Radial basis function-based image segmentation using a receptive field

Kovačević, Domagoj; Loncaric, S.

doi:10.1109/cbms.1997.596421

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…Moreover, the training algorithm is relatively simple as compared to the iterative back-propagation algorithm used in the multi-layer perceptron (MLP). The proposed algorithm does not perform well on trained data [26].…”

Section: Literature Reviewmentioning

confidence: 86%

Automated Brain Tumor Detection in Medical Brain Images and Clinical Parameters using Data Mining Techniques: A Review

Khan¹,

Singh²,

Maheshwari³

2014

IJCA

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Data mining is a growing field of research that intersects with many other fields such as Artificial Intelligent, Statistics, Visualization, Parallel Computing and Image Processing. Data mining techniques are good for Brain MRI image classification that can diagnose brain tumor and other diseases. In this paper we present an overview of the current research being carried out using the data mining techniques for the diagnosis of brain tumor. The goal of this study is to identify the most well performing data mining algorithms used on medical brain MRI and Clinical parameters. The following algorithms have been identified: Decision Trees, Support Vector Machine, Artificial Neural Networks and their Multilayer Perceptron model, and Fuzzy C-Means. Analyses show that it is very difficult to name a single data mining algorithm as the most suitable for the brain tumor detection or classification. At times some algorithms perform better than others, but there are cases when the properties of some of the above mentioned algorithm are combined together, they provide effective result. This paper also provides a critical evaluation of the literature reviewed, which reveals new facets of.

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Section: Literature Reviewmentioning

confidence: 86%

Automated Brain Tumor Detection in Medical Brain Images and Clinical Parameters using Data Mining Techniques: A Review

Khan¹,

Singh²,

Maheshwari³

2014

IJCA

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“…Kovacevic et al [14] propos a segmentation method for brain images that performs a basic segmentation process comprising three steps. In the first step, prominent features of images are extracted and normalization is carried out.…”

Section: Literature Reviewmentioning

confidence: 99%

A Review of Fully Automated Techniques for Brain Tumor Detection From MR Images

Gondal¹,

Khan²

2013

IJMECS

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Abstract-Radiologists use medical images to diagnose diseases precisely. However, identification of brain tumor from medical images is still a critical and complicated job for a radiologist. Brain tumor identification form magnetic resonance imaging (MRI) consists of several stages. Segmentation is known to be an essential step in medical imaging classification and analysis. Performing the brain MR images segmentation manually is a difficult task as there are several challenges associated with it. Radiologist and medical experts spend plenty of time for manually segmenting brain MR images, and this is a nonrepeatable task. In view of this, an automatic segmentation of brain MR images is needed to correctly segment White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CSF) tissues of brain in a shorter span of time. The accurate segmentation is crucial as otherwise the wrong identification of disease can lead to severe consequences. Taking into account the aforesaid challenges, this research is focused towards highlighting the strengths and limitations of the earlier proposed segmentation techniques discussed in the contemporary literature. Besides summarizing the literature, the paper also provides a critical evaluation of the surveyed literature which reveals new facets of research. However, articulating a new technique is beyond the scope of this paper.

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“…For medical image segmentation, RBF extracts features and classifies image pixels (Sing et al, 2005). In (Kovacevic, 1997), an RBF network was utilized to segment CT images of the head. In (Halkiotis, 2007), an RBF network was used to detect clustered microclasifcations in digital mammograms automatically.…”

Section: Radial Basis Function Networkmentioning

confidence: 99%