Galaxy classification using fractal signature

Lekshmi, S.; Revathy, K.; Nayar, S. R. Prabhakaran

doi:10.1051/0004-6361:20030541

Cited by 33 publications

(17 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The slope of the line resulting from plotting the surface areas versus scale changes is known as the fractal signature [23]. The fractal signature gives important information about the fineness of variations in the gray level surface, with no need for artificial decomposition into harmonic frequencies [22].…”

Section: Fractal Analysismentioning

confidence: 99%

Quantitative assessment of pressure sore generation and healing through numerical analysis of high-frequency ultrasound images

Moghimi¹,

Baygi²,

Torkaman³

et al. 2010

JRRD

View full text Add to dashboard Cite

Abstract-This article focuses on the development of a method to quantitatively assess the healing process of artificially induced pressure sores using high-frequency (20 MHz) ultrasound images. We induced sores in guinea pigs and monitored predefined regions on days 3, 7, 14, and 21 after sore generation. We extracted relevant parameters regarding the tissue echographic structure and attenuation properties. We examined tissue healing by defining a healing function that used the extracted parameters. We verified the significance of the extracted features by using analysis of variance and multiple comparison tests. The features displayed ascending/descending behavior during wound generation and reverse behavior during healing. We optimized the parameters of our healing function by using a pattern search method. We tested the efficiency of the optimized values by calculating the healing function value on assessment days and then comparing these results with the expected pattern of changes in the tissue conditions after removing the applied pressure. The results of this study suggest that the methodology developed may be a viable tool for quantitative assessment of pressure sores during their early generation as well as during healing stages.

show abstract

Section: Fractal Analysismentioning

confidence: 99%

Quantitative assessment of pressure sore generation and healing through numerical analysis of high-frequency ultrasound images

Moghimi¹,

Baygi²,

Torkaman³

et al. 2010

JRRD

View full text Add to dashboard Cite

show abstract

“…The change in measured area with changing scale is used as the fractal signature of the texture [5]. The surface area of the image is computed by the method suggested by mandelbrot for curve measurement.…”

Section: Fractal Signaturementioning

confidence: 99%

“…Fractal dimension using box-counting methods is an effective measure in quantifying nuclear changes in human cancer cells. Fractal features provide an objective means and a powerful tool for analyzing and extracting various features of images [5].…”

Section: Introductionmentioning

confidence: 99%

“…Based on the self-similarity of geometric forms, one can find the power law describing the number of pieces 'a' versus 1/s, where 's' is the scale factor which characterizes the part s as copies of the whole, the exponent of this law is the fractal dimension. The galaxies were classified using fractal dimension and fractal signature which gave a classification rate upto 95% [5].The K-Means and Fuzzy C-Means algorithms are used for classification of remotely sensed images. In both the methods it has been found that 98% classification rate could be achieved [6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Classifying Mammogram Images Using Fractal Features

Tripathy¹,

Pathak²,

Thilagam³

et al. 2007

International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007)

View full text Add to dashboard Cite

Fractal geometry is becoming increasingly important in the study of image characteristics. For recognition of regions and object in natural scenes, there is always a need for features, which are invariant and they provide a good set of descriptive values for the region. There are numerous methods available to estimate parameters from the image of the fractal surface. In this paper, fractal features of digital mammogram images are studied with aim of classifying breast cancer. Fractal dimensions(FD) and fractal signature(FS) are considered as different features to characterize the degree of self-similarity among the points in the clusters. The result has shown the potential usefulness of the fractal signature and fractal dimension for image analysis. The K-Means algorithm is used for classification of images. It has been found that 96% classification rate could be achieved.

show abstract

“…Recently, many machine learning based algorithms were applied for automatic classification of galaxies, such as the approach presented in [5], which used fractal signature as galaxy feature for galaxy classification via using (binary bar code) to identify galaxies by comparing signatures from a cor responding database. That approach applied two classifiers 1) K-nearest neighbour, 2) artificial neural network.…”

Section: Introductionmentioning

confidence: 99%

An intelligent approach for galaxies images classification

Elfattah

El-Bendary

ElSoud

et al. 2013

13th International Conference on Hybrid Intelligent Systems (HIS 2013)

View full text Add to dashboard Cite

This article presents an intelligent automatic ap proach for galaxies images classification based on Artificial Neural Network (ANN) and moment-based features extraction algorithms. The proposed approach consists of three phases; namely, image denoising, feature extraction, and classification phases. For the denoising phase, noise pixels are removed from input images, then input galaxy image is normalized to a uniform scale and Hu seven invariant moment algorithm is applied to reduce the dimensionality of the feature space during the feature extraction phase. Finally, during the classification phase, Self Organize Feature Maps (SOFMs) and Time Lag Recurrent Networks (TLRNs) algorithms are utilized for classifying the input galaxies images into one of four obtained source catalogue types. Experimental results showed that SOFMs provided better classification results than having TLRNs applied. It is alsoconcluded that a small set of features is sufficient to classify galaxy images and provide a fast classification.

show abstract

Galaxy classification using fractal signature

Cited by 33 publications

References 24 publications

Quantitative assessment of pressure sore generation and healing through numerical analysis of high-frequency ultrasound images

Quantitative assessment of pressure sore generation and healing through numerical analysis of high-frequency ultrasound images

Classifying Mammogram Images Using Fractal Features

An intelligent approach for galaxies images classification

Contact Info

Product

Resources

About