Segmentation and grading of brain tumors on apparent diffusion coefficient images using self-organizing maps

Chinnadurai, Vijayakumar; Damayanti, Gharpure; Pant, Rajendra; Sreedhar, CM

doi:10.1016/j.compmedimag.2007.04.004

Cited by 73 publications

(31 citation statements)

References 22 publications

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“…A number of approaches have been used to segment and predict the grade and volume of the brain tumour. Vijayakumar et al [74] proposed a computer-assisted method based on hierarchical multiresolution wavelet to perform segmentation of brain tumours on apparent diffusion coefficient (ADC) images. Kitajima et al [75] developed an algorithm for differential diagnosis among pituitary adenoma, craniopharyngioma and Rathke's cleft cyst with MRI images.…”

Section: Brain Tumour Diagnosismentioning

confidence: 99%

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

2016

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Section: Brain Tumour Diagnosismentioning

confidence: 99%

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

2016

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“…The brain cortex is organized in such a manner that closer neurons will give answers to the same kind of stimulus; this is one of the reason because of which SOM technique is used in visual pattern recognition. Vijayakumar et al [16] proposed SOM method to segment tumor, necrosis, cysts, edema, and normal tissue in T2 and FLAIR MRI. Murugavalli and Rajamani presented a hybrid technique of a Hierarchical Self Organizing Map (HSOM) and Fuzzy Clustering Mean (FCM)to detect various tissues like white matter, gray matter, CSF and tumor in T1 MR images [17].…”

Section: C3 Artificial Neural Networkmentioning

confidence: 99%

Survey on Segmentation of Brain Tumor: A Review of Literature

Anand¹,

Kaur²

2016

International Journal of Advanced Research in Computer and Comm

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Segmentation of brain tumor is a vital step in the initial detection of tumor in the medical field. Although various methods have been presented for brain tumor segmentation, but enhancing tumor is a challenging task since tumor possess complex characteristics in appearance and boundaries. Brain tumor' segmentation must be done with precision in the clinical practises. The objective of this review paper presents a comprehensive overview for MRI brain tumor segmentation methods. In this paper, various techniques like region based, fuzzy clustering means (FCM), thresholding and artificial neural network (ANN) algorithms have been discussed. Comparative analysis among these various segmentation conventions has been evaluated. Finally, culmination and future aspects are addressed for brain tumor segmentation methods.

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“…In order to visualize the resulting SOM cluster structure, the unified distance matrix (U-matrix) has been used by some authors (e.g. Vijayakumar et al 2007 65 ). However, since it has been argued that this method might not result in a map with crisp boundaries to the clusters (Worner & Gevrey, 2006 66 ), we use in this study a hierarchical cluster analysis with a Ward linkage method in order to clearly delineate the boundaries of each resulting cluster.…”

Section: Kohonen Mapsmentioning

confidence: 99%