Abbas Cheddad

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• PhD (SMIEEE - ACM Distinguished Speaker)
• Professor (Associate) at University of Tartu

Mammographic percent density is an established marker of breast cancer risk. In a study of screen film mammograms we recently reported a novel feature from the pectoral muscle region to be associated with breast cancer risk independently of area percent density. We now investigate whether our novel feature is associated with risk in a study based o...

Mammographic density, the white radiolucent part of a mammogram, is a marker of breast cancer risk and mammographic sensitivity. There are several means of measuring mammographic density, among which are area-based and volumetric-based approaches. Current volumetric methods use only unprocessed, raw, mammograms, which is a problematic restriction s...

Background: Mammographic density is a strong risk factor for breast cancer. Methods: We present a novel approach to enhance area density measures that takes advantage of the relative density of the pectoral muscle that appears in lateral mammographic views. We hypothesized that the grey scale of film mammograms is normalized to volume breast den...

There exist several algorithms that deal with text encryption. However, there has been little research carried out to date on encrypting digital images or video files. This paper describes a novel way of encrypting digital images with password protection using 1D SHA-2 algorithm coupled with a compound forward transform. A spatial mask is generated...

Since it was first presented in 2002, optical projection tomography (OPT) has emerged as a powerful tool for the study of biomedical specimen on the mm to cm scale. In this paper, we present computational tools to further improve OPT image acquisition and tomographic reconstruction. More specifically, these methods provide: semi-automatic and preci...

In this paper, we investigate the current state and development of personalized smart immersive extended reality environments (PSI-XR). PSI-XR has gained increasing traction across various fields such as education, entertainment, and healthcare, offering customized immersive experiences that address users’ personalized needs. This study performs a...

This study introduces FLAME (Fault Localization using Augmented Model Enhancement), a novel fault diagnosis framework for District Heating (DH) substations. Automated Fault Detection and Diagnosis (FDD) has become imperative as many DH substations perform sub-optimal due to faults. The main challenges complicating accurate fault diagnosis are incre...

This study introduces FLAME (Fault Localization using Augmented Model Enhancement), a novel fault diagnosis framework for District Heating (DH) substations. Automated Fault Detection and Diagnosis (FDD) has become imperative as many DH substations perform sub-optimal due to faults. The main challenges complicating accurate fault diagnosis are incre...

Personality trait identification through handwriting analysis presents a challenging area within automated document recognition based on Artificial Intelligence solutions. Recent studies relied on solutions automating graphonomic processes, while others address only a few local features, conversely few studies offer solutions based on textural feat...

Vowel-based voice analysis is gaining attention as a potential non-invasive tool for COPD classification, offering insights into phonatory function. The growing need for voice data has necessitated the adoption of various techniques, including segmentation, to augment existing datasets for training comprehensive Machine Learning (ML) modelsThis stu...

Diabetic retinopathy (DR) is a leading cause of blindness worldwide, necessitating early detection to prevent severe visual impairment. Despite numerous proposed classification techniques, challenges persist due to the high parameter count of deep learning algorithms, imbalanced datasets, and limited performance. This study introduces a novel frame...

District Heating (DH) systems are essential for energy-efficient urban heating. However, despite the advancements in automated fault detection and diagnosis (FDD), DH still faces challenges in operational faults that impact efficiency. This study introduces the Shared Nearest Neighbor Enhanced District Heating Anomaly Detection (SHEDAD) approach, d...

This book covers the Proceedings of the 13th International Conference on Information Systems and Advanced Technologies “ICISAT’2023.” One of the evocative and valuable dimensions of this conference is the way it brings together researchers, scientists, academics, and engineers in the field from different countries and enables discussions and debate...

Background Chronic obstructive pulmonary disease (COPD) is a severe condition affecting millions worldwide, leading to numerous annual deaths. The absence of significant symptoms in its early stages promotes high underdiagnosis rates for the affected people. Besides pulmonary function failure, another harmful problem of COPD is the systematical ef...

13th International Conference on Information Systems and Advanced Technologies “ICISAT 2023” New Trends in Artificial Intelligence, Computing and Decision Making. Volume 2

Recently, there has been a substantial surge in interest surrounding diffusion models, which are considered a prominent class of generative models. This surge is primarily attributed to their potential applications in a variety of deep learning problems. The primary objective of this study is to assess the effectiveness of diffusion models as a dat...

Brain tumor segmentation, among various tasks in medical image analysis, has garnered significant attention in the research community. Despite continuous efforts by researchers, accurate brain tumor segmentation remains a key challenge. This challenge arises due to various factors, including location uncertainty, morphological uncertainty, low cont...

This study developed a procedure for rapidly reconstructing a crack profile for calculating the parameters of fracture mechanics such as stress intensity factor with energy release rate (J) and displacement opening crack tip using data from the eddy current sensor. The inverse problem focused on adopting genetic algorithms to solve the direct probl...

Digital audio signal reconstruction of a lost or corrupt segment using deep learning algorithms has been explored intensively in recent years. Nevertheless, prior traditional methods with linear interpolation, phase coding and tone insertion techniques are still in vogue. However, we found no research work on reconstructing audio signals with the f...

During the last decade, we have witnessed a rapid development of extended reality (XR) technologies such as augmented reality (AR) and virtual reality (VR). Further, there have been tremendous advancements in artificial intelligence (AI) and machine learning (ML). These two trends will have a significant impact on future digital societies. The visi...

This article presents an interactive method for 3D cloud animation at the landscape scale by employing machine learning. To this end, we utilize deep convolutional generative adversarial network (DCGAN) on GPU for training on home-captured cloud videos and producing coherent animation frames. We limit the size of input images provided to DCGAN, the...

Amidst the outbreak of the coronavirus (COVID 19) pandemic, distance education, where the learning process is conducted online, has become the norm. Campus-based programs and courses have been redesigned in a timely manner which was a challenge for teachers not used to distance teaching. Students engagement and active participation become an issue;...

Mouse models for streptozotocin (STZ) induced diabetes probably represent the most widely used systems for preclinical diabetes research, owing to the compound’s toxic effect on pancreatic β-cells. However, a comprehensive view of pancreatic β-cell mass distribution subject to STZ administration is lacking. Previous assessments have largely relied...

Large amount of data are generated from in-situ monitoring of additive manufacturing (AM) processes which is later used in prediction modelling for defect classification to speed up quality inspection of products. A high volume of this process data is defect-free (majority class) and a lower volume of this data has defects (minority class) which re...

Due to the exponential growth of medical information in the form of, e.g., text, images, Electrocardiograms (ECGs), X-ray, multimedia, etc., the management of a patient’s data has become a huge challenge. Particularly, the extraction of features from various different formats and their representation in a homogeneous way are areas of particular int...

Introduction to the special section on intelligent systems and pattern recognition (SS:ISPR20) 1. Introduction and Background Intelligent systems and pattern recognition have cooperatively benefited different scientific fields. We can exemplify this importance by just mentioning the research in face detection and recognition, medical diagnosis and...

This paper presents a digital image dataset of historical handwritten birth records stored in the archives of several parishes across Sweden, together with the corresponding metadata that supports the evaluation of document analysis algorithms' performance. The dataset is called SHIBR (the Swedish Historical Birth Records). The contribution of this...

In this paper, we present a hybrid and interdisciplinary approach for the calculation of argument strength, based on the coupling of two existing frameworks-the emerging Argument Recognition (eAR) framework of: (1) the emerging Named Entity Recognition Information Retrieval System (eNER-IRS) for textual medical articles, and (2) the Generic Multime...

This paper aims to address data labelling issues in process data to support in-situ process monitoring of additive manufactured components. For this, we adopted an active learning (AL) approach to minimise the manual effort for data labelling for classification models. In this study, we present an approach that utilises pre-trained models to extrac...

The plethora of digitalised historical document datasets released in recent years has rekindled interest in advancing the field of handwriting pattern recognition. In the same vein, a recently published data set, known as ARDIS, presents handwritten digits manually cropped from 15.000 scanned documents of Swedish church books and exhibiting various...

Nowadays, the field of multimedia retrieval system has earned a lot of attention as it helps retrieve information more efficiently and accelerates daily tasks. Within this context, image processing techniques such as layout analysis and word recognition play an important role in transcribing content in printed or handwritten documents into digital...

The plethora of digitalised historical document datasets released in recent years has rekindled interest in advancing the field of handwriting pattern recognition. In the same vein, a recently published data set, known as ARDIS, presents handwritten digits manually cropped from 15.000 scanned documents of Swedish church books and exhibiting various...

Machine learning (ML) has an impressive capacity to learn and analyze a large volume of data. This study aimed to train different algorithms to discriminate between healthy and pathologic corneal images by evaluating digitally processed spectral-domain optical coherence tomography (SD-OCT) corneal images. A set of 22 SD-OCT images belonging to a ra...

Machine learning (ML) has a large capacity to learn and analyze a large volume of data. This study aimed to train different algorithms to discriminate between healthy and pathologic corneal images by evaluating digitally processed spectral-domain optical coherence tomography (SD-OCT) corneal images. A set of 22 SD-OCT images belonging to a random s...

This paper provides insights into a workflow of different applications of machine learning coupled with image analysis in the healthcare sector which we have undertaken. As case studies, we use personalized breast cancer screenings and diabetes research (i.e., Beta-cell mass quantification in mice and diabetic retinopathy analysis). Our tools play...

The management of PCa is dependent on biomarkers of biological aggression. This includes an invasive biopsy to facilitate a histopathological assessment of the tumour’s Gleason score. This review explores the technical processes of applying radiomics to the evaluation of PCa. By exploring how a deep radiomics approach further optimizes the predicti...

The larger and complete Mini-DDSM data set is uploaded (45 GB), you can get it 👍 https://www.kaggle.com/cheddad/miniddsm2 It required a tremendous time, coding and machine processing power to get it in shape, below are some of the merits of this new Mini-DDSM version: 1. The intention here is to make an easy access to the DDSM (half resolution th...

One of the crucial aspects of additive manufacturing is the monitoring of the welding process for quality assurance of components. A common way to analyse the welding process is through visual inspection of melt-pool images to identify possible defects in manufacturing. Recent literature studies showed the potential use of prediction models for def...

This paper introduces a new image-based handwritten historical digit dataset named Arkiv Digital Sweden (ARDIS). The images in ARDIS dataset are extracted from 15,000 Swedish church records which were written by different priests with various handwriting styles in the nineteenth and twentieth centuries. The constructed dataset consists of three sin...

Age estimation has attracted attention for its various medical applications. There are many studies on human age estimation from biomedical images. However, there is no research done on mammograms for age estimation, as far as we know. The purpose of this study is to devise an AI-based model for estimating age from mammogram images. Due to lack of...

This paper provides insights into a workflow of different applications of machine learning coupled with image analysis in the healthcare sector which we have undertaken. As case studies, we use personalized breast cancer screenings and diabetes research (i.e., Beta-cell mass quantification in mice and diabetic retinopathy analysis). Our tools play...

A unique member of the power transformation family is known as the Box-Cox transformation. The latter can be seen as a mathematical operation that leads to finding the optimum lambda (λ) value that maximizes the log-likelihood function to transform a data to a normal distribution and to reduce heteroscedasticity. In data analytics, a normality assu...

The design of aircraft engines involves computationally expensive engineering simulations. One way to solve this problem is the use of response surface models to approximate the high-fidelity time-consuming simulations while reducing computational time. For a robust design, sensitivity analysis based on these models allows for the efficient study o...

A unique member of the power transformation family is known as the Box-Cox transformation. The latter can be seen as a mathematical operation that leads to finding the optimum lambda ({\lambda}) value that maximizes the log-likelihood function to transform a data to a normal distribution and to reduce heteroscedasticity. In data analytics, a normal...

Diabetic retinopathy is the most common cause of new cases of blindness in people of working age. Early diagnosis is the key to slowing the progression of the disease, thus preventing blindness. Retinal fundus images form an important basis for judging these retinal diseases. To the best of our knowledge, no prior studies have scrutinized the predi...

Purpose: To train different machine learning algorithms to discriminate between healthy and pathologic corneas by evaluating processed anterior segment OCT corneal images (AS-OCT). Methods: Experimental comparative pilot study. Data set included 22 pathologic and 71 healthy images. Image segmentation and a region of interest were selected for each...

In engineering, design analyses of complex products rely on computer simulated experiments. However, high-fidelity simulations can take significant time to compute. It is impractical to explore design space by only conducting simulations because of time constraints. Hence, surrogate modelling is used to approximate the original simulations. Since s...

More details about the datasets can be found: https://ardisdataset.github.io/ARDIS/ https://link.springer.com/article/10.1007/s00521-019-04163-3

Extracting built-up areas from remote sensing data like Landsat 8 satellite is a challenge. We have investigated it by proposing a new index referred as Built-up Land Features Extraction Index (BLFEI). The BLFEI index takes advantage of its simplicity and good separability between the four major component of urban system, namely built-up, barren, v...

This paper proposes a preprocessing stage to augment the bank of features that one can retrieve from binary images to help increase the accuracy of pattern recognition algorithms. To this end, by applying successive dilations to a given shape, we can capture a new dimension of its vital characteristics which we term hereafter: the shape growth patt...

Objective: We provide an e-Science perspective on the workflow from risk factor discovery and classification of disease to evaluation of personalized intervention programs. As case studies, we use personalized prostate and breast cancer screenings. Materials and methods: We describe an e-Science initiative in Sweden, e-Science for Cancer Prevent...

Mathematical morphology has been of a great significance to several scientific fields. Dilation, as one of the fundamental operations, has been very much reliant on the common methods based on the set theory and on using specific shaped structuring elements to morph binary blobs. We hypothesised that by performing morphological dilation while explo...

Background Interval breast cancers are often diagnosed at a more advanced stage than screen-detected cancers. Our aim was to identify features in screening mammograms of the normal breast that would differentiate between future interval cancers and screen-detected cancers, and to understand how each feature affects tumor detectability. Methods From...

Historical documents are essentially formed of handwritten texts that exhibit a variety of perceptual environment complexities. The cursive and connected nature of text lines on one hand and the presence of artefacts and noise on the other hand hinder achieving plausible results using current image processing algorithm. In this paper, we present a...

Vehicular Ad-Hoc Network (VANet) is an emerging research area, it offers a wide range of applications including safety, road traffic efficiency, and infotainment applications. Recently researchers are studying the possibility of making use of deployed VANet applications for data collection. In this case, vehicles are considered as mobile collectors...

The software package, CASAM, that implements my algorithms on mammographic image processing can be ordered now free of charge for non-commercial use. Please fill and sign the agreement letter in the link below and return it (scanned) to the email shown in the document. http://abbascheddad.net/MEB.htm

An important application in wireless networks is data collection. It aims to gather and deliver specific data for concerned authorities. Many researchers invest in vehicular ad hoc networks for that purpose to acquire data from different sources on the roads as from its vicinity. A vehicle is considered as a mobile data collector, it gathers real-t...

There is described a method of encrypting a set of 2D input data, preferably image data. The method comprises obtaining the hash value of a password and re-sizing the hash value to fit the size of the 2D input data. The re-sized data is transformed using an irreversible transform, and the output of the transform is then used to encode the 2D data.

With the advancements in computer-related technologies more data are created in digital form, allowing easy control over the handling, collection, storage, and dissemination of such data. Unfortunately, this progress, in spite of its technical advantages, may have brought with it a great deal of risks, especially the ones related to the security of...

To compare tumor characteristics and risk factors of interval breast cancers and screen-detected breast cancers, taking mammographic density into account. Women diagnosed with invasive breast cancer from 2001 to 2008 in Stockholm, Sweden, with data on tumor characteristics (n = 4,091), risk factors, and mammographic density (n = 1,957) were include...

There is described a method for detecting the presence of skin tone in an image. A gray scale representation of a pixel within the image is provided. Next, a red chrominance independent representation for is provided for the pixel. Then, the two representations are analysed to determine whether a difference in value between the representations corr...

By adapting OPT to include the capability of imaging in the near infrared (NIR) spectrum, we here illustrate the possibility to image larger bodies of pancreatic tissue, such as the rat pancreas, and to increase the number of channels (cell types) that may be studied in a single specimen. We further describe the implementation of a number of comput...

This paper presents a novel dynamic threshold approach to discriminate skin pixels and non-skin pixels in colour images. Fixed decision boundaries (or fixed threshold) classification approaches are successfully applied to detect human skin tone in colour images. These fixed thresholds mostly failed in two situations as they only search for a certai...

The Internet is a widely open source to everyone to access Web pages. Using a web browser anyone can access websites. Because of this facility people can easily download images from websites without the owner's knowledge and use them in their own documents. Also image content may be modified for illegal purposes. Therefore a system is needed to aut...

Optical projection tomography (OPT) imaging is a powerful tool for three-dimensional imaging of gene and protein distribution patterns in biomedical specimens. We have previously demonstrated the possibility, by this technique, to extract information of the spatial and quantitative distribution of the islets of Langerhans in the intact mouse pancre...

Optical projection tomography (OPT) imaging is a powerful tool for three-dimensional imaging of gene and protein distribution patterns in biomedical specimens. We have previously demonstrated the possibility, by this technique, to extract information of the spatial and quantitative distribution of the islets of Langerhans in the intact mouse pancre...

Lossy compression attacks in digital watermarking are one of the major issues in digital watermarking. Cheddad et al. proposed a robust secured self-embedding method which is resistant to a certain amount of JPEG compression. Our experimental results show that the self-embedding method is resistant to JPEG compression attacks and not resistant to o...

This paper presents a novel dynamic threshold approach to discriminate skin pixels and non-skin pixels in color images. Fixed decision boundaries (or fixed threshold) classification approaches are successfully applied to segment human skin. These fixed thresholds mostly failed in two situations as they only search for a certain skin color range: 1)...

Steganography is the science that involves communicating secret data in an appropriate multimedia carrier, e.g., image, audio, and video files. It comes under the assumption that if the feature is visible, the point of attack is evident, thus the goal here is always to conceal the very existence of the embedded data. Steganography has various usefu...

In this paper, the concept of object-oriented embedding (OOE) is introduced into information hiding in general and particularly to steganography which is the science that involves undetectable communication of secret data in an appropriate multimedia carrier. The proposal takes advantage of computer vision to orient the embedding process. Although,...

Challenges face biometrics researchers and particularly those who are dealing with skin tone detection include choosing a colour space, generating the skin model and processing the obtained regions to fit applications. The majority of existing methods have in common the de-correlation of luminance from the considered colour channels. Luminance is u...

The recent digital revolution has facilitated communication, data portability and on-the-fly manipulation. Unfortunately, this has brought along some critical security vulnerabilities that put digital documents at risk. The problem is in the security mechanism adopted to secure these documents by means of encrypted passwords; however, this security...

The majority of existing methods have one thing in common which is the de-correlation of luminance from the considered colour channels. It is believed that the luminance is underestimated here since it is seen as the least contributing colour component to skin colour detection. This work questions this claim by showing that luminance can be useful...

Privacy is freedom from unauthorized intrusion. As the perceived risk from terrorist threat increases so does the justification for increased levels of surveillance, whether online or whilst attending a local park. Is this level of surveillance justified or are we only exchanging one set of freedoms for another. Governments claim that this is neces...

Segmentation of human faces from still images is a research field of rapidly increasing interest. Although the field encounters several challenges, this paper seeks to present a novel face segmentation and facial feature extraction algorithm for gray intensity images (each containing a single face object). Face location and extraction must first be...

This paper proposes a novel encryption method with password protection based on an extended version of SHA-1 (secure hash algorithm) that is able to encrypt 2D bulk data such as images. There has been a modest research in the literature on encryption of digital images though. The algorithm benefits also from the conjugate symmetry exhibited in what...

The recent digital revolution has facilitated communication, data portability and on-the-fly manipulation. Unfortunately, this has brought along some critical security vulnerabilities that put digital documents at risk. The problem is in the security mechanism adopted to secure these documents by means of encrypted passwords; however, this security...

The history of steganography can be traced back to ancient civilization - the Persian and Greek conflict around 480 B.C and ancient Egyptian civilization - when steganography was first reported to exist. Steganography is the process of hiding information in a multimedia carrier. Steganalysis, which is the official counter attack science, has defeat...

Previous studies have shown that Steganography tools such as the S-Tools application outperforms counterpart tools in hiding data in the spatial domain. Another tool called F5 is identified as a robust tool in implementing Steganography in the frequency domain. This paper presents a Steganographic system which exploits the YCbCr colour space. YCbCr...

Steganography is defined as the science of hiding or embedding ";data"; in a transmission medium. Its ultimate objectives, which are undetectability, robustness (i.e., against image processing and other attacks) and capacity of the hidden data (i.e., how much data we can hide in the carrier file), are the main factors that distinguish it from other...

Automatic segmentation of digital images is of utmost importance in many applications especially those related to machine vision, i.e., robotics and vision in the production industry. There exist different algorithms to carry out image segmentation. This paper presents a novel image segmentation algorithm with low computational complexity. The prop...

In this paper, we tackle the problem of 3D reconstruction of human faces from a given stereo pair 2D instances, namely left and right images. The generated 3D model is developed to be the main input for Medical imaging tasks, although the model can be exploited in other fields as well. The algorithm is decomposed into two phases. The first phase de...

Recognition of human faces out of many still images is a research field of fast increasing interest, and a fast and accurate algorithm is still a field to be explored. Our proposed approach in this research is based on Voronoi diagram and contour linearization. We use the former method to locate a probable region of faces and to compensate for some...

Watermarking is the process of embedding watermark into an image such that the embedded watermark can be extracted later. Lossy compression attacks in digital watermarking are one of the major issues in digital watermarking. Cheddad et al. proposed a robust secured self-embedding method which is resistant to a certain amount of JPEG compression. Ou...

Digital video has become increasingly susceptible to spatio-temporal manipulations as a result of recent ad-vances in video editing tools. Therefore it is difficult to get video records to stand up as 100% secure evidence in court, example for a criminal evidence. In this paper, we propose a novel digital video authen-tication approach based on a s...

Feature extraction (this includes face segmentation because the face itself is considered a global feature) is a very useful step in face recognition and in pattern matching in general. The goodness of a particular feature extraction method is judged on the basis of how much it is efficient and accurate in discrimination between objects of interest...