A biclustering approach based on factor graphs and the max-sum algorithm

Denitto, Matteo; Farinelli, Alessandro; Figueiredo, Mário A. T.; Bicego, Manuele

doi:10.1016/j.patcog.2016.08.033

Cited by 22 publications

(14 citation statements)

References 34 publications

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“…Those methods are determined in this research. The main difference between these methods is that canny edge detection method is in the spatial domain while others are in the frequency domain [9].…”

Section: Segmentation Methods and Algorithmmentioning

confidence: 99%

“…Cognitive Impairment decrease quality of life and treatment amongst MS patients [5][6][7][8]. MS is an inflammatory demyelinating and degenerative disease of the CNS, distinctive pathologically by a different part of brain inflammation, demyelination, axonal loss, often causing motor, sensorial, vision, correlation, and cognitive impairment [9].…”

mentioning

confidence: 99%

“…Moreover, Multiple Sclerosis is between two and three-times more average in females than in males, but males have a propensity for later disease [10]. As mentioned, one of the most famous facilities for diagnosis MS disease is Magnetic Resonance Imaging (MRI) techniques [9], T2weighted (T2-w) and gadolinium-enhanced T1-weighted (T1-w), are highly aware in detecting MS lesions. MRI-derived metrics have become the most important medical tool for diagnosing MS disease.…”

mentioning

confidence: 99%

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Segmentation of Multiple Sclerosis Lesion in Brain MR Images Using Fuzzy C-Means

Gheshlaghi¹,

Madani²,

Suratgar³

et al. 2018

IJAIA

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Magnetic resonance images (MRI) play an important role in supporting and substituting clinical information in the diagnosis of multiple sclerosis (MS) disease by presenting lesion in brain MR images. In this paper, an algorithm for MS lesion segmentation from Brain MR Images has been presented. We revisit the modification of properties of fuzzy -c means algorithms and the canny edge detection. By changing and reformed fuzzy c-means clustering algorithms, and applying canny contraction principle, a relationship between MS lesions and edge detection is established. For the special case of FCM, we derive a sufficient condition and clustering parameters, allowing identification of them as (local) minima of the objective function.

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Section: Segmentation Methods and Algorithmmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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Segmentation of Multiple Sclerosis Lesion in Brain MR Images Using Fuzzy C-Means

Gheshlaghi¹,

Madani²,

Suratgar³

et al. 2018

IJAIA

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Section: Introductionmentioning

confidence: 99%

“…Moreover, Multiple sclerosis is between two and three-times more average in females than in males, but males have a propensity for later disease [10]. As mentioned, one of the most famous facilities for diagnosis MS disease is Magnetic Resonance Imaging (MRI) techniques [9], T2-weighted (T2-w) and gadolinium-enhanced T1-weighted (T1-w), are highly aware in detecting MS plaques. MRI-derived metrics have become the most important medical tool for diagnosing MS disease.…”

Section: Introductionmentioning

confidence: 99%

Multiple Sclerosis Diagnosis with Fuzzy C-Means

Gheshlaghi¹,

Madani²,

Suratgar³

et al. 2018

Computer Science &Amp; Information Technology

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Magnetic resonance imaging (MRI) can support and substitute clinical information in the diagnosis of multiple sclerosis (MS) by presenting lesion. In this paper, we present an algorithm for MS lesion segmentation. We revisit the modification of properties of fuzzy c means algorithms and the canny edge detection. Using reformulated fuzzy c means algorithms, apply canny contraction principle, and establish a relationship between MS lesions and edge detection. For the special case of FCM, we derive a sufficient condition for fixed lesions, allowing identification of them as (local) minima of the objective function.

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