporal arteries. The proposed semi-automatic segmentation method can be used for studies focusing on three-dimensional visualization and volumetric quantification of Giant Cell Arteritis.

Sign in / Sign up

Export Citation Format

Share Document