Reyer Zwiggelaar

Reyer Zwiggelaar
Aberystwyth University | AU · Department of Computer Science

PhD

About

266
Publications
47,679
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
4,517
Citations
Citations since 2016
54 Research Items
2663 Citations
20162017201820192020202120220100200300400
20162017201820192020202120220100200300400
20162017201820192020202120220100200300400
20162017201820192020202120220100200300400
Introduction

Publications

Publications (266)
Article
Full-text available
Hospital length of stay of patients is a crucial factor for the effective planning and management of hospital resources. There is considerable interest in predicting the LoS of patients in order to improve patient care, control hospital costs and increase service efficiency. This paper presents an extensive review of the literature, examining the a...
Chapter
Full-text available
The primary objective of hospital managers is to establish appropriate healthcare planning and organisation by allocating facilities, equipment and manpower resources necessary for hospital operation in accordance with a patients needs while minimising the cost of healthcare. Length of stay (LoS) prediction is generally regarded as an important mea...
Article
Full-text available
This paper presents a machine learning based approach for the discrimination of malignant and benign microcalcification (MC) clusters in digital mammograms. A series of morphological operations was carried out to facilitate the feature extraction from segmented microcalcification. A combination of morphological, texture, and distribution features f...
Article
Computer-aided diagnosis (CAD) systems can be employed to help classify mammographic microcalcification clusters. In this paper, a novel method for the classification of the microcalcification clusters based on topology/connectivity has been introduced. The proposed method is distinct from existing techniques which concentrate on morphology and tex...
Article
Full-text available
Context and background: Breast cancer is one of the most common diseases threatening the human lives globally, requiring effective and early risk analysis for which learning classifiers supported with automated feature selection offer a potential robust solution. Motivation: Computer aided risk analysis of breast cancer typically works with a se...
Preprint
Full-text available
Current deep learning based detection models tackle detection and segmentation tasks by casting them to pixel or patch-wise classification. To automate the initial mass lesion detection and segmentation on the whole mammographic images and avoid the computational redundancy of patch-based and sliding window approaches, the conditional generative ad...
Conference Paper
Full-text available
Prostate cancer is the second most commonly diagnosed cancer among men and currently multi-parametric MRI is a promising imaging technique used for clinical workup of prostate cancer. Accurate detection and localisation of the prostate tissue boundary on various MRI scans can be helpful for obtaining a region of interest for Computer Aided Diagnosi...
Conference Paper
Full-text available
We propose a novel method for the classification of benign and malignant micro-calcifications using a multi-scale tree-based modelling approach. By taking the connectivity between individual calcifications into account, micro-calcification trees were build at multiple scales along with the extraction of several trees related micro-calcification fea...
Article
This guest editorial introduces the special section on Advances in Breast Imaging.
Article
Full-text available
This paper presents a method for automatic breast pectoral muscle segmentation in mediolateral oblique mammograms using a Convolutional Neural Network (CNN) inspired by the Holistically-nested Edge Detection (HED) network. Most of the existing methods in the literature are based on hand-crafted models such as straight-line, curve-based techniques o...
Article
Full-text available
Breast density is considered to be one of the major risk factors in developing breast cancer. High breast density can also affect the accuracy of mammographic abnormality detection due to the breast tissue characteristics and patterns. We reviewed variants of local binary pattern descriptors to classify breast tissue which are widely used as textur...
Article
Multistage processing of automated breast ultrasound lesions recognition is dependent on the performance of prior stages. To improve the current state of the art, we propose the use of end-to-end deep learning approaches using fully convolutional networks (FCNs), namely FCN-AlexNet, FCN-32s, FCN-16s, and FCN-8s for semantic segmentation of breast l...
Book
This book constitutes the thoroughly refereed post-conference proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018, held in Funchal, Madeira, Portugal, in January 2018. The 25 revised full papers presented were carefully reviewed and selected from a total of 299 submissions. The pap...
Chapter
Mammographic tissue density is considered to be one of the major risk factors for developing breast cancer. In this paper we use quantitative measurements of Local Binary Patterns and its variants for breast tissue classification. We compare the classification results of LBP, ELBP, Uniform ELBP and M-ELBP for classifying mammograms as fatty, glandu...
Chapter
Early detection of microcalcification (MC) clusters plays a crucial role in enhancing breast cancer diagnosis. Two automated MC cluster segmentation techniques are proposed based on morphological operations that incorporate image decomposition and interpolation methods. For both approaches, initially the contrast between the background tissue and M...
Chapter
Full-text available
We present a novel approach for classifying the Gleason score for prostate tumours based on MRI data. Proposed approach uses three scores: 2, 3 and 4–5 (representing Gleason scores 4 and 5 as one single class). Patches are extracted from annotated MRI data for each of the class. Raw image patches have been used as features, instead of extracting ma...
Article
Full-text available
Considering the importance of early diagnosis of breast cancer, a supervised patch-wise texton-based approach has been developed for the classification of mass abnormalities in mammograms. The proposed method is based on texture-based classification of masses in mammograms and does not require segmentation of the mass region. In this approach, patc...
Conference Paper
Full-text available
We present a novel approach for classifying the Gleason score for prostate tumours based on MRI data. Proposed approach uses three scores: 2, 3 and 4-5 (representing Gleason scores 4 and 5 as one single class). Patches are extracted from annotated MRI data for each of the class. Raw image patches have been used as features, instead of extracting ma...
Conference Paper
Full-text available
Classification of benign and malignant masses in mammograms is a challenging problem. It has wide applications in the development of Computer Aided Diagnosis (CAD) systems, however many challenges still need to be addressed. Due to the risk associated with segmenting the mass region, focus is shifting from selecting the features just from the mass...
Article
Full-text available
Computer Aided Detection (CAD) systems are being developed to assist radiologists in diagnosis. For breast cancer the emphasis is shifting from detection to classification of abnormalities. The presented work concentrates on the benign versus malignant classification of micro-calcification clusters, which are a specific type of mammographic abnorma...
Article
Recent improvements in biomedical image analysis using deep learning based neural networks could be exploited to enhance the performance of Computer Aided Diagnosis (CAD) systems. Considering the importance of breast cancer worldwide and the promising results reported by deep learning based methods in breast imaging, an overview of the recent state...
Conference Paper
Breast cancer continues to be the most common type of cancer among women. Early detection of breast cancer is key to effective treatment. The presence of clusters of fine, granular microcalcifications in mammographic images can be a primary sign of breast cancer. The malignancy of any cluster of microcalcification cannot be reliably determined by r...
Article
Full-text available
Breast cancer is one of the major causes of death in women. Computer Aided Diagnosis (CAD) systems are being developed to assist radiologists in early diagnosis. Micro-calcifications can be an early symptom of breast cancer. Besides detection, classification of micro-calcification as benign or malignant is essential in a complete CAD system. We hav...
Conference Paper
Full-text available
The mitosis count, one of the main components considered for grading breast cancer on histology images, is used to assess tumour proliferation. On breast histology sections, different coloured stains are used to highlight existing cellular components. To keep the wealth of information in the stain colour representation and decrease the sensitivity...
Conference Paper
Full-text available
Formal concept analysis is a powerful tool in analyzing data and extracting rules from the formal context. The main framework of formal concept analysis is concept lattice which essentially describes the relationship between the objects and the attributes. And each node of the concept lattice is a formal concept. When processing the uncertain infor...
Article
Purpose: Automated and accurate tissue classification in 3D brain Magnetic Resonance images is essential in volumetric morphometry or as a preprocessing step for diagnosing brain diseases. However, noise, intensity inhomogeneity and partial volume effects limit the classification accuracy of existing methods. This paper provides a comparative stud...
Article
Full-text available
Breast lesion detection using ultrasound imaging is considered an important step of Computer-Aided Diagnosis systems. Over the past decade, researchers have demonstrated the possibilities to automate the initial lesion detection. However, the lack of a common dataset impedes research when comparing the performance of such algorithms. This paper pro...
Conference Paper
Multiparametric magnetic resonance imaging (mp-MRI) has shown its potential in prostate cancer detection. In this study, we investigate the application of 3D texton based prostate cancer detection using T2-weighted (T2W) MRI, dynamic contrast-enhanced (DCE) MRI and apparent diffusion coefficient (ADC) maps. For the T2W and ADC modalities, the tradi...
Conference Paper
Full-text available
Along with the recent improvement in medical image analysis, exploring deep learning based approaches in the context of mammography image processing has become more realistic. In this paper, we concatenate on both conventional machine learning and deep learning approaches to classify mass abnormalities in mammographic images. Using a deep convoluti...
Conference Paper
Classification of benign and malignant masses in mammograms is a complex task due to the appearance similarities in both classes. Thus, classification of masses in mammograms is considered an important step in the development of current Computer Aided Diagnosis (CAD) systems. In this paper, we present a way to classify masses without the need for s...
Article
Breast density, defined as the proportion of fibroglandular tissue over the entire breast has been linked with a higher risk of develop- ing breast cancer, in fact it has been suggested that women with a mammographic breast density higher than 75 percent have a four-to six-fold higher risk of developing breast cancer than women with little or no de...
Article
Full-text available
This study aims to investigate the effects of window size on the performance of prostate cancer CAD and to identify discriminant texture descriptors in prostate T2-W MRI. For this purpose we extracted 215 texture features from 418 T2-W MRI images and extracted them using 9 different window sizes (3 × 3 to 19 × 19). The Bayesian Network and Random F...
Article
Full-text available
It has been shown that breast density and parenchymal patterns are significant indicators in mammographic risk assessment. In addition, studies have shown that the sensitivity of computer aided tools decreases significantly with increase in breast density. As such, mammographic density estimation and classification plays an important role in CAD sy...
Conference Paper
We have investigated the classification of micro-calcification clusters in mammograms by combining two existing approaches. One of the approaches involves extracting and using topological information (connectivity) about micro-calcification clusters as feature vectors to classify them as being benign or malignant. The other approach involves extrac...
Article
Full-text available
Purpose:In this paper the authors propose a texton based prostate computer aided diagnosis approach which bypasses the typical feature extraction process such as filtering and convolution which can be computationally expensive. The study focuses the peripheral zone because 75% of prostate cancers start within this region and the majority of prostat...
Article
Background and objectives: Automatic brain structures segmentation in magnetic resonance images has been widely investigated in recent years with the goal of helping diagnosis and patient follow-up in different brain diseases. Here, we present a review of the state-of-the-art of automatic methods available in the literature ranging from structure...
Article
Full-text available
In this paper we propose a prostate cancer computer-aided diagnosis (CAD) system and suggest a set of discriminant texture descriptors extracted from T2-weighted MRI data which can be used as a good basis for a multimodality system. For this purpose, 215 texture descriptors were extracted and eleven different classifiers were employed to achieve th...
Conference Paper
Mammography is one of the most effective techniques for early detection of breast cancer. The quality of the image may suffer from poor resolution or low contrast, which can effect the efficiency of radiologists. In order to improve the visual quality of mammograms, this paper introduces a new mammographic image enhancement algorithm. Firstly an in...
Conference Paper
It has been shown that breast density and parenchymal patterns are important indicators in mammographic risk assessment. In addition, the accuracy of detecting abnormalities depends strongly on the structure and density of breast tissue. As such, mammographic parenchymal modelling and the related density estimation or classification are playing an...
Conference Paper
This paper investigates the use of mereotopological barcodes to help non-experts classify microcalcification clusters as either benign or malignant. When compared against classification using the microcalcification cluster segmentation maps, the use of barcodes is able to see a significant improvement in classification performance with the AUC sign...
Conference Paper
Breast cancer is the most frequently diagnosed cancer in women. To date, the exact cause(s) of breast cancer still remains unknown. The most effective way to tackle the disease is early detection through breast screening programmes. Breast density is a well established image based risk factor. An accurate dense breast tissue segmentation can play a...
Article
3D human pose estimation is a very difficult task. In this paper we propose that this problem can be more easily solved by first finding the solutions to a set of easier sub-problems. These are to locally estimate pose conditioned on a fixed root node state, which defines the global position and orientation of the person. The global solution can th...
Article
Full-text available
On behalf of the Organizing committee of International Conference on Engineering and Technology for Sustainable Development 2015 (ICET4SD 2015), we would like to express our gratitude and welcome all participants to the beautiful Yogyakarta, Indonesia. This conference had been held for the first time by the Faculty of Industrial Technology Universi...
Article
Full-text available
The need to reduce the dimensionality of a dataset whilst retaining its inherent manifold structure is key to many pattern recognition, machine learning, and computer vision problems. This process is often referred to as manifold learning since the structure is preserved during dimensionality reduction by learning the intrinsic low-dimensional mani...
Article
During mammographic image acquisition, a compression paddle is used to even the breast thickness in order to obtain optimal image quality. Clinical observation has indicated that some mammograms may exhibit abrupt intensity change and low visibility of tissue structures in the breast peripheral areas. Such appearance discrepancies can affect image...
Conference Paper
The accurate modelling of texture appearance in mammographic images is an important step for many automated mammographic risk classification approaches. However, the inherent high-dimensional nature of such texture based models can lead to sub-optimal classification performance. The work in this paper uses Random Projections to reduce the dimension...
Conference Paper
In this paper, a new unsupervised approach is proposed for the segmentation of Multiple Sclerosis (MS) lesions in multimodality Magnetic Resonance (MR) images. The proposed segmentation scheme is based on joint histogram modelling followed by false positive reduction and alpha matting, which is used to deal with the tissue density overlap problem a...
Article
Full-text available
We propose a methodology for prostate cancer detection and localisation within the peripheral zone based on combining multiple segmentation techniques. We extract four image features using Gaussian and median filters. Subsequently, we use each image feature separately to generate binary segmentations. Finally, we take the intersection of all four b...
Conference Paper
Full-text available
Many studies have reported the limitations of computer-aided diagnosis systems using a single T2-W MRI which include weak texture descriptors and an extensive amount of noise. Therefore, researchers have used multiparametric MRI to improve the performances of their methods. We propose a computer-aided diagnosis (CADx) method for prostate cancer wit...
Conference Paper
Full-text available
In this paper, we present our preliminary results classifying benign and malignant tissues within the prostate peripheral zone using textons. For this purpose, patches are randomly extracted from malignant and benign regions and we perform k-means clustering to generate textons. All textons are combined to form the texton dictionary which was used...
Article
Full-text available
Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susc...
Article
Breast cancer is the most frequently diagnosed cancer in women. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective ways to overcome the disease. Successful mammographic density segmentation is a key aspect in deriving correct tissue composition, ensuring...