Dr. Alaa AlZoubi Profile

Dr. Alaa AlZoubi

at Univ of Buckingham

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Publications (7)

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

Land cover analysis using satellite imagery for humanitarian mine action and ERW survey

Nasiru Ibrahim, Shathel Fahs, Alaa AlZoubi

Proceedings Volume 11734, 1173402 (2021) https://doi.org/10.1117/12.2589792

KEYWORDS: Satellites, Satellite imaging, Mining, Earth observing sensors, Image analysis, Land mines, Vegetation, Safety, Remote sensing, Product safety

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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 | 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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