Tumor detection by using Zernike moments on segmented magnetic resonance brain images

İşcan, Zafer; Dokur, Zümray; Ölmez, Tamer

doi:10.1016/j.eswa.2009.08.003

Cited by 74 publications

(26 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At this stage, the important properties of the brain MRI which could be helpful to us in detecting the abnormalities in brain are identified [1,2,4,6]. After the identification of those features, their size is reduced for easier processing [3,6,8].…”

Section: Mri Features' Extractionmentioning

confidence: 99%

A simple and intelligent approach for brain MRI classification

Nazir

Wahid

Khan

2015

Journal of Intelligent &Amp; Fuzzy Systems

View full text Add to dashboard Cite

There are many approaches for accurate and automatic classification of brain MRI. In this paper, a simple approach for automatic detection and classification is presented. Artificial Neural Network has been utilized for brain MRI classification as malignant or benign. The approach consists of three stages namely pre processing, features' extraction and classification. In pre-processing stage, filters are applied for the removal of noise. In the features' extraction phase, color moments are extracted as mean features from the MRI images and the color moments extracted are presented to simple feed forward artificial neural network for classification. The method was applied using total 70 images with 25 normal images and 45 abnormal images. The classification accuracy was found to be 88.9% for training data, 94.9% for validation data and 94.2% for testing data whereas the overall accuracy of 91.8% was observed.

show abstract

Section: Mri Features' Extractionmentioning

confidence: 99%

A simple and intelligent approach for brain MRI classification

Nazir

Wahid

Khan

2015

Journal of Intelligent &Amp; Fuzzy Systems

View full text Add to dashboard Cite

show abstract

“…With these properties, Zernike moments have been widely used in a variety of fields: invariant pattern or object recognition [5,25,13,40,24,29], image reconstruction [36,31], image segmentation [12], edge detection [11], context-based image retrieval [27], face recognition [15], gait recognition [47] and biomedical imaging [41,22]. Additionally, higher-order Zernike moments have been used in several applications such as in image watermarking [26,6], moving object reconstruction [37], and peg-free hand shape verification [3].…”

Section: Introductionmentioning

confidence: 99%

Fast computation of exact Zernike moments using cascaded digital filters

Lim

Honarvar

Thung

et al. 2011

Information Sciences

View full text Add to dashboard Cite

“…In recent years, moments have been successfully used in a variety of research areas such as image registration, 1 face recognition, 2 angle estimation, 3 watermarking, 4 pattern reconstruction, 5 medical imaging, [6][7][8] focus measures, 9 image analysis, 10 forensic applications, 11 gait phase detection, 12 and so forth. In the 1960s, Hu 13 introduced a set of invariants based on the low-order geometric moments for pattern recognition tasks.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of color biomedical images by means of quaternion generic Jacobi-Fourier moments in the framework of polar pixels

et al. 2016

View full text Add to dashboard Cite

Abstract. A detailed analysis of the quaternion generic Jacobi-Fourier moments (QGJFMs) for color image description is presented. In order to reach numerical stability, a recursive approach is used during the computation of the generic Jacobi radial polynomials. Moreover, a search criterion is performed to establish the best values for the parameters α and β of the radial Jacobi polynomial families. Additionally, a polar pixel approach is taken into account to increase the numerical accuracy in the calculation of the QGJFMs. To prove the mathematical theory, some color images from optical microscopy and human retina are used. Experiments and results about color image reconstruction are presented.

show abstract

Tumor detection by using Zernike moments on segmented magnetic resonance brain images

Cited by 74 publications

References 26 publications

A simple and intelligent approach for brain MRI classification

A simple and intelligent approach for brain MRI classification

Fast computation of exact Zernike moments using cascaded digital filters

Reconstruction of color biomedical images by means of quaternion generic Jacobi-Fourier moments in the framework of polar pixels

Contact Info

Product

Resources

About