Unsupervised medical image segmentation on brain MRI images using Skew Gaussian distribution

Vadaparthi, Nagesh; Yarramalle, Srinivas; Penumatsa, Suresh Varma

doi:10.1109/icrtit.2011.5972371

Cited by 8 publications

(7 citation statements)

References 6 publications

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“…Using above metrics, the performance evaluation is carried out and the comparison is done with respect to the model proposed using skew symmetric distribution [5] and the results are presented below in Table -5 and bar graphs -2. …”

Section: B1s4mentioning

confidence: 99%

“…Many segmentation algorithms are presented in literature [6], [7], [8], [9], [10]. Among these techniques, medical image segmentation based on K-Means is mostly utilized [5]. But, the main disadvantage with K-Means is that, K-Means are slow in convergence and pseudo unsupervised learning that requiresthe initial value of K. Apart from K-Means, hierarchical clustering algorithm is also used but even this algorithm shares similar arguments as the case of K-Means algorithm.…”

Section: Fuzzy C-means Clustering Algorithmmentioning

confidence: 99%

“…Among these methods, techniques based on segmentation and mixture models have gained popularity. Segmentation is mainly used by radiologists to segment the image into meaningful regions [1], [2], [3], [4].The specific application of these segmentation techniques is to detect the tumor regions by segmenting the MRI data [5]. The size of the tumor can be estimated by using the segmentation techniques thereby planning for the treatment.…”

Section: Introductionmentioning

confidence: 99%

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Segmentation of Brain MR Images based on Finite Skew Gaussian Mixture Model with Fuzzy CMeans Clustering and EM Algorithm

Vadaparthi¹,

Srinivas²,

Suresh³

et al. 2011

IJCA

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Segmentation is a process of converting inhomogeneous data into homogeneous data. There are many segmentation techniques available inthe literature. Among these techniques, finite Gaussian Mixture Model using EM algorithm is one mostly used. However, Gaussian Mixture Model is suited well when the image under consideration is symmetric. But in reality, medical images are asymmetric. Hence, it is needed to develop new algorithms for segmenting non -symmetric images. Therefore, skew symmetric mixture model is utilized for this purpose. The segmentation is carried out by using Fuzzy C-Means clustering technique and the updated parameters are obtained through EM algorithm. The model is tested with 8 images and the segmentation evaluation is carried out by using objective evaluation criteria namely Jaccard Coefficient (JC) and Volumetric Similarity (VS), Variation of Information (VOI), Global Consistency Error (GCE) and Probabilistic Rand Index (PRI). The performance evaluation of reconstructed images is carried out by using image quality metrics. The experimentation is carried out using T 1 weighted images and the results are compared with the existing models.

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Section: B1s4mentioning

confidence: 99%

Section: Fuzzy C-means Clustering Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Segmentation of Brain MR Images based on Finite Skew Gaussian Mixture Model with Fuzzy CMeans Clustering and EM Algorithm

Vadaparthi¹,

Srinivas²,

Suresh³

et al. 2011

IJCA

Self Cite

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“…It is also observed that the image regions have finite range of pixel intensities (-∞, +∞) and may not be symmetric and Meso kurtic [3]. In this paper, to have an accurate modeling of the feature vector, finite truncated skew Gaussian is considered by assuming that the pixel intensities in the entire image follow a Finite Truncated Skew Gaussian distribution [4][5] [6][7] [8].…”

Section: Introductionmentioning

confidence: 99%

An Improved Brain Mr Image Segmentation using Truncated Skew Gaussian Mixture

Vadaparthi¹,

Yerramalle²,

Penumatsa³

2015

ijacsa

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“…Nagesh Vadaparthi et al [22] have offered a paper in which particular cases like Acoustic neuroma, it was presumed that there was an option of hearing loss, dizziness and other symptoms associated to brain. Surgery can cure various acoustic neuromas.…”

mentioning

confidence: 99%

A Novel Method of Brain Tumor Segmentation Using Extensive Feature Set

Bhanumurthy¹,

Anne²

2014

AIVP

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Brain tissue Segmentation from the MRI images is having significance in the medical research field. The accurate Segmentation of the normal as well as the abnormal tissues is the complex assignment in this process. Because of the inconsistency and difficulty of abnormal tissues, MRI Brain Image Segmentation turned into more hard procedure. In this paper, a technique is proposed for segmenting the abnormalities such as Tumor and Atrophy in the MRI Brain images. (1) Feature extraction (2) Classification (3) Segmentation are the three stages offered in this work. At first, the features such as energy, entropy, homogeneity, contrast and correlation from MRI Brain Images are extracted. Next, by utilizing Neuro-Fuzzy classifier, the Classification process is carried out and for this process, the feature set is specified as the input. From the outcome of Classification, the images are categorized into normal as well as abnormal. The further procedure Segmentation is performed according to this outcome only. The abnormal MRI images are segmented into abnormal tissues like Tumor and Atrophy using Region Growing method. Utilizing MATLAB platform the implementation of the proposed technique is made. The experimentation is carried out on the MRI Brain Images by BrainWeb data sets. The performance of our proposed technique is assessed with the help of the metrics namely FPR, FNR, Specificity, Sensitivity and Accuracy. Therefore, using our proposed technique with enhanced classification, the abnormal tissues of MRI Brain images are segmented accurately.

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Unsupervised medical image segmentation on brain MRI images using Skew Gaussian distribution

Cited by 8 publications

References 6 publications

Segmentation of Brain MR Images based on Finite Skew Gaussian Mixture Model with Fuzzy CMeans Clustering and EM Algorithm

Segmentation of Brain MR Images based on Finite Skew Gaussian Mixture Model with Fuzzy CMeans Clustering and EM Algorithm

An Improved Brain Mr Image Segmentation using Truncated Skew Gaussian Mixture

A Novel Method of Brain Tumor Segmentation Using Extensive Feature Set

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