A fuzzy approach to assess the indication of dementia based on magnetic reasoning imaging

Huang, Yo-Ping; Yang, Bohan; Zaza, Samuele; Liu, Wei-Heng

doi:10.1109/ifuzzy.2013.6825459

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…This algorithm attempts to partition objects into C fuzzy clusters based on minimizing an objective function, named C means function. FCM is used in brain tissue segmentation in MRI images (Huang et al, 2013). Caldairou et al (2011) presented an FCM-based algorithm to segment and cluster brain tissues into GM, WM, and CSF at higher noise levels and achieved significant performance compared with standard algorithms.…”

Section: Fuzzy Clusteringmentioning

confidence: 99%

Imaging and machine learning techniques for diagnosis of Alzheimer’s disease

Mirzaei

Adeli

2016

Reviews in the Neurosciences

110

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AbstractAlzheimer’s disease (AD) is a common health problem in elderly people. There has been considerable research toward the diagnosis and early detection of this disease in the past decade. The sensitivity of biomarkers and the accuracy of the detection techniques have been defined to be the key to an accurate diagnosis. This paper presents a state-of-the-art review of the research performed on the diagnosis of AD based on imaging and machine learning techniques. Different segmentation and machine learning techniques used for the diagnosis of AD are reviewed including thresholding, supervised and unsupervised learning, probabilistic techniques, Atlas-based approaches, and fusion of different image modalities. More recent and powerful classification techniques such as the enhanced probabilistic neural network of Ahmadlou and Adeli should be investigated with the goal of improving the diagnosis accuracy. A combination of different image modalities can help improve the diagnosis accuracy rate. Research is needed on the combination of modalities to discover multi-modal biomarkers.

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Section: Fuzzy Clusteringmentioning

confidence: 99%

Imaging and machine learning techniques for diagnosis of Alzheimer’s disease

Mirzaei

Adeli

2016

Reviews in the Neurosciences

110

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“…For a brief summ ary of how Freesurfer process and analyze MRI image please refer to our previous conference paper [7]. After the software processed the surface, we can obtain the thickness for every cortex, spherical area, curvature and normal vector to sphere.…”

Section: A Freesurfer Softwarementioning

confidence: 99%

Evaluating dementia risk from MRI by fuzzy inference model

Huang

Zaza

2014

Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control

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It is well-known that individuals' cognitive functions and memory will decay with age, an irreversible process that can result in various diseases. Among them, Alzheimer's disease (AD)is the most common mental disease which affects elderly people whose age is over 65. Though the main causes remain a mystery till nowadays, there is no doubt that this disease appears repeatedly in aged societies. Thus, there is a strong need to improve our current knowledge in order to figure out whether a patient suffers from AD or not, which is not even easy to understand. Since the literature reports that AD subjects present a thin cortical thickness, this study continues our previous one towards this direction. In this study we improved the previous fuzzy c-means clustering which is used to find centroids from persistence diagrams, this one coming from the cortical thickness.As last step we defined a new fuzzy model to estimate whether a subject is affected by dementia or not, based on cortical thickness.New and more experiments conducted on normal, schizophrenia and AD patients gather that our system is reliable and indeed can help to find the Alzheimer's disease in patients.

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