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.

scholarly journals Versatile Framework for Medical Image Processing and Analysis with Application to Automatic Bone Age Assessment

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Chen Zhao ◽  
Jungang Han ◽  
Yang Jia ◽  
Lianghui Fan ◽  
Fan Gou
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Medical Image ◽  
Deep Neural Networks ◽  
Medical Image Processing ◽  
Bone Age ◽  
Age Assessment ◽  
Image Processing And Analysis ◽  
Learning Technique ◽  
Bone Age Assessment

Deep learning technique has made a tremendous impact on medical image processing and analysis. Typically, the procedure of medical image processing and analysis via deep learning technique includes image segmentation, image enhancement, and classification or regression. A challenge for supervised deep learning frequently mentioned is the lack of annotated training data. In this paper, we aim to address the problems of training transferred deep neural networks with limited amount of annotated data. We proposed a versatile framework for medical image processing and analysis via deep active learning technique. The framework includes (1) applying deep active learning approach to segment specific regions of interest (RoIs) from raw medical image by using annotated data as few as possible; (2) generative adversarial Network is employed to enhance contrast, sharpness, and brightness of segmented RoIs; (3) Paced Transfer Learning (PTL) strategy which means fine-tuning layers in deep neural networks from top to bottom step by step to perform medical image classification or regression tasks. In addition, in order to understand the necessity of deep-learning-based medical image processing tasks and provide clues for clinical usage, class active map (CAM) is employed in our framework to visualize the feature maps. To illustrate the effectiveness of the proposed framework, we apply our framework to the bone age assessment (BAA) task using RSNA dataset and achieve the state-of-the-art performance. Experimental results indicate that the proposed framework can be effectively applied to medical image analysis task.

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.

The Location of Cancer Area Based on Region Growing Algorithm in Medical Image

2013 ◽  
Vol 760-762 ◽  
pp. 1552-1555 ◽  
Author(s):  
Jing Jing Wang ◽  
Xiao Wei Song ◽  
Mei Fang
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Processing ◽  
Region Growing ◽  
Challenging Problem ◽  
Correct Judgment ◽  
Threshold Segmentation ◽  
Seed Points

Image segmentation in medical image processing has been extensively used which has also been applied in different fields of medicine to assist doctors to make the correct judgment and grasp the patient's condition. However, nowadays there are no image threshold segmentation techniques that can be applied to all of the medical images; so it has became a challenging problem. In this paper, it applies a method of identifying edge of the tissues and organs to recognize its contour, and then selects a number of seed points on the contour range to locate the cancer area by region growing. And finally, the result has demonstrated that this method can mostly locate the cancer area accurately.

scholarly journals IBK – A NEW TOOL FOR MEDICAL IMAGE PROCESSING

2010 ◽  
Vol 13 (4) ◽  
pp. 20-27
Author(s):  
Linh Duy Tran ◽  
Linh Quang Huynh
Keyword(s):  
Image Processing ◽  
Diagnostic Imaging ◽  
3D Reconstruction ◽  
Programming Language ◽  
Medical Image ◽  
Medical Images ◽  
Rapid Development ◽  
Medical Image Processing ◽  
Matlab Programming

Along with the rapid development of diagnostic imaging equipment, software for medical image processing has played an important role in helping doctors and clinicians to reach accurate diagnoses. In this paper, methods to build a multipurpose tool based on Matlab programming language and its applications are presented. This new tool features enhancement, segmentation, registration and 3D reconstruction for medical images obtained from commonly used diagnostic imaging equipment.

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