Mhd Saeed Sharif

Artificial neural network-based system for PET volume segmentation

Tumour detection, classification, and quantification in positron emission tomography (PET) imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI) approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs), as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.

An efficient intelligent analysis system for confocal corneal endothelium images

A confocal microscope provides a sequence of images of the corneal layers and structures at different depths from which medical clinicians can extract clinical information on the state of health of the patients cornea. A hybrid model based on snake and particle swarm optimisation (S-PSO) is proposed in this paper to analyse the confocal endothelium images. The proposed system is able to pre-process images (including quality enhancement and noise reduction), detect cells, measure cell densities and identify abnormalities in the analysed data sets. Three normal corneal data sets acquired using a confocal microscope, and three abnormal confocal endothelium images associated with diseases have been investigated in the proposed system. Promising results are presented and the performance of this system is compared with manual and two morphological based approaches. The average differences between the manual and the automatic cell densities calculated using S-PSO and two other morphological based approaches is 5%, 7% and 13% respectively. The developed system will be deployable as a clinical tool to underpin the expertise of ophthalmologists in analysing confocal corneal images.

Rapid prototyping of AES encryption for wireless communication system on FPGA

This paper proposes a wireless communication system for secure transmission of data. Bluetooth is used as the wireless communication medium due to its low-cost and low-power consumption features. Advanced encryption standard (AES) protocol is implemented for the security reason over Bluetooth stack. RC10 prototyping board with Xilinx XC3S1500L-4-FG320 device has been used for the hardware evaluation of the system design. The system is configured to capture frames in real-time from the on-board OminiVision 9650 CMOS camera and transmit these frames securely via Bluetooths connectivity. On reception, the images are decrypted and displayed on the external monitor. The design has also been demonstrated for edge detection over the wireless communication channel. The overall design is evaluated in terms of resource utilisation and maximum operating frequency.

Efficient FPGA implementation of a wireless communication system using Bluetooth connectivity

The development of the security layers between the wireless terminals is one of the biggest trends in wireless communications. Bluetooth can be described as the short range and the low power supplements that holds the connection protocol through various devices. This paper presents the development of a secure wireless connection terminals on a field programmable gate array (FPGA). The wireless connection has been established using Bluetooth technology and the initialisation of a secure algorithm for data exchange is implemented using the advanced encryption standards (AES). The proposed system has been validated and demonstrated using using an image processing application which involves the encryption and decryption of acquired images from the RC10 FPGA prototyping boards cam-era. The evaluation of different building block has been carried out in terms of area, resources used and power consumption.

Medical image classification based on artificial intelligence approaches

A new intelligent system to tackle the main challenges of confocal corneal imaging is developed.This system underpins the expertise of ophthalmologists.It provides clinically useful factors, saves a useful amount of clinician time in the process.It is able to model the stromal keratocyte cells for better evaluation and fast analysis.Early approval by corneal clinicians. Corneal images can be acquired using confocal microscopes which provide detailed views of the different layers inside a human cornea. Some corneal problems and diseases can occur in one or more of the main corneal layers: the epithelium, stroma and endothelium. Consequently, for automatically extracting clinical information associated with corneal diseases, identifying abnormality or evaluating the normal cornea, it is important to be able to automatically recognise these layers reliably. Artificial intelligence (AI) approaches can provide improved accuracy over the conventional processing techniques and save a useful amount of time over the manual analysis time required by clinical experts. Artificial neural networks (ANNs), adaptive neuro fuzzy inference systems (ANFIS) and a committee machine (CM) have been investigated and tested to improve the recognition accuracy of the main corneal layers and identify abnormality in these layers. The performance of the CM, formed from ANN and ANFIS, achieves an accuracy of 100% for some classes in the processed data sets. Three normal corneal data sets and seven abnormal corneal images associated with diseases in the main corneal layers have been investigated with the proposed system. Statistical analysis for these data sets is performed to track any change in the processed images. This system is able to pre-process (quality enhancement, noise removal), classify corneal images, identify abnormalities in the analysed data sets and visualise corneal stroma images as well as each individual keratocyte cell in a 3D volume for further clinical analysis.

Preparation of 2D sequences of corneal images for 3D model building

A confocal microscope provides a sequence of images, at incremental depths, of the various corneal layers and structures. From these, medical practioners can extract clinical information on the state of health of the patients cornea. In this work we are addressing problems associated with capturing and processing these images including blurring, non-uniform illumination and noise, as well as the displacement of images laterally and in the anterior-posterior direction caused by subject movement. The latter may cause some of the captured images to be out of sequence in terms of depth. In this paper we introduce automated algorithms for classification, reordering, registration and segmentation to solve these problems. The successful implementation of these algorithms could open the door for another interesting development, which is the 3D modelling of these sequences.

Artificial neural network-statistical approach for PET volume analysis and classification

The increasing number of imaging studies and the prevailing application of positron emission tomography (PET) in clinical oncology have led to a real need for efficient PET volume handling and the development of new volume analysis approaches to aid the clinicians in the clinical diagnosis, planning of treatment, and assessment of response to therapy. A novel automated system for oncological PET volume analysis is proposed in this work. The proposed intelligent system deploys two types of artificial neural networks (ANNs) for classifying PET volumes. The first methodology is a competitive neural network (CNN), whereas the second one is based on learning vector quantisation neural network (LVQNN). Furthermore, Bayesian information criterion (BIC) is used in this system to assess the optimal number of classes for each PET data set and assist the ANN blocks to achieve accurate analysis by providing the best number of classes. The system evaluation was carried out using experimental phantom studies (NEMA IEC image quality body phantom), simulated PET studies using the Zubal phantom, and clinical studies representative of nonsmall cell lung cancer and pharyngolaryngeal squamous cell carcinoma. The proposed analysis methodology of clinical oncological PET data has shown promising results and can successfully classify and quantify malignant lesions.

An efficient algorithm and architecture for medical image segmentation and tumour detection

Medical image segmentation is very important for radiotherapy planning and cancer diagnosis. There are many techniques for medical image segmentation based on thresholding, classification, and multiresolution analysis (MRA). This paper proposes a system based on MRA and artificial intelligence techniques (AI) for tumour segmentation in DICOM images. The slowest parts of the proposed system have been accelerated using field programmable gate arrays (FPGA). Hardware implementation of Haar wavelet transform based factorization approach (HWTF) on reconfigurable hardware using distributed arithmetic (DA) principles is presented. The developed architecture can be integrated into a system for automatic detection and segmentation of tumour in positron emission tomography (PET) images.

3D Oncological PET volume analysis using CNN and LVQNN

The increasing numbers of patient scans and the prevailing application of positron emission tomography (PET) in clinical oncology have led to a need for efficient PET volume handling and the development of new volume analysis approaches to aid clinicians in the diagnosis of disease and planning of treatment. A novel automated system for oncological PET volume segmentation is proposed in this paper. The proposed intelligent system is using competitive neural network (CNN) and learning vector quantisation neural network (LVQNN) for clustering and quantifying phantom and real PET volumes. Bayesian information criterion (BIC) has been used in this system to assess the optimal number of clusters for each PET data set. The experimental study using phantom PET volume was conducted for quantitative evaluation of the performance of the proposed segmentation algorithm. The analysis of the resulting segmentation of clinical oncological PET data seems to confirm that this approach shows promise and can successfully segment patient lesion.

Actor-Network Theory as a Framework to Analyse Technology Acceptance Model’s External Variables: The Case of Autonomous Vehicles

The main factor for growth in a globalised and highly competitive world is to have an innovative and continuous improvement for the new technologies; however, it is difficult to guarantee the success of such factor without considering the human nature of the people. The Unified Theory of Acceptance and Use of Technology (UTAUT2) is a model that has been used for years to help us understand the drivers of acceptance of new information technologies by its users. This paper presents the Actor-Network Theory (ANT) as a framework to analyse external variables influencing technology acceptance. We have identified a new construct and moderating factor enabling the extension of the UTAUT2. The scenario used to conduct our investigation is the Autonomous Vehicle (AV) which is a disruptive technology and may prove to be the next big evolution in personal transportation. The study was conducted using an anonymous survey, over 410 responses so far, and numerous interviews with experts in the field of sociology, psychology and computer science in order to refine the proposed model. Our research findings reveal not only the usefulness of ANT in developing an understanding the human and non-human actants playing a role in consumer’s behavioural intention of using AV, but ANT also helps us to argue that culture is a direct determinant of behavioural intention and social class is a very important moderating aspect.