A Mathematical Morphology Approach to the Star/Galaxy Characterization

Candéas, Alcione Jandir; Neto, Ulisses de Mendonça Braga; Filho, Edson Costa de Barros Carvalho

doi:10.1590/s0104-65001997000100002

Cited by 9 publications

(5 citation statements)

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“…For binary signals, erosion is a Minkowski set subtraction (i.e., an intersection of set translations), and dilation is a Minkowski set addition (i.e., a union of set translations). These operators were extended to operate on non-binary signals 39,37,14,10 . There are two main types of morphological filter: Binary morphological filters and Grayscale morphology filters.…”

Section: Mathematical Morphologymentioning

confidence: 99%

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Rough Morphology Hybrid Approach for Mammography Image Classification and Prediction

Hassanien

Abraham

2008

Int. J. Comp. Intel. Appl.

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The objective of this research is to illustrate how rough sets can be successfully integrated with mathematical morphology and provide a more effective hybrid approach to resolve medical imaging problems. Hybridization of rough sets and mathematical morphology techniques has been applied to depict their ability to improve the classification of breast cancer images into two outcomes: malignant or benign cancer. Algorithms based on mathematical morphology are first applied to enhance the contrast of the whole original image; to extract the region of interest and to enhance the edges surrounding that region. Then, features are extracted characterizing the underlying texture of the regions of interest by using the gray-level co-occurrence matrix. The rough set approach to attribute reduction and rule generation is further presented. Finally, rough morphology is designed for discrimination of different regions of interest to test whether they represent malignant cancer or benign cancer. To evaluate performance of the presented rough morphology approach, we tested different mammogram images. The experimental results illustrate that the overall performance in locating optimal orientation offered by the proposed approach is high compared with other hybrid systems such as rough-neural and rough-fuzzy systems.

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Section: Mathematical Morphologymentioning

confidence: 99%

“…3: end for 4: Create a new information table S new by using the set of intervals C ai 5: Find the minimal subset of C ai that discerns all the objects in the decision class D using (10).…”

Section: Rough Discretization Processmentioning

confidence: 99%

Rough Morphology Hybrid Approach for Mammography Image Classification and Prediction

Hassanien

Abraham

2008

Int. J. Comp. Intel. Appl.

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“…Some of these applications include image smoothing (Lea & Kellar 1989), removing cirrus emission (Appleton, Siqueira, & Basart 1993;He 1996), astronomical object extraction (Candeas, Braga Neto, & Carvalho Filho 1996, and quantifying simulated galaxy distributions (Ueda 1999).…”

Section: Mathematical Morphologymentioning

confidence: 99%

Mathematical Morphology: Star/Galaxy Differentiation & Galaxy Morphology Classification

Moore

Pimbblet

Drinkwater

2006

Publ. Astron. Soc. Aust

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We present an application of Mathematical Morphology (MM) for the classification of astronomical objects, both for star/galaxy differentiation and galaxy morphology classification. We demonstrate that, for CCD images, 99.3 ± 3.8% of galaxies can be separated from stars using MM, with 19.4 ± 7.9% of the stars being misclassified. We demonstrate that, for photographic plate images, the number of galaxies correctly separated from the stars can be increased using our MM diffraction spike tool, which allows 51.0 ± 6.0% of the high-brightness galaxies that are inseparable in current techniques to be correctly classified, with only 1.4 ± 0.5% of the high-brightness stars contaminating the population. We demonstrate that elliptical (E) and late-type spiral (Sc-Sd) galaxies can be classified using MM with an accuracy of 91.4 ± 7.8%. It is a method involving fewer 'free parameters' than current techniques, especially automated machine learning algorithms. The limitation of MM galaxy morphology classification based on seeing and distance is also presented. We examine various star/galaxy differentiation and galaxy morphology classification techniques commonly used today, and show that our MM techniques compare very favourably.

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“…Several classification algorithms have included photometric colours (e.g., Pollo et al 2010;Kovacs & Szapudi 2016) and/or morphological parameters, such as extension and concentration index calculated, for instance, by SEXTRACTOR (Bertin & Arnouts 1996). Other works implemented a more complex approach on astronomical images, such as moment indices and granulometry (Candéas et al 1997;Moore et al 2006). As demonstrated in Pimbblet et al (2001), among several parameters, both the Full Width at Half Maximum (FWHM) and SEXTRACTOR's stellarity parameter, provide a reliable morphological proxy to separate stars and galaxies.…”

Section: Introductionmentioning

confidence: 99%

The S-PLUS: a star/galaxy classification based on a Machine Learning approach

Costa-Duarte,

Sampedro,

Molino

et al. 2019

Preprint

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We present a star/galaxy classification for the Southern Photometric Local Universe Survey (S-PLUS), based on a Machine Learning approach: the Random Forest algorithm. We train the algorithm using the S-PLUS optical photometry up to r=21, matched to SDSS/DR13, and morphological parameters. The metric of importance is defined as the relative decrease of the initial accuracy when all correlations related to a certain feature is vanished. In general, the broad photometric bands presented higher importance when compared to narrow ones. The influence of the morphological parameters has been evaluated training the RF with and without the inclusion of morphological parameters, presenting accuracy values of 95.0% and 88.1%, respectively. Particularly, the morphological parameter FWHM/PSF performed the highest importance over all features to distinguish between stars and galaxies, indicating that it is crucial to classify objects into stars and galaxies. We investigate the misclassification of stars and galaxies in the broad-band colour-colour diagram (g − r) versus (r −i). The morphology can notably improve the classification of objects at regions in the diagram where the misclassification was relatively high. Consequently, it provides cleaner samples for statistical studies. The expected contamination rate of red galaxies as a function of the redshift is estimated, providing corrections for red galaxy samples. The classification of QSOs as extragalactic objects is slightly better using photometric-only case. An extragalactic point-source catalogue is provided using the classification without any morphology feature (only the SED information) with additional constraints on photometric redshifts and FWHM/PSF values.

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A Mathematical Morphology Approach to the Star/Galaxy Characterization

Cited by 9 publications

References 0 publications

Rough Morphology Hybrid Approach for Mammography Image Classification and Prediction

Rough Morphology Hybrid Approach for Mammography Image Classification and Prediction

Mathematical Morphology: Star/Galaxy Differentiation & Galaxy Morphology Classification

The S-PLUS: a star/galaxy classification based on a Machine Learning approach

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