Medical Image Segmentation Methods, Algorithms, and Applications

Norouzi, Alireza; Rahim, Mohd Shafry Mohd; Altameem, Ayman; Saba, Tanzila; Rad, Abdolvahab Ehsani; Rehman, Amjad; Uddin, Mueen

doi:10.1080/02564602.2014.906861

Cited by 272 publications

(150 citation statements)

References 70 publications

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“…1 Database sample. Digital X-ray database sample tissues [10][11][12]. There are three common types of dental diseases [6,13,14] such as (a) Enamel caries represent formation of a microbial dental plaque.…”

Section: Caries Categorizationmentioning

confidence: 99%

Digital Dental X-ray Database for Caries Screening

Rad

Rahim

Rehman

et al. 2016

3D Res

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Standard database is the essential requirement to compare the performance of image analysis techniques. Hence the main issue in dental image analysis is the lack of available image database which is provided in this paper. Periapical dental X-ray images which are suitable for any analysis and approved by many dental experts are collected. This type of dental radiograph imaging is common and inexpensive, which is normally used for dental disease diagnosis and abnormalities detection. Database contains 120 various Periapical X-ray images from top to bottom jaw. Dental digital database is constructed to provide the source for researchers to use and compare the image analysis techniques and improve or manipulate the performance of each technique.

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Section: Caries Categorizationmentioning

confidence: 99%

Digital Dental X-ray Database for Caries Screening

Rad

Rahim

Rehman

et al. 2016

3D Res

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“…Yazdani et al [32] have provided detail survey of segmentation techniques for brain MRI which might prove to be helpful in AD cases. Norouzi et al [29] in their paper have covered many segmentation methods for Medical Image Analysis covering advantages and disadvantages of each with application to MRI and CT. Automatic classification using various machine learning methods, segmentation of diseased tissues or other anatomical parts of brain, feature extraction and selection methods has significant impact for detection of the onset and progression of this disease. For this reason feature extraction and selection methods and classification methods can also be incorporated in the detection of AD.…”

Section: Literature Surveymentioning

confidence: 99%

Neuroimaging and Pattern Recognition Techniques for Automatic Detection of Alzheimer’s Disease: A Review

Kamathe¹,

Joshi²

2017

IJIVP

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Alzheimer's disease (AD) is the most common form of dementia with currently unavailable firm treatments that can stop or reverse the disease progression. A combination of brain imaging and clinical tests for checking the signs of memory impairment is used to identify patients with AD. In recent years, Neuroimaging techniques combined with machine learning algorithms have received lot of attention in this field. There is a need for development of automated techniques to detect the disease well before patient suffers from irreversible loss. This paper is about the review of such semi or fully automatic techniques with detail comparison of methods implemented, class labels considered, data base used and the results obtained for related study. This review provides detailed comparison of different Neuroimaging techniques and reveals potential application of machine learning algorithms in medical image analysis; particularly in AD enabling even the early detection of the disease-the class labelled as Multiple Cognitive Impairment.

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“…In a study by Norouzi et al (3) , medical segmentation methods, algorithms and applications were discussed. Advantages and disadvantages of each segmentation method were described along with their examination and its algorithm applied in Magnetic Resonance Imaging and Computed Tomography image analysis.…”

Section: + Both Authors Contributed Equallymentioning

confidence: 99%

Identification of Possible Infarction on the Cross Section of Cardiac Muscle Tissue Using Color Segmentation and Difference of Gaussian

Chico

Famador

2016

Proceedings of the 4th IIAE International Conference on Intelligent Systems and Image Processing 2016

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Medical Imaging is essential for medical diagnosis and treatment so there is a need to be able to obtain quantitative data from them for analysis and these data can be obtained through Image Processing. The data from the microscopic images could possibly contain information about concerning changes and variations in the forms of organisms that describe the relationship between the human body and disease. In this study, Color Segmentation was used to detect possible infarction locations by analyzing digital microscopic images of the cross section of cardiac muscle tissues and the Derivative of Gaussian (DoG) was used to stitch the selected microscopic images together. With these algorithms we were able to detect the possible locations of infarction and obtain their quantitative data and create an application for automated analysis.

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Medical Image Segmentation Methods, Algorithms, and Applications

Cited by 272 publications

References 70 publications

Digital Dental X-ray Database for Caries Screening

Digital Dental X-ray Database for Caries Screening

Neuroimaging and Pattern Recognition Techniques for Automatic Detection of Alzheimer’s Disease: A Review

Identification of Possible Infarction on the Cross Section of Cardiac Muscle Tissue Using Color Segmentation and Difference of Gaussian

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