Ahmad Fadzil M. Hani

Missing Attribute Value Prediction Based on Artificial Neural Network and Rough Set Theory

In this research, artificial neural network (ANN) combined with rough set theory (RST), named as ANNRST, is proposed to predict missing values of attribute. The prediction of missing values of attribute is applied on heart disease data from UCI datasets. The ANN used is multilayer perceptron (MLP) with resilient back-propagation learning. RST can reduce the dimensionality of attributes through its reduct. Reduct is used as input of ANN combined with decision attribute. By simulating of missing values, the prediction accuracy of ANN is compared to ANNRST. The accuracy of ANNRST is also compared with missing data imputation ofk-Nearest Neighbor (k-NN), most common attribute value method and ANN with piecewise linear network-orthonormal least square feature selection (PLN-OLS). Simulation results show that ANNRST can predict the missing value with maximum accuracy close to ANN without dimensionality reduction (pure ANN) and outperform k-NN, most common attribute value method, and ANN with PLN-OLS.

Renegotiation in Service Level Agreement Management for a Cloud-Based System

Managing Service Level Agreement (SLA) within a cloud-based system is important to maintain service continuity and improve trust due to cloud flexibility and scalability. We conduct a general review on cloud-based systems to understand how service continuity and trust are addressed in cloud SLA management. The review shows that SLA renegotiation is necessary to improve trust and maintain service continuity; however, research on SLA renegotiation is limited. Of the two key approaches in renegotiation, namely bargaining-based negotiation and offer generation--based negotiation, the latter approach is the most promising due to its ability to generate optimized multiple-offer SLA parameters within one round during renegotiation.

Non-invasive and in vivo assessment of osteoarthritic articular cartilage: a review on MRI investigations

Early detection of knee osteoarthritis (OA) is of great interest to orthopaedic surgeons, rheumatologists, radiologists, and researchers because it would allow physicians to provide patients with treatments and advice to slow the onset or progression of the disease. Early detection can be achieved by identifying early changes in selected features of degenerative articular cartilage (AC) using non-invasive imaging modalities. Magnetic resonance imaging (MRI) is becoming the standard for assessment of OA. The aim of this paper was to review the influence of MRI on the selection, detection, and measurement of AC features associated with early OA. Our review of the literature indicates that the changes associated with early OA are in cartilage thickness, cartilage volume, cartilage water content, and proteoglycan content that can be accurately, consistently, and non-invasively measured using MRI. Choosing an MR pulse sequence that provides the capability to assess cartilage physiology and morphology in a single acquisition and advanced multi-nuclei MRI is desirable. The results of the review indicate that using an ultra-high magnetic strength, MR imager does not affect early OA detection. In conclusion, MRI is currently the most suitable modality for early detection of knee OA, and future research should focus on the quantitative evaluation of early OA features using advances in MR hardware, software, and data processing with sophisticated image/pattern recognition techniques.

Physiological assessment of in vivo human knee articular cartilage using sodium MR imaging at 1.5 T

Osteoarthritis is a common joint disorder that is most prevalent in the knee joint. Knee osteoarthritis (OA) can be characterized by the gradual loss of articular cartilage (AC). Formation of lesion, fissures and cracks on the cartilage surface has been associated with degenerative AC and can be measured by morphological assessment. In addition, loss of proteoglycan from extracellular matrix of the AC can be measured at early stage of cartilage degradation by physiological assessment. In this case, a biochemical phenomenon of cartilage is used to assess the changes at early degeneration of AC. In this paper, a method to measure local sodium concentration in AC due to proteoglycan has been investigated. A clinical 1.5-T magnetic resonance imaging (MRI) with multinuclear spectroscopic facility is used to acquire sodium images and quantify local sodium content of AC. An optimised 3D gradient-echo sequence with low echo time has been used for MR scan. The estimated sodium concentration in AC region from four different data sets is found to be ~225±19mmol/l, which matches the values that has been reported for the normal AC. This study shows that sodium images acquired at clinical 1.5-T MRI system can generate an adequate quantitative data that enable the estimation of sodium concentration in AC. We conclude that this method is potentially suitable for non-invasive physiological (sodium content) measurement of articular cartilage.

Gaussian Bayes classifier for medical diagnosis and grading: Application to diabetic retinopathy

Data from medical imaging system need to be analysed for diagnostics and clinical purposes. In a computerized system, the analysis normally involves classification process to determine disease and its condition. In an earlier work based on a database of 315 fundus images (FINDeRS), it is found that the foveal avascular zone (FAZ) enlargement strongly correlates with diabetic retinopathy (DR) progression having a correlation factor up to 0.883 at significant levels better than 0.01. However, it is also found that the FAZ areas can belong to different DR severity but with different levels of certainty having a Gaussian distribution. In this research work, the suitability of the Gaussian Bayes classifier in determining DR severity level is investigated. A v-fold cross-validation (VFCF) process is applied to the FINDeRS database to evaluate the performance of the classifier. It is shown that the classifier achieved sensitivity of >84%, specificity of >97% and accuracy of >95% for all DR stages. At high values of sensitivity (>95%), specificity (>97%) and accuracy (>98%) obtained for No DR and Severe NPDR/PDR stages, the Gaussian Bayes classifier is suitable as part of a computerised DR grading and monitoring system for early detection of DR and for effective treatment of severe cases.

Missing data estimation on heart disease using Artificial Neural Network and Rough Set Theory

The objective of this research is to implement a method for estimating the real missing data in heart disease datasets and to show how it affects the resulting knowledge. Missing data is common problem in knowledge discovery from database (KDD) processes that can lead significant error in extracted knowledge. We use hybridization of artificial neural network and rough set theory (ANNRST) to estimate the real missing data on heart disease from UCI (University of California, Irvine) datasets. ANN with reduced input features is used to estimate the missing data. RST is used to reduce the dimensionality of input features and to extract the knowledge as reducts and rules from heart disease datasets with estimated missing data. RST, decomposition tree, local transfer function classifier (LTF-C) and k-nearest neighbor (k-NN) classifier are used to calculate the accuracy. Comparative study with k-NN estimation, most common attribute value filling and deletion of missing data are made to evaluate the extracted knowledge. ANNRST can be considered as the appropriate estimation method when strong relationship between original complete datasets and estimated datasets is important (the estimated datasets really represent the nature of original complete datasets) as it gives the best accuracy and coverage for almost all the classifiers.

Local gray level S-curve transformation A generalized contrast enhancement technique for medical images

Most medical images suffer from inadequate contrast and brightness, which leads to blurred or weak edges (low contrast) between adjacent tissues resulting in poor segmentation and errors in classification of tissues. Thus, contrast enhancement to improve visual information is extremely important in the development of computational approaches for obtaining quantitative measurements from medical images. In this research, a contrast enhancement algorithm that applies gray-level S-curve transformation technique locally in medical images obtained from various modalities is investigated. The S-curve transformation is an extended gray level transformation technique that results into a curve similar to a sigmoid function through a pixel to pixel transformation. This curve essentially increases the difference between minimum and maximum gray values and the image gradient, locally thereby, strengthening edges between adjacent tissues. The performance of the proposed technique is determined by measuring several parameters namely, edge content (improvement in image gradient), enhancement measure (degree of contrast enhancement), absolute mean brightness error (luminance distortion caused by the enhancement), and feature similarity index measure (preservation of the original image features). Based on medical image datasets comprising 1937 images from various modalities such as ultrasound, mammograms, fluorescent images, fundus, X-ray radiographs and MR images, it is found that the local gray-level S-curve transformation outperforms existing techniques in terms of improved contrast and brightness, resulting in clear and strong edges between adjacent tissues. The proposed technique can be used as a preprocessing tool for effective segmentation and classification of tissue structures in medical images.

Development of private cloud storage for medical image research data

Medical imaging research deals with large, heterogeneous and fragmented amounts of medical images. Such challenges lead to the need for secure, federated and functional medical image storage within this research community. With limited IT professional and research fund, the community is better provided with an outsourcing arrangement. Cloud computing promises lower cost, high scalability and availability which can be a natural solution such problems in long-term medical image archive. This paper presents a private cloud storage design and prototype development within an organization to solve such issues. Leveraging on the ability of cloud computing is shown meet to the system requirements. The prototype is implemented on OwnCloud cloud storage framework. The complete functionality of OwnCloud made it an ideal platform to develop and deploy this kind of cloud-based system. OwnCloud can keep images in different file formats and share such images to other researchers in simple way.

Enhancement of colour fundus image and FFA image using RETICA

Diabetic Retinopathy (DR) is a vision loss impairment due to complications arising from the diabetic condition and affects the retina and resulting pathologies can be monitored by analysing the colour fundus image. However, in retinal fundus images, the contrast between the retinal vasculature and the background is very low and varies within the image making visualisation and analysis of small retinal vasculatures difficult. Therefore, enhancement of the fundus image is important to provide the best visualization of the retinal blood vessels. Fluorescein angiogram overcomes this imaging problem but it is invasive and leads to other physiological problems. In this research work, a non-invasive digital image enhancement technique called RETICA has been developed that overcomes the problem of varied and low contrast in fundus images. RETICA first normalises the varied contrast using a Retinex based method that separates the illumination from the reflectance part of the image followed by ICA that forms the original retinal pigment makeup namely the macular, haemoglobin and melanin retinal pigment. The haemoglobin image exhibits the highest contrast for retinal vessels. Results based on a dataset of 13 fundus images show that RETICA successfully normalises the low and varied contrast and enhances the retinal vessels. It achieved a better average contrast improvement factor of up to 5.56 compared to the invasive FFA with 5.34. This improvement in contrast reduces the need for fluorescein angiogram in DR assessment.

Segmentation of Acne Vulgaris Lesions

Acne is chronic disorder of the pilosebaceous units with excess sebum production, follicular epidermal hyper proliferation, inflammation and P acnes activity. It affects 85% of adolescents at some time during their lives. Dermatologists use manual methods such as direct visual assessment and ordinary flash photography to assess the acne. These methods are very time consuming and tedious. To address these issues, researchers in recent years have proposed computational imaging methods for aiding in the acne diagnosis. To develop algorithm with an automated acne grading method is the objective of this proposed method. This work presents an image segmentation method for acne lesions based on color features with K-means clustering. The segmentation results from randomly selected images show the sensitivity, specificity, positive predictive value and negative predictive value greater than 81%.