Prof. Hongbo Du Profile

Prof. Hongbo Du

Lecturer at University of Buckingham

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Author

Publications (18)

Proceedings Article | 27 May 2022 Presentation + Paper

Fully convolutional network for breast lesion segmentation in ultrasound image: towards false positive reduction

Sicong Zhang, Alaa Al Zoubi, Hongbo Du

Proceedings Volume 12100, 1210007 (2022) https://doi.org/10.1117/12.2618502

KEYWORDS: Image segmentation, Ultrasonography, Breast, Data modeling, Medical imaging, Image processing, Convolution, Neural networks, RGB color model

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Proceedings Article | 27 May 2022 Presentation + Paper

Machine leaning assessment of border irregularity of thyroid nodules from ultrasound images

Fakher Mohammad, Alaa AlZoubi, Hongbo Du, Sabah Jassim

Proceedings Volume 12100, 1210006 (2022) https://doi.org/10.1117/12.2618470

KEYWORDS: Fractal analysis, Ultrasonography, Distance measurement, Skin, Cancer, Image classification, Feature extraction, Visualization, Data modeling, Binary data

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Proceedings Article | 27 May 2022 Presentation + Paper

Ultrasound image augmentation by tumor margin appending for robust deep learning based breast lesion classification

Tahir Hassan, Alaa Al Zoubi, Hongbo Du, Sabah Jassim

Proceedings Volume 12100, 1210008 (2022) https://doi.org/10.1117/12.2618656

KEYWORDS: Tumors, Breast, Data modeling, Performance modeling, Image segmentation, Statistical modeling, Image classification, Diagnostics, Ultrasonography, Medical imaging

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Proceedings Article | 30 June 2021 Paper

A generic approach for automatic crack recognition in buildings glass facade and concrete structures

F. Mohammad, A. AlZoubi, H. Du, S. Jassim

Proceedings Volume 11878, 1187808 (2021) https://doi.org/10.1117/12.2601061

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Proceedings Article | 12 April 2021 Presentation + Paper

Towards optimal cropping: breast and liver tumor classification using ultrasound images

Tahir Hassan, Alaa AlZoubi, Hongbo Du, Sabah Jassim

Proceedings Volume 11734, 117340G (2021) https://doi.org/10.1117/12.2589038

KEYWORDS: Image classification, Ultrasonography, Liver, Breast, Computer aided diagnosis and therapy, Machine learning, Diagnostics, Computing systems, Computer aided design, Classification systems

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In tumor diagnostics from Ultrasound scan images, the region of interest is often determined by marking the boundary of the suspect mass by experts, simply by clicking on sufficient number of tumor boundary points. To determine whether the tumor is malignant or benign, clinical experts who are trained for long time on how to interpret image information from the marked tumor region and from the surrounding area. In contrast, in designing automatic computer aided diagnosis system using both traditional and conventional machine learning, the relevant image features are generally obtained by cropping the tumor as region of interest (RoI) without considering the periphery of the tumor that might contain important discriminative information for better classification accuracy. In this work, we investigate the impact on classification accuracy of different types of tumors by the cropping strategy where the tumor area will be augmented by a proportion of the surrounding region of the ROI. The required optimal proportion need to be determined so that the cropped ROIs encapsulate information about posterior echo and shadow of the tumor in addition to internal texture and echo that has mainly been used as classification indicators. Recently proposed cropping techniques use the best fitting ellipse of the tumor and examine the proportion by which the ellipse is expanded to improve accuracy. Unfortunately, the fitting ellipse may not reflect the shape of the tumor. Here, we investigate a number of alternative approaches of cropping the ROIs using the concept of convex hull shape(s) determined from the tumor boundary points selected by radiologists. Initially, we check several expansion ratio scales of the convex hull ranging from 0.6 to 4.0 against the cropped tumor without margin. Several classification methods including handcrafted features and deep learning methods are adopted for breast and liver tumors using ultrasound images. We shall demonstrate the importance of optimal cropping for breast and liver ultrasound tumor classification. Furthermore, optimal margin depends on the cancer type and classification method as well.

Proceedings Article | 12 April 2021 Presentation + Paper

Sensitivity and stability of pretrained CNN filters

Jehan Ghafuri, Hongbo Du, Sabah Jassim

Proceedings Volume 11734, 117340B (2021) https://doi.org/10.1117/12.2589521

KEYWORDS: Image filtering, Image analysis, Condition numbers, Ultrasonography, Machine vision, Image classification, Gaussian filters, Data analysis, Convolutional neural networks, Computer vision technology

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Proceedings Article | 12 April 2021 Paper

A model-based adaptive method for speckle noise reduction in ultrasound images of ovarian tumours: a new approach

Dhurgham Al-Karawi, Dheyaa Ibrahim, Hisham Al-Assam, Hongbo Du, Sabah Jassim

Proceedings Volume 11734, 117340J (2021) https://doi.org/10.1117/12.2587671

KEYWORDS: Speckle, Ultrasonography, Denoising, Model-based design, Image quality, Image filtering, Tissues, Target detection, Solids, Solid modeling

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Proceedings Article | 19 May 2020 Presentation + Paper

Topological aspects of CNN convolution layers for medical image analysis

Jehan Ghafuri, Hongbo Du, Sabah Jassim

Proceedings Volume 11399, 113990X (2020) https://doi.org/10.1117/12.2567476

KEYWORDS: Image filtering, Convolution, Gaussian filters, Medical imaging, Ultrasonography, Condition numbers, Bladder, Image classification, Feature extraction, RGB color model

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Proceedings Article | 19 May 2020 Presentation + Paper

Automatic glass crack recognition for high building façade inspection

Fakher Mohammad, Alaa AlZoubi, Hongbo Du, Sabah Jassim

Proceedings Volume 11399, 113990W (2020) https://doi.org/10.1117/12.2567409

KEYWORDS: Glasses, Image segmentation, Inspection, Video, Edge detection, Binary data, Unmanned aerial vehicles, Detection and tracking algorithms, Feature extraction, Image analysis, Image classification

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Proceedings Article | 10 May 2017 Paper

An automated technique for potential differentiation of ovarian mature teratomas from other benign tumours using neural networks classification of 2D ultrasound static images: a pilot study

Dhurgham Al-karawi, A. Sayasneh, Hisham Al-Assam, Sabah Jassim, N. Page, D. Timmerman, T. Bourne, Hongbo Du

Proceedings Volume 10221, 102210F (2017) https://doi.org/10.1117/12.2267278

KEYWORDS: Ultrasonography, Surgery, Neural networks, Tumor growth modeling, Image processing, Image segmentation, Binary data, Diagnostics, Image classification, Tissues, Performance modeling, Computer aided diagnosis and therapy

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Proceedings Article | 10 May 2017 Presentation + Paper

Automatic pelvis segmentation from x-ray images of a mouse model

Omar Al Okashi, Hongbo Du, Hisham Al-Assam

Proceedings Volume 10221, 1022108 (2017) https://doi.org/10.1117/12.2264803

KEYWORDS: Image segmentation, X-rays, X-ray imaging, Mouse models, Biological research, Quantitative analysis, Image processing algorithms and systems, Image processing, Bone, Image filtering, Binary data, Image enhancement

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Proceedings Article | 10 May 2017 Paper

Using trainable segmentation and watershed transform for identifying unilocular and multilocular cysts from ultrasound images of ovarian tumour

Dheyaa Ahmed Ibrahim, Hisham Al-Assam, Hongbo Du, Sabah Jassim

Proceedings Volume 10221, 102210B (2017) https://doi.org/10.1117/12.2267468

KEYWORDS: Image segmentation, Ultrasonography, Computing systems, Image analysis, Diagnostics, Machine learning, Image processing algorithms and systems, Medical imaging, Neural networks, Ovarian cancer, Image processing, Evolutionary algorithms, Binary data, Tumor growth modeling, Cancer

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Proceedings Article | 19 May 2016 Paper

Facial expression identification using 3D geometric features from Microsoft Kinect device

Dongxu Han, Naseer Al Jawad, Hongbo Du

Proceedings Volume 9869, 986903 (2016) https://doi.org/10.1117/12.2223029

KEYWORDS: Feature extraction, Facial recognition systems, Databases, Eye, 3D modeling, Nose, Distance measurement, Gold, Analytical research, Pattern recognition

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Proceedings Article | 19 May 2016 Paper

Automatic segmentation and measurements of gestational sac using static B-mode ultrasound images

Dheyaa Ahmed Ibrahim, Hisham Al-Assam, Hongbo Du, Jessica Farren, Dhurgham Al-karawi, Tom Bourne, Sabah Jassim

Proceedings Volume 9869, 98690B (2016) https://doi.org/10.1117/12.2224514

KEYWORDS: Image segmentation, Ultrasonography, Speckle, Image filtering, Diagnostics, Image enhancement, Image processing, Computing systems, Feature extraction, Tissues

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Proceedings Article | 22 May 2014 Paper

Effectiveness of image features and similarity measures in cluster-based approaches for content-based image retrieval

Hongbo Du, Hanan Al-Jubouri, Harin Sellahewa

Proceedings Volume 9120, 912008 (2014) https://doi.org/10.1117/12.2057721

KEYWORDS: Image retrieval, Feature extraction, Visualization, Image segmentation, Image processing, Expectation maximization algorithms, Databases, Content based image retrieval, RGB color model, Image processing algorithms and systems

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Proceedings Article | 22 May 2014 Paper

A color and texture based multi-level fusion scheme for ethnicity identification

Hongbo Du, Sheerko Hma Salah, Hawkar Ahmed

Proceedings Volume 9120, 91200B (2014) https://doi.org/10.1117/12.2057722

KEYWORDS: RGB color model, Feature extraction, Databases, Image fusion, Facial recognition systems, Binary data, Light sources and illumination, Image processing, Nose, Discrete wavelet transforms

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Proceedings Article | 22 May 2014 Paper

Automatic segmentation and classification of gestational sac based on mean sac diameter using medical ultrasound image

Shan Khazendar, Jessica Farren, Hisham Al-Assam, Ahmed Sayasneh, Hongbo Du, Tom Bourne, Sabah Jassim

Proceedings Volume 9120, 91200A (2014) https://doi.org/10.1117/12.2057720

KEYWORDS: Image segmentation, Ultrasonography, Medical imaging, Image processing, Image enhancement, Image classification, Databases, Image analysis, Diagnostics, Image quality

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Proceedings Article | 2 May 2006 Paper

Content-based video indexing and searching with wavelet transformation

Florian Stumpf, Naseer Al-Jawad, Hongbo Du, Sabah Jassim

Proceedings Volume 6250, 62500Q (2006) https://doi.org/10.1117/12.668365

KEYWORDS: Video, Databases, Biometrics, Wavelets, Visualization, Feature extraction, Distance measurement, Discrete wavelet transforms, Nanoimprint lithography, Imaging systems

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Showing 5 of 18 publications

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