scholarly journals Predicting Scores of Medical Imaging Segmentation Methods with Meta-learning

2020 ◽  
pp. 242-253
Author(s):  
Tom van Sonsbeek ◽  
Veronika Cheplygina
Keyword(s):  
Medical Imaging ◽  
Segmentation Methods ◽  
Meta Learning

scholarly journals Subject Review: Diagnoses cancer diseases systems for most body's sections using image processing techniques

2021 ◽  
Vol 6 (3) ◽  
pp. 056-062
Author(s):  
Dena Nadir George ◽  
Haitham Salman Chyad ◽  
Raniah Ali Mustafa
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Medical Imaging ◽  
Image Processing Techniques ◽  
Classification Feature ◽  
Segmentation Methods ◽  
Comprehensive Survey ◽  
Segmentation Image ◽  
Processing Techniques ◽  
Cancer Diseases

Medical imaging has become an important part of diagnosing, early detection, and treating cancers. In this paper, a comprehensive survey on various image processing techniques for medical images specifically examined cancer diseases for most body sections. These sections are Bone, Liver, Kidney, Breast, Lung, and Brain. Detection of medical imaging involves different stages such as classification, segmentation, image pre-processing, and feature extraction. With regard to this work, many image processing methods will be studied, over 10 surveys reviewing classification, feature extraction, and segmentation methods utilized image processing for cancer diseases for most body's sections are clearly reviewed.

scholarly journals Extracting Retinal Anatomy and Pathological Structure Using Multiscale Segmentation

2019 ◽  
Vol 9 (18) ◽  
pp. 3669 ◽  
Author(s):  
Geng ◽  
Che ◽  
Xiao ◽  
Liu
Keyword(s):  
Image Segmentation ◽  
Medical Imaging ◽  
Blood Vessels ◽  
General Applicability ◽  
Learning Techniques ◽  
Large Databases ◽  
Segmentation Methods ◽  
Fundus Photographs ◽  
Optic Discs ◽  
Pathological Structure

Fundus image segmentation technology has always been an important tool in the medical imaging field. Recent studies have validated that deep learning techniques can effectively segment retinal anatomy and determine pathological structure in retinal fundus photographs. However, several groups of image segmentation methods used in medical imaging only provide a single retinopathic feature (e.g., roth spots and exudates). In this paper, we propose a more accurate and clinically oriented framework for the segmentation of fundus images from end-to-end input. We design a four-path multiscale input network structure that learns network features and finds overall characteristics via our network. Our network’s structure is not limited by segmentation of single retinopathic features. Our method is suitable for exudates, roth spots, blood vessels, and optic discs segmentation. The structure has general applicability to many fundus models; therefore, we use our own dataset for training. In cooperation with hospitals and board-certified ophthalmologists, the proposed framework is validated on retinal images from large databases and can improve diagnostic performance compared to state-of-the-art methods that use smaller databases for training. The proposed framework detects blood vessels with an accuracy of 0.927, which is comparable to exudate accuracy (0.939) and roth spot accuracy (0.904), providing ophthalmologists with a practical diagnostic and a robust analytical tool.

scholarly journals The Impact of Neural Network Techniques in the Optimization of the Image Processing

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Dalya Abdullah Anwer
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Medical Imaging ◽  
Threshold Value ◽  
World Health ◽  
Morphological Operations ◽  
Segmentation Methods ◽  
The World ◽  
Feature Values ◽  
The Impact

Nowadays, image processing is widely utilized in many applications and for various purposes. Scholars proposed and suggested various techniques of image processing. The neural network is one of the main processing techniques, which is a state-of-art method. This paper aims to investigate neural network techniques in the field of image processing. Moreover, medical imaging, as well as increasing trends of utilizing digital medical imaging, has gained huge attention in the health sectors. In this regard, this paper focuses on the effect of neural networks in optimizing medical image processing. In this context, the early diagnosis and detection of the eye have an important role in the avoidance of visual impairment, because of the fact that around 45 million people have visual impairments all over the world, according to the World Health Organization. For this reason, the current paper introduces a new method based on image processing for vascular segmentation based on a morphological active contour. Then, segmentation carried out based on morphological operations, fuzzy c-means, and watershed transform. The output of such segmentation methods was given to conventional neural network. The optimized feature values are then extracted. The threshold value is set to compare these optimized feature values. From this, the best segmentation methods will be obtained.

Coronary Stenosis Measurements Using K-Means Clustering

2018 ◽  
Author(s):  
Farhad Akhbardeh ◽  
Hasan Demirel
Keyword(s):  
Medical Imaging ◽  
Medical Science ◽  
Region Of Interest ◽  
Alternative Methods ◽  
Body Images ◽  
Manual Inspection ◽  
Segmentation Methods ◽  
Edge Based ◽  
Processing Techniques ◽  
Time Frames

Medical imaging is one of common area that nowadays researchers uses human body images for clinical or medical science [1] [2]. Currently most of the diagnoses are performed by doctors after manual inspection of real time frames of the video generated by the respective medical imaging systems. In this paper, we propose to use digital image processing techniques in detection and categorization of the clogs in the arteries (stenosis/blockage) by using the frames generated from the X-ray angiography [3][4]. Utilized image pre-processing methods includes selecting a line of Interest (LOI) on blocked vessel and further selection of the region of interest (ROI) on that area, then automatically cropping the region of interest followed by Gaussian filtering for smoothing. In the post processing, three alternative methods are proposed to measure the stenosis in the vessel. The first method applies thresholding (Local) to extract the vessel of interest. The extracted vessel is analyzed for the calculation of the stenosis in percentage [5]. The second method utilizes segmentation (both edge-based and region-based) of the vessel tissue over the extracted pixels of ROI. The final method uses K-means clustering to differentiate between the vessel regions and non-vessel regions. Among the proposed methods K-means clustering based method outperforms the thresholding and segmentation methods.

Introduction to Medical Imaging

2009 ◽  
Author(s):  
Nadine Barrie Smith ◽  
Andrew Webb
Keyword(s):  
Medical Imaging

An acid indicator for medical imaging

Nature ◽  
1999 ◽  
Author(s):  
Philip Ball
Keyword(s):  
Medical Imaging
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