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Dissertation / Habilitation ; Online: Synthetic Studies of Mn(III) Dipyrromethene Peroxynitrite Decomposition Catalysts by Maryam Imani Nejad, Doctor of Pharmacy

Imani Nejad, Maryam

2013

Abstract: One of the key contributors to the pathogenesis of various diseases is "nitroxidative stress," a condition caused by the overproduction of peroxynitrite (PN). Redox-active transition metal complexes, which function as PN decomposition catalysts (PNDCs), ... ...

Abstract	One of the key contributors to the pathogenesis of various diseases is "nitroxidative stress," a condition caused by the overproduction of peroxynitrite (PN). Redox-active transition metal complexes, which function as PN decomposition catalysts (PNDCs), can redirect oxidative potential of PN and are therefore able to reduce nitroxidative stress. Herein, synthetic methods for making polyfunctional trianionic dipyrromethene (DPM) ligand systems with an X-conjugation site for biomolecule coupling were studied. In addition, derivatives of PNDCs with electron donating groups were synthesized to vary the physicochemical properties and study the catalytic activity. Cross coupling of aryl bromide analogues of PNDCs with amines under Buchwald-Hartwig reaction conditions were investigated. Post-chelate conjugation of a variety of acetylene derivatives with an aryl bromide of the DPM backbone under Sonogashira coupling conditions was also studied. Finally, a succesful Suzuki cross-coupling of the key PNDC aryl bromide with a functionalized aryl boronic acid was demonstrated. Since both PNDCs and S1P antagonists are effective in treating inflammatory and neuropathic pain, hypothesized that a conjugate of our Mn(III)-DPM and a functional antagonist (FTY-720) of the sphingosine-1-phosphate receptor(s) may have synergistic activity. Synthetic routes for preparing two prototype analogues via the X-conjugation site chemistry were also developed.
Schlagwörter	Organic chemistry\|Pharmacy sciences
Thema/Rubrik (Code)	540
Sprache	ENG
Erscheinungsdatum	2013-01-01 00:00:01.0
Verlag	Southern Illinois University at Edwardsville
Erscheinungsland	us
Dokumenttyp	Dissertation / Habilitation ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: Two-step discriminant analysis based multi-view polarimetric SAR image classification with high confidence.

Imani, Maryam

Scientific reports

2022 Band 12, Heft 1, Seite(n) 5984

Abstract: Polarimetric synthetic aperture radar (PolSAR) image classification is a hot topic in remote sensing field. Although recently many deep learning methods such as convolutional based networks have provided great success in PolSAR image classification, but ... ...

Abstract	Polarimetric synthetic aperture radar (PolSAR) image classification is a hot topic in remote sensing field. Although recently many deep learning methods such as convolutional based networks have provided great success in PolSAR image classification, but they need a high volume of labeled samples, which are not usually available in practice, or they cause a high computational burden for implementation. In this work, instead of spending cost for network training, the inherent nature of PolSAR image is used for generation of convolutional kernels for extraction of deep and robust features. Moreover, extraction of diverse scattering characteristics contained in the coherency matrix of PolSAR and fusion of their output classification results with a high confidence have high impact in providing a reliable classification map. The introduced method called discriminative features based high confidence classification (DFC) utilizes several approaches to deal with difficulties of PolSAR image classification. It uses a multi-view analysis to generate diverse classification maps with different information. It extracts deep polarimetric-spatial features, consistent and robust with respect to the original PolSAR data, by applying several pre-determined convolutional filters selected from the important regions of image. Convolutional kernels are fixed without requirement to be learned. The important regions are determined with selecting the key points of image. In addition, a two-step discriminant analysis method is proposed to reduce dimensionality and result in a feature space with minimum overlapping and maximum class separability. Eventually, a high confidence decision fusion is implemented to find the final classification map. Impact of multi-view analysis, selection of important regions as fixed convolutional kernels, two-step discriminant analysis and high confidence decision fusion are individually assessed on three real PolSAR images in different sizes of training sets. For example, the proposed method achieves 96.40% and 98.72% overall classification accuracy by using 10 and 100 training samples per class, respectively in L-band Flevoland image acquired by AIRSAR. Generally, the experiments show high efficiency of DFC compared to several state-of-the-art methods especially for small sample size situations.
Sprache	Englisch
Erscheinungsdatum	2022-04-08
Erscheinungsland	England
Dokumenttyp	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-09871-w
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Two-step discriminant analysis based multi-view polarimetric SAR image classification with high confidence

Maryam Imani

Scientific Reports, Vol 12, Iss 1, Pp 1-

2022 Band 13

Abstract: Abstract Polarimetric synthetic aperture radar (PolSAR) image classification is a hot topic in remote sensing field. Although recently many deep learning methods such as convolutional based networks have provided great success in PolSAR image ... ...

Abstract	Abstract Polarimetric synthetic aperture radar (PolSAR) image classification is a hot topic in remote sensing field. Although recently many deep learning methods such as convolutional based networks have provided great success in PolSAR image classification, but they need a high volume of labeled samples, which are not usually available in practice, or they cause a high computational burden for implementation. In this work, instead of spending cost for network training, the inherent nature of PolSAR image is used for generation of convolutional kernels for extraction of deep and robust features. Moreover, extraction of diverse scattering characteristics contained in the coherency matrix of PolSAR and fusion of their output classification results with a high confidence have high impact in providing a reliable classification map. The introduced method called discriminative features based high confidence classification (DFC) utilizes several approaches to deal with difficulties of PolSAR image classification. It uses a multi-view analysis to generate diverse classification maps with different information. It extracts deep polarimetric-spatial features, consistent and robust with respect to the original PolSAR data, by applying several pre-determined convolutional filters selected from the important regions of image. Convolutional kernels are fixed without requirement to be learned. The important regions are determined with selecting the key points of image. In addition, a two-step discriminant analysis method is proposed to reduce dimensionality and result in a feature space with minimum overlapping and maximum class separability. Eventually, a high confidence decision fusion is implemented to find the final classification map. Impact of multi-view analysis, selection of important regions as fixed convolutional kernels, two-step discriminant analysis and high confidence decision fusion are individually assessed on three real PolSAR images in different sizes of training sets. For example, the proposed method ...
Schlagwörter	Medicine ; R ; Science ; Q
Thema/Rubrik (Code)	006
Sprache	Englisch
Erscheinungsdatum	2022-04-01T00:00:00Z
Verlag	Nature Portfolio
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel: Automatic diagnosis of coronavirus (COVID-19) using shape and texture characteristics extracted from X-Ray and CT-Scan images.

Imani, Maryam

Biomedical signal processing and control

2021 Band 68, Seite(n) 102602

Abstract: Automatic diagnosis of coronavirus (COVID-19) is studied in this research. Deep learning methods especially convolutional neural networks (CNNs) have shown great success in COVID-19 diagnosis in recent works. But they are efficient when the depth of ... ...

Abstract	Automatic diagnosis of coronavirus (COVID-19) is studied in this research. Deep learning methods especially convolutional neural networks (CNNs) have shown great success in COVID-19 diagnosis in recent works. But they are efficient when the depth of network is high enough. However, the use of a deep network requires a sufficiently large training set, which is not available in practice. From the other hand, the use of a shallow CNN may not provide superior results because it is not able to rich feature extraction due to lacking enough convolutional layers. To deal with this difficulty, the contextual features reduced by convolutional filters (CFRCF) is proposed in this work. CFRCF extracts shape and textural features as contextual feature maps from the chest X-ray radiographs and abdominal computed tomography (CT) images. Morphological operators, Gabor filter banks and attribute filters are used for contextual feature extraction. Then, two convolutional filters are applied to the contextual feature cube to extract the nonlinear sub-features and hidden relationships among the contextual features. Finally, a fully connected layer is used to produce a reduced feature vector which is fed to a classifier. Support vector machine and random forest are used as classifier. The experimental results show the superior performance of the proposed method from the recognition accuracy and running time point of view using limited training samples. More than 76% and 94% overall classification accuracy is obtained by the proposed method in CT scan and X-ray images datasets, respectively.
Sprache	Englisch
Erscheinungsdatum	2021-04-02
Erscheinungsland	England
Dokumenttyp	Journal Article
ZDB-ID	2241886-6
ISSN	1746-8108 ; 1746-8094
ISSN (online)	1746-8108
ISSN	1746-8094
DOI	10.1016/j.bspc.2021.102602
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: PolSAR Classification Using Contextual Based Locality Preserving Projection and Guided Filtering

Maryam Imani

International Journal of Information and Communication Technology Research, Vol 13, Iss 2, Pp 29-

2021 Band 38

Abstract: Contextual feature extraction is studied for polarimetric synthetic aperture radar (PolSAR) image classification in this work. The contextual locality preserving projection (CLPP) method is proposed for generation of contextual feature cubes using ... ...

Abstract	Contextual feature extraction is studied for polarimetric synthetic aperture radar (PolSAR) image classification in this work. The contextual locality preserving projection (CLPP) method is proposed for generation of contextual feature cubes using limited training samples. The local information in neighborhood regions is used to extend the training set by including the spatial information. Then, a supervised transform is applied to the polarimetric-contextual feature cube to reduce the data dimensionality while preserves the local structures and settles the samples belonging to the same class close together. Finally, a guided filter is applied to the classification map to degrade the speckle noise. The classification results on two real L-band PolSAR data from AIRSAR show superior performance of CLPP for PolSAR classification in small sample size situations.
Schlagwörter	locality preserving projection ; spatial feature extraction ; classification ; polarization ; guided filter ; Information technology ; T58.5-58.64 ; Telecommunication ; TK5101-6720 ; Electronic computers. Computer science ; QA75.5-76.95
Thema/Rubrik (Code)	004
Sprache	Englisch
Erscheinungsdatum	2021-06-01T00:00:00Z
Verlag	Iran Telecom Research Center
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel: Nearest polarimetric and spatial neighbours for feature space projection and guidance image-based spatial filtering for PolSAR image classification

Imani, Maryam

Remote sensing letters. 2022 Apr. 03, v. 13, no. 4

2022

Abstract: A feature space projection based on the nearest polarimetric and spatial neighbours is proposed for polarimetric synthetic aperture radar (PolSAR) images. The objective function is designed such that increases the class discrimination while preserves the ...

Abstract	A feature space projection based on the nearest polarimetric and spatial neighbours is proposed for polarimetric synthetic aperture radar (PolSAR) images. The objective function is designed such that increases the class discrimination while preserves the contextual properties. Each polarimetric or spatial neighbour is contributed proportional to with its Wishart distance to the given pixel. A guidance image, which involves all polarimetric channels, is also introduced for spatial filtering of the initial classification map, which is efficient for improving the final classification map. The proposed method shows high performance for PolSAR image classification using limited training samples.
Schlagwörter	image analysis ; polarimetry ; synthetic aperture radar
Sprache	Englisch
Erscheinungsverlauf	2022-0403
Umfang	p. 406-417.
Erscheinungsort	Taylor & Francis
Dokumenttyp	Artikel
ISSN	2150-7058
DOI	10.1080/2150704X.2022.2033873
Datenquelle	NAL Katalog (AGRICOLA)

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Artikel: Median-mean line based collaborative representation for PolSAR terrain classification

Imani, Maryam

National Authority of Remote Sensing & Space Science The Egyptian Journal of Remote Sensing and Space Sciences (Online). 2022 Feb., v. 25, no. 1

2022

Abstract: A collaborative representation (CR) based method is proposed for polarimetric synthetic aperture radar (PolSAR) data classification in this work. Although CR can well smooth the PolSAR data and remove the speckle noise but it may degrade the class ... ...

Abstract	A collaborative representation (CR) based method is proposed for polarimetric synthetic aperture radar (PolSAR) data classification in this work. Although CR can well smooth the PolSAR data and remove the speckle noise but it may degrade the class boundaries in heterogeneous regions. To deal with this difficulty, a weighted CR with considering the edge information is proposed. In addition, to further utilize the contextual information, the residual terms of CR are smoothed while the misfitting terms are minimized. Moreover, the median-mean line metric is used to degrade the outlier effects with involving interpolation or extrapolation of mean and median values. The proposed method called median-mean line based CR (MMLCR) leads to superior PolSAR classification results particularly when a limited number of training samples is available. For example, 94.79% overall classification accuracy is achieved for classification of the Flevoland dataset containing 15 classes with just using 10 training samples per class.
Schlagwörter	data collection ; landscapes ; polarimetry ; synthetic aperture radar
Sprache	Englisch
Erscheinungsverlauf	2022-02
Umfang	p. 281-288.
Erscheinungsort	Elsevier B.V.
Dokumenttyp	Artikel
ISSN	1110-9823
DOI	10.1016/j.ejrs.2022.01.011
Datenquelle	NAL Katalog (AGRICOLA)

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Artikel ; Online: Entropy/anisotropy/alpha based 3DGabor filter bank for PolSAR image classification

Imani, Maryam

Geocarto International. 2022 Dec. 13, v. 37, no. 27 p.18491-18519

2022

Abstract: There are two types of features in a polarimetric synthetic aperture radar (PolSAR) image: 1-physical scattering characteristics of radar, and 2-geometric and texture properties. To handle the 3D nature of PolSAR image, a revised 3DGabor filter bank is ... ...

Abstract	There are two types of features in a polarimetric synthetic aperture radar (PolSAR) image: 1-physical scattering characteristics of radar, and 2-geometric and texture properties. To handle the 3D nature of PolSAR image, a revised 3DGabor filter bank is proposed here. Although the 3DGabor filter can explore the interaction between three dimensions of the PolSAR cube, but it does not utilize the physical information of radar including the scattering mechanism. To deal with this issue, the physical parameters of the entropy/anisotropy/alpha decomposition theory are used to revise the 3DGabor filter. The output is joint texture and physical features of the PolSAR cube. These features are highly discriminative such that they can provide high accurate classification maps specially by using limited training samples. The experimental results on three PolSAR images show better performance of the proposed method with respect to several competitors with a significant difference from the statistical point of view.
Schlagwörter	anisotropy ; entropy ; image analysis ; polarimetry ; synthetic aperture radar ; texture ; Entropy/anisotropy/alpha ; 3DGabor filter ; classification ; PolSAR
Sprache	Englisch
Erscheinungsverlauf	2022-1213
Umfang	p. 18491-18519.
Erscheinungsort	Taylor & Francis
Dokumenttyp	Artikel ; Online
ISSN	1752-0762
DOI	10.1080/10106049.2022.2142963
Datenquelle	NAL Katalog (AGRICOLA)

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Artikel: Low frequency and radar’s physical based features for improvement of convolutional neural networks for PolSAR image classification

Imani, Maryam

National Authority of Remote Sensing & Space Science The Egyptian Journal of Remote Sensing and Space Sciences (Online). 2022 Feb., v. 25, no. 1

2022

Abstract: Although various deep neural networks such as convolutional neural networks (CNNs) have been suggested for classification of polarimetric synthetic aperture radar (PolSAR) images, but, they have several deficiencies. CNNs have weakness in producing ... ...

Abstract	Although various deep neural networks such as convolutional neural networks (CNNs) have been suggested for classification of polarimetric synthetic aperture radar (PolSAR) images, but, they have several deficiencies. CNNs have weakness in producing classification maps with reduced noise and also are disabled in extraction of polarimetric/scattering information to explore the physical characteristics of the radar image. A deep neural network based on convolutional blocks is proposed for PolSAR image classification in this work that deals with the above difficulties. The low frequency components of the PolSAR image are added to the output of convolutional blocks to help the network to learn noise reduction. Moreover, eight fuzzy clustering maps obtained by the polarimetric entropy and averaged alpha angle are extracted as radar’s physical feature maps which concatenated with the spatial features extracted by convolutional blocks. So, the proposed network while learns to reduce the speckle noise, learns to simultaneously extract the spatial-physical characteristics of the PolSAR cube. The experiments on two real PolSAR datasets show superior performance of the proposed network compared to CNN, residual network and some other well-done networks.
Schlagwörter	data collection ; entropy ; image analysis ; polarimetry ; synthetic aperture radar
Sprache	Englisch
Erscheinungsverlauf	2022-02
Umfang	p. 55-62.
Erscheinungsort	Elsevier B.V.
Dokumenttyp	Artikel
ISSN	1110-9823
DOI	10.1016/j.ejrs.2021.12.007
Datenquelle	NAL Katalog (AGRICOLA)

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Artikel ; Online: Scattering and contextual features fusion using a complex multi-scale decomposition for polarimetric SAR image classification

Imani, Maryam

Geocarto International. 2022 Dec. 13, v. 37, no. 27 p.17216-17241

2022

Abstract: Polarimetric synthetic aperture radar (PolSAR) images contain rich information about back-scattering and physical characteristics of targets. So, they have high ability for discrimination of different land cover classes. The aim of this research is to ... ...

Abstract	Polarimetric synthetic aperture radar (PolSAR) images contain rich information about back-scattering and physical characteristics of targets. So, they have high ability for discrimination of different land cover classes. The aim of this research is to introduce an efficient method for PolSAR image classification. Extraction of both scattering and contextual features is important for class discrimination. Therefore, the scattering and contextual feature fusion (SCF) method is proposed to fuse the extracted polarimetric and morphological features through applying a complex multi-scale decomposition. The dual tree complex wavelet transform is used to decompose each scattering feature map into its details and approximate components. The contextual feature maps are decomposed in a similar way. Then, details of two kinds of feature maps are fused region by region. This process is also done for the approximation components containing the low frequency information. The result will be a high dimensional fused feature space. The principal discriminant analysis (PDA) is proposed to reduce the data dimensionality with discarding noisy components and increasing the class discrimination. The extracted features are then fed into a simple classifier to obtain the classification map. Three L-band PolSAR images acquired by airborne synthetic aperture radar (AIRSAR) and electronically steered array radar (ESAR) are used for doing experiments. The SCF method shows superior classification results with respect to several state-of-the-art PolSAR classifiers. For example, for the Flevoland dataset containing 15 classes, without applying post processing, the SCF method results in 95.22% overall accuracy compared to 2DCNN with 91.84% and 3DCNN with 93.94% overall accuracy. With applying post processing, the classification results of SCF, 2DCNN and 3DCNN are increased to 99.55%, 98.61% and 99.09%, respectively.
Schlagwörter	data collection ; discriminant analysis ; image analysis ; land cover ; polarimetry ; synthetic aperture radar ; trees ; wavelet ; Dual tree complex wavelet transform ; feature fusion ; polarimetric SAR ; classification
Sprache	Englisch
Erscheinungsverlauf	2022-1213
Umfang	p. 17216-17241.
Erscheinungsort	Taylor & Francis
Dokumenttyp	Artikel ; Online
ISSN	1752-0762
DOI	10.1080/10106049.2022.2123961
Datenquelle	NAL Katalog (AGRICOLA)

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