A Review of Snake Models in Medical MR Image Segmentation

2014 ◽  
Vol 69 (2) ◽  
Author(s):  
Mohammed Sabbih Hamoud Al-Tamimi ◽  
Ghazali Sulong
Keyword(s):  
Image Data ◽  
Contour Detection ◽  
Current Status ◽  
Ultrasound Images ◽  
Biomedical Image Processing ◽  
Image Domain ◽  
Biomedical Image ◽  
Advantages And Disadvantages ◽  
Contour Segmentation ◽  
External Forces

Developing an efficient algorithm for automated Magnetic Resonance Imaging (MRI) segmentation to characterize tumor abnormalities in an accurate and reproducible manner is ever demanding. This paper presents an overview of the recent development and challenges of the energy minimizing active contour segmentation model called snake for the MRI. This model is successfully used in contour detection for object recognition, computer vision and graphics as well as biomedical image processing including X-ray, MRI and Ultrasound images. Snakes being deformable well-defined curves in the image domain can move under the influence of internal forces and external forces are subsequently derived from the image data. We underscore a critical appraisal of the current status of semi-automated and automated methods for the segmentation of MR images with important issues and terminologies. Advantages and disadvantages of various segmentation methods with salient features and their relevancies are also cited.

scholarly journals Biomedical Image Segmentation and Analysis in Deep Learning

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Tuan Anh Tran ◽  
Tien Dung Cao ◽  
Vu-Khanh Tran ◽  
◽  
Keyword(s):  
Deep Learning ◽  
Image Data ◽  
Patient Treatment ◽  
Biomedical Data ◽  
Biomedical Image Processing ◽  
Human Organ ◽  
Biomedical Image ◽  
Breast Segmentation ◽  
Effective Diagnosis ◽  
Disease Analysis

Biomedical Image Processing, such as human organ segmentation and disease analysis, is a modern field in medicine development and patient treatment. Besides there are many kinds of image formats, the diversity and complexity of biomedical data is still a big issue to all of researchers in their applications. In order to deal with the problem, deep learning give us a successful and effective solutions. Unet and LSTM are two general approaches to the most of case of medical image data. While Unet helps to teach a machine in learning data from each image accompanied with its labelled information, LSTM helps to remember states from many slices of images by times. Unet gives us the segmentation of tumor, abnormal things from biomedical images and then the LSTM gives us the effective diagnosis on a patient disease. In this paper, we show some scenarios of using Unets and LSTM to segment and analysis on many kinds of human organ images and results of brain, retinal, skin, lung and breast segmentation.

scholarly journals Towards an easy generation of scientific three-dimensional models for embedding into 3D PDF (Portable Document Format) files

2014 ◽  
Author(s):  
Axel Newe
Keyword(s):  
Three Dimensional ◽  
Image Data ◽  
Portable Document Format ◽  
3D Models ◽  
Model Data ◽  
Biomedical Image Processing ◽  
Intensive Training ◽  
Biomedical Image ◽  
Three Dimensional Models ◽  
Document Format

The Portable Document Format (PDF) allows for embedding three-dimensional (3D) models and is therefore particularly suitable to exchange and present respective data, especially as regards scholarly articles. The generation of the necessary model data, however, is still challenging, especially for inexperienced users. This prevents an unrestrained proliferation of 3D PDF usage in scientific communication. This article introduces a new module for the biomedical image processing framework MeVisLab. It enables even novice users to generate the model data files without requiring programming skills and without the need for an intensive training by simply using it as a conversion tool. Advanced users can benefit from the full capability of MeVisLab to generate and export the model data as part of an overall processing chain. Although MeVisLab is primarily designed for handling biomedical image data, the new module is not restricted to this domain. It can be used for all scientific disciplines.

Mammogram Mining Using Genetic Ant-Miner

2011 ◽  
pp. 232-250
Author(s):  
K. Thangavel ◽  
R. Roselin
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Image Analysis ◽  
Comparative Analysis ◽  
Image Data ◽  
Experimental Results ◽  
Biomedical Image Analysis ◽  
Image Mining ◽  
Image Domain ◽  
Biomedical Image

Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. It is an extension of data mining to image domain and an interdisciplinary endeavour. This chapter focuses on mammogram classification using genetic Ant-Miner. The key idea is to generate classifier for classifying mammograms as normal or abnormal using the proposed Genetic Ant-Miner algorithm. The Genetic Algorithm has been employed to optimize some of the ant parameters. A comparative analysis is performed in order to achieve the efficiency of the proposed algorithm. Further, the experimental results reveals that the improvement of the proposed Genetic Ant-Miner in the domain of Biomedical image Analysis.

Analysis of Biomedical Image for Alzheimer's Disease Detection

2020 ◽  
pp. 224-251
Author(s):  
Rashmi Kumari ◽  
Shashank Pushkar
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Image Data ◽  
Infrared Sensors ◽  
Biomedical Image Processing ◽  
3D Images ◽  
Brain Image ◽  
Biomedical Image ◽  
Image Capturing ◽  
Statistical Results

Image analysis is giving a huge breakthrough in every field of science and technology. The image is just a collection of pixels and light intensity. The image capturing was done in two ways: (1) by using infrared sensors and (2) by using radiography. The normal images are captured by using the infrared sensors. Radiography uses the various forms of a light family, such as x-ray, gamma rays, etc., to capture the image. The study of neuroimaging is one of the challenging research topics in the field of biomedical image processing. So, from this note, the motivation for this work is to analyze 3D images to detect Alzheimer's disease and compare the statistical results of the whole brain image data with standard doctor's results. The authors also provide a very short implementation for brain slicing and feature extraction using Freesurfer and OpenNeuro dataset.

Analysis of Biomedical Image for Alzheimer's Disease Detection

2021 ◽  
pp. 170-190
Author(s):  
Rashmi Kumari ◽  
Shashank Pushkar
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Image Data ◽  
Infrared Sensors ◽  
Biomedical Image Processing ◽  
3D Images ◽  
Brain Image ◽  
Biomedical Image ◽  
Image Capturing ◽  
Statistical Results

Image analysis is giving a huge breakthrough in every field of science and technology. The image is just a collection of pixels and light intensity. The image capturing was done in two ways: (1) by using infrared sensors and (2) by using radiography. The normal images are captured by using the infrared sensors. Radiography uses the various forms of a light family, such as x-ray, gamma rays, etc., to capture the image. The study of neuroimaging is one of the challenging research topics in the field of biomedical image processing. So, from this note, the motivation for this work is to analyze 3D images to detect Alzheimer's disease and compare the statistical results of the whole brain image data with standard doctor's results. The authors also provide a very short implementation for brain slicing and feature extraction using Freesurfer and OpenNeuro dataset.

scholarly journals Performance Analysis of Gaussian, Median, Mean and Weiner Filters on Biomedical Image De-Noising

2020 ◽  
Author(s):  
Subrato Bharati ◽  
Prajoy Podder
Keyword(s):  
Noise Reduction ◽  
Medical Image ◽  
Medical Images ◽  
Signal To Noise Ratio ◽  
Image Data ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Biomedical Image ◽  
Uniform Noise ◽  
Diagnostic Values

Noise reduction in medical images is a perplexing undertaking for the researchers in digital image processing. Noise generates maximum critical disturbances as well as touches the medical images quality, ultrasound images in the field of biomedical imaging. The image is normally considered as gathering of data and existence of noises degradation the image quality. It ought to be vital to reestablish the original image noises for accomplishing maximum data from images. Medical images are debased through noise through its transmission and procurement. Image with noise reduce the image contrast and resolution, thereby decreasing the diagnostic values of the medical image. This paper mainly focuses on Gaussian noise, Pepper noise, Uniform noise, Salt and Speckle noise. Different filtering techniques can be adapted for noise declining to improve the visual quality as well as reorganization of images. Here four types of noises have been undertaken and applied on medical images. Besides numerous filtering methods like Gaussian, median, mean and Weiner applied for noise reduction as well as estimate the performance of filter through the parameters like mean square error (MSE), peak signal to noise ratio (PSNR), Average difference value (AD) and Maximum difference value (MD) to diminish the noises without corrupting the medical image data.

Mammogram Mining Using Genetic Ant-Miner

Data Mining ◽  
2013 ◽  
pp. 775-792
Author(s):  
K. Thangavel ◽  
R. Roselin
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Image Analysis ◽  
Comparative Analysis ◽  
Image Data ◽  
Experimental Results ◽  
Biomedical Image Analysis ◽  
Image Mining ◽  
Image Domain ◽  
Biomedical Image

Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. It is an extension of data mining to image domain and an interdisciplinary endeavour. This chapter focuses on mammogram classification using genetic Ant-Miner. The key idea is to generate classifier for classifying mammograms as normal or abnormal using the proposed Genetic Ant-Miner algorithm. The Genetic Algorithm has been employed to optimize some of the ant parameters. A comparative analysis is performed in order to achieve the efficiency of the proposed algorithm. Further, the experimental results reveals that the improvement of the proposed Genetic Ant-Miner in the domain of Biomedical image Analysis.

scholarly journals Towards an easy generation of scientific three-dimensional models for embedding into 3D PDF (Portable Document Format) files

2014 ◽  
Author(s):  
Axel Newe
Keyword(s):  
Three Dimensional ◽  
Image Data ◽  
Portable Document Format ◽  
3D Models ◽  
Model Data ◽  
Biomedical Image Processing ◽  
Intensive Training ◽  
Biomedical Image ◽  
Three Dimensional Models ◽  
Document Format

The Portable Document Format (PDF) allows for embedding three-dimensional (3D) models and is therefore particularly suitable to exchange and present respective data, especially as regards scholarly articles. The generation of the necessary model data, however, is still challenging, especially for inexperienced users. This prevents an unrestrained proliferation of 3D PDF usage in scientific communication. This article introduces a new module for the biomedical image processing framework MeVisLab. It enables even novice users to generate the model data files without requiring programming skills and without the need for an intensive training by simply using it as a conversion tool. Advanced users can benefit from the full capability of MeVisLab to generate and export the model data as part of an overall processing chain. Although MeVisLab is primarily designed for handling biomedical image data, the new module is not restricted to this domain. It can be used for all scientific disciplines.

scholarly journals Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 551
Author(s):  
Chris Boyd ◽  
Greg Brown ◽  
Timothy Kleinig ◽  
Joseph Dawson ◽  
Mark D. McDonnell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Model Comparison ◽  
Disease Risk ◽  
Imaging Modalities ◽  
Current Status ◽  
Advantages And Disadvantages ◽  
Body Regions ◽  
Artery Disease

Research into machine learning (ML) for clinical vascular analysis, such as those useful for stroke and coronary artery disease, varies greatly between imaging modalities and vascular regions. Limited accessibility to large diverse patient imaging datasets, as well as a lack of transparency in specific methods, are obstacles to further development. This paper reviews the current status of quantitative vascular ML, identifying advantages and disadvantages common to all imaging modalities. Literature from the past 8 years was systematically collected from MEDLINE® and Scopus database searches in January 2021. Papers satisfying all search criteria, including a minimum of 50 patients, were further analysed and extracted of relevant data, for a total of 47 publications. Current ML image segmentation, disease risk prediction, and pathology quantitation methods have shown sensitivities and specificities over 70%, compared to expert manual analysis or invasive quantitation. Despite this, inconsistencies in methodology and the reporting of results have prevented inter-model comparison, impeding the identification of approaches with the greatest potential. The clinical potential of this technology has been well demonstrated in Computed Tomography of coronary artery disease, but remains practically limited in other modalities and body regions, particularly due to a lack of routine invasive reference measurements and patient datasets.

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