An Approach to Detect Cavities in X-Ray Astronomical Images Using Granular Convolutional Neural Networks

Zhixian MA; Jie ZHU; Weitian LI; Haiguang XU

doi:10.1587/transinf.2017EDP7079

An Approach to Detect Cavities in X-Ray Astronomical Images Using Granular Convolutional Neural Networks

Zhixian MA, Jie ZHU, Weitian LI, Haiguang XU

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Detection of cavities in X-ray astronomical images has become a field of interest, since the flourishing studies on black holes and the Active Galactic Nuclei (AGN). In this paper, an approach is proposed to detect cavities in X-ray astronomical images using our newly designed Granular Convolutional Neural Network (GCNN) based classifiers. The raw data are firstly preprocessed to obtain images of the observed objects, i.e., galaxies or galaxy clusters. In each image, pixels are classified into three categories, (1) the faint backgrounds (BKG), (2) the cavity regions (CAV), and (3) the bright central gas regions (CNT). And the sample sets are then generated by dividing large images into subimages with a window size according to the cavities' scale. Since the number of BKG samples are far more than the other types, to achieve balanced training sets, samples from the major class are split into subsets, i.e., granule. Then a group of three-convolutional-layer granular CNN networks without subsampling layers are designed as the classifiers, and trained with the labeled granular sample sets. Finally, the trained GCNN classifiers are applied to new observations, so as to estimate the cavity regions with a voting strategy and locate them with elliptical profiles on the raw observation images. Experiments and applications of our approach are demonstrated on 40 X-ray astronomical observations retrieved from chandra Data Archive (CDA). Comparisons among our approach, the β-model fitting and the Unsharp Masking (UM) methods were also performed, which prove our approach was more accurate and robust.

Publication: IEICE TRANSACTIONS on Information Vol.E100-D No.10 pp.2578-2586

Publication Date: 2017/10/01

Publicized: 2017/07/18

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2017EDP7079

Type of Manuscript: PAPER

Category: Pattern Recognition

Authors

Zhixian MA
  Shanghai Jiao Tong University
Jie ZHU
  Shanghai Jiao Tong University
Weitian LI
  Shanghai Jiao Tong University
Haiguang XU
  Shanghai Jiao Tong University

Keyword

X-ray astronomical image, cavity, detection, granular convolutional neural network (GCNN)

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Zhixian MA, Jie ZHU, Weitian LI, Haiguang XU, "An Approach to Detect Cavities in X-Ray Astronomical Images Using Granular Convolutional Neural Networks" in IEICE TRANSACTIONS on Information, vol. E100-D, no. 10, pp. 2578-2586, October 2017, doi: 10.1587/transinf.2017EDP7079.
Abstract: Detection of cavities in X-ray astronomical images has become a field of interest, since the flourishing studies on black holes and the Active Galactic Nuclei (AGN). In this paper, an approach is proposed to detect cavities in X-ray astronomical images using our newly designed Granular Convolutional Neural Network (GCNN) based classifiers. The raw data are firstly preprocessed to obtain images of the observed objects, i.e., galaxies or galaxy clusters. In each image, pixels are classified into three categories, (1) the faint backgrounds (BKG), (2) the cavity regions (CAV), and (3) the bright central gas regions (CNT). And the sample sets are then generated by dividing large images into subimages with a window size according to the cavities' scale. Since the number of BKG samples are far more than the other types, to achieve balanced training sets, samples from the major class are split into subsets, i.e., granule. Then a group of three-convolutional-layer granular CNN networks without subsampling layers are designed as the classifiers, and trained with the labeled granular sample sets. Finally, the trained GCNN classifiers are applied to new observations, so as to estimate the cavity regions with a voting strategy and locate them with elliptical profiles on the raw observation images. Experiments and applications of our approach are demonstrated on 40 X-ray astronomical observations retrieved from chandra Data Archive (CDA). Comparisons among our approach, the β-model fitting and the Unsharp Masking (UM) methods were also performed, which prove our approach was more accurate and robust.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7079/_p

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@ARTICLE{e100-d_10_2578,
author={Zhixian MA, Jie ZHU, Weitian LI, Haiguang XU, },
journal={IEICE TRANSACTIONS on Information},
title={An Approach to Detect Cavities in X-Ray Astronomical Images Using Granular Convolutional Neural Networks},
year={2017},
volume={E100-D},
number={10},
pages={2578-2586},
abstract={Detection of cavities in X-ray astronomical images has become a field of interest, since the flourishing studies on black holes and the Active Galactic Nuclei (AGN). In this paper, an approach is proposed to detect cavities in X-ray astronomical images using our newly designed Granular Convolutional Neural Network (GCNN) based classifiers. The raw data are firstly preprocessed to obtain images of the observed objects, i.e., galaxies or galaxy clusters. In each image, pixels are classified into three categories, (1) the faint backgrounds (BKG), (2) the cavity regions (CAV), and (3) the bright central gas regions (CNT). And the sample sets are then generated by dividing large images into subimages with a window size according to the cavities' scale. Since the number of BKG samples are far more than the other types, to achieve balanced training sets, samples from the major class are split into subsets, i.e., granule. Then a group of three-convolutional-layer granular CNN networks without subsampling layers are designed as the classifiers, and trained with the labeled granular sample sets. Finally, the trained GCNN classifiers are applied to new observations, so as to estimate the cavity regions with a voting strategy and locate them with elliptical profiles on the raw observation images. Experiments and applications of our approach are demonstrated on 40 X-ray astronomical observations retrieved from chandra Data Archive (CDA). Comparisons among our approach, the β-model fitting and the Unsharp Masking (UM) methods were also performed, which prove our approach was more accurate and robust.},
keywords={},
doi={10.1587/transinf.2017EDP7079},
ISSN={1745-1361},
month={October},}

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TY - JOUR
TI - An Approach to Detect Cavities in X-Ray Astronomical Images Using Granular Convolutional Neural Networks
T2 - IEICE TRANSACTIONS on Information
SP - 2578
EP - 2586
AU - Zhixian MA
AU - Jie ZHU
AU - Weitian LI
AU - Haiguang XU
PY - 2017
DO - 10.1587/transinf.2017EDP7079
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2017
AB - Detection of cavities in X-ray astronomical images has become a field of interest, since the flourishing studies on black holes and the Active Galactic Nuclei (AGN). In this paper, an approach is proposed to detect cavities in X-ray astronomical images using our newly designed Granular Convolutional Neural Network (GCNN) based classifiers. The raw data are firstly preprocessed to obtain images of the observed objects, i.e., galaxies or galaxy clusters. In each image, pixels are classified into three categories, (1) the faint backgrounds (BKG), (2) the cavity regions (CAV), and (3) the bright central gas regions (CNT). And the sample sets are then generated by dividing large images into subimages with a window size according to the cavities' scale. Since the number of BKG samples are far more than the other types, to achieve balanced training sets, samples from the major class are split into subsets, i.e., granule. Then a group of three-convolutional-layer granular CNN networks without subsampling layers are designed as the classifiers, and trained with the labeled granular sample sets. Finally, the trained GCNN classifiers are applied to new observations, so as to estimate the cavity regions with a voting strategy and locate them with elliptical profiles on the raw observation images. Experiments and applications of our approach are demonstrated on 40 X-ray astronomical observations retrieved from chandra Data Archive (CDA). Comparisons among our approach, the β-model fitting and the Unsharp Masking (UM) methods were also performed, which prove our approach was more accurate and robust.
ER -