Fuzzy Markovian Segmentation in Application of Magnetic Resonance Images

Ruan, Su; Moretti, Bruno; Fadili, Jalal M.; Bloyet, Daniel

doi:10.1006/cviu.2002.0957

Cited by 57 publications

(36 citation statements)

References 34 publications

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“…However, some finite mixture (FM) models have the limitation of not considering the spatial information. That is why increasing attention has been paid recently to methods that model the spatial information by a Markov random field (MRF) [19], [22]- [25]. Finally, nonparametric classification techniques can be considered when no well justified parametric model is known [26], [27].…”

Section: A State-of-the-artmentioning

confidence: 99%

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Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images

Cuadra

Cammoun

Butz³

et al. 2005

IEEE Trans. Med. Imaging

340

271

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We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.Index Terms-Brain tissue models, hidden Markov random fields models, magnetic resonance imaging, partial volume, statistical classification, validation study.

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Section: A State-of-the-artmentioning

confidence: 99%

“…Practically, the sum among all the image voxels of (25) can also be written (29) where is the image histogram [45]. This decreases significantly the number of computations in (26)- (28).…”

Section: Maximizationmentioning

confidence: 99%

Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images

Cuadra

Cammoun

Butz³

et al. 2005

IEEE Trans. Med. Imaging

340

271

View full text Add to dashboard Cite

show abstract

“…A novel approach called enhanced possibilistic Fuzzy C-Means clustering is proposed for segmenting MR brain image into different tissue types on both normal and tumor affected pathological brain images. FCM methods has been proposed for the segmentation of MR Images [26,27]and for the segmentation of major tissues in [28,29] and possible tumor on T1-weighted volumes. The FCM is often used in medical image segmentation [30,31].…”

Section: B Fuzzy C-means (Fcm)mentioning

confidence: 99%

A comparative study of Image Region-Based Segmentation Algorithms

Lalaoui¹,

Mohamadi²

2013

IJACSA

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“…Other more successful approaches consider texture information [10,11]. Different machine learning (ML) segmentation techniques have been investigated: Neural Networks [11], SVMs (Support Vector Machines) [12,13], MRFs (Markov Random Fields) [14,15], and recently CRFs (Conditional Random Fields) [16]. But, as previously mentioned, statistical techniques may not allow differentiation between non-enhancing tumor and normal tissue due to overlapping intensity distributions of healthy tissue with tumor and surrounding edema [17].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Based Experimental Verification of Significance of Skull Tissue removal in Brain Tumor Image segmentation

Hemanth¹,

Vijila²,

Anitha³

2010

IJCA

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In anatomical aspects, magnetic resonance (MR) imaging offers more accurate information for medical examination than other medical images such as X-ray, ultrasonic and CT images. Tumor segmentation from MRI data is an important but time consuming task performed manually by medical experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue, among different patients and, in many cases, similarity between tumor and normal tissue. One of the reasons behind the inferior segmentation efficiency is the presence of artifacts in the MR images. One such artifact is the extracranial tissues (skull). These extracranial tissues often interfere with the normal tissues during segmentation that accounts for the inferior segmentation efficiency. In this paper, an automated segmentation and lesion detection algorithm for high segmentation efficiency is proposed for abnormal MR brain images. The proposed segmentation algorithm consists of three steps. In the first step, extracranial tissues are removed using morphological operations. In the second step, fuzzy C-means algorithm is used to segment the MR brain images into four groups: white matter, gray matter, cerebrospinal fluid and the abnormal tumor region. Finally, pseudo-colouring operation is performed on the segmented image to detect the abnormal tumor region. The proposed method has been applied to abnormal images from four different types namely metastase, meningima, glioma and astrocytoma.the superior nature of the proposed approach is justified by performing a comparative analysis on skull stripped images and non-skull stripped images. Experimental results suggest that the proposed approach provides an effective and promising method for brain tumor extraction from MR images with high accuracy.

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Fuzzy Markovian Segmentation in Application of Magnetic Resonance Images

Cited by 57 publications

References 34 publications

Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images

Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images

A comparative study of Image Region-Based Segmentation Algorithms

Fuzzy Based Experimental Verification of Significance of Skull Tissue removal in Brain Tumor Image segmentation

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