Aouache Mustapha

Provisioning Quality of Service of Wireless Telemedicine for E-Health Services: A Review

In general, on-line medical consultation reduces time required for medical consultation and induces improvement in the quality and efficiency of healthcare services. The scope of study includes several key features of present day e-health applications such as X-ray, ECG, video, diagnosis images and other common applications. Moreover, the provision of Quality of Service (QoS) in terms of specific medical care services in e-health, the priority set for e-health services and the support of QoS in wireless networks and techniques or methods aimed at IEEE 802.11 to secure the provision of QoS has been assessed as well. In e-health, medical services in remote places which include rustic healthcare centres, ships, ambulances and home healthcare services can be supported through the applications of e-health services such as medical databases, electronic health data and the transferring of text, video, sound and images. Given this, a proposal has been made for a multiple service wireless networking with multiple sets of priorities. In relation to the terms of an acceptable QoS level by the customers of e-health services, prioritization is an important criterion in a multi-traffic network. The requirement for QoS in medical networking of wireless broadband has paved the way for bandwidth prerequisites and the live transmission or real-time medical applications. The proposed wireless network is capable of handling medical applications for both normal and life-threatening conditions as characterized by the level of emergencies. In addition, the allocation of bandwidth and the system that controls admittance designed based on IEEE 802.16 especially for e-health services or wireless telemedicine will be discussed in this study. It has been concluded that under busy traffic conditions, the proposed architecture can used as a feasible and reliable infrastructure network for telemedicine.

Design and development of a content-based medical image retrieval system for spine vertebrae irregularity

BackgroundContent-based medical image retrieval (CBMIR) system enables medical practitioners to perform fast diagnosis through quantitative assessment of the visual information of various modalities.MethodsIn this paper, a more robust CBMIR system that deals with both cervical and lumbar vertebrae irregularity is afforded. It comprises three main phases, namely modelling, indexing and retrieval of the vertebrae image. The main tasks in the modelling phase are to improve and enhance the visibility of the x-ray image for better segmentation results using active shape model (ASM). The segmented vertebral fractures are then characterized in the indexing phase using region-based fracture characterization (RB-FC) and contour-based fracture characterization (CB-FC). Upon a query, the characterized features are compared to the query image. Effectiveness of the retrieval phase is determined by its retrieval, thus, we propose an integration of the predictor model based cross validation neural network (PMCVNN) and similarity matching (SM) in this stage. The PMCVNN task is to identify the correct vertebral irregularity class through classification allowing the SM process to be more efficient. Retrieval performance between the proposed and the standard retrieval architectures are then compared using retrieval precision (Pr@M) and average group score (AGS) measures.ResultsExperimental results show that the new integrated retrieval architecture performs better than those of the standard CBMIR architecture with retrieval results of cervical (AGS > 87%) and lumbar (AGS > 82%) datasets.ConclusionsThe proposed CBMIR architecture shows encouraging results with high Pr@M accuracy. As a result, images from the same visualization class are returned for further used by the medical personnel.

An analysis of x-ray image enhancement methods for vertebral bone segmentation

Image enhancement is a critical component in getting a good segmentation, especially for X-ray images. Magnification of the contrast and sharpness of the image will increase the accuracy of the subsequent modules for an autonomous disease diagnosis system. In this paper, we analyze various methods of preprocessing techniques for vertebral bone segmentation. Three methods are considered which are histogram equalization (HE), gamma correction (GC) and contrast limited adaptive histogram equalizer (CLAHE). This work aims to compare and quantify the precision and accuracy of the techniques that are used to enhance the image quality. Experimental results of the system yield favorable results where the most accurate technique is CLAHE, followed by GC and HE.

Automatic Vertebral Fracture Assessment System (AVFAS) for Spinal Pathologies Diagnosis Based on Radiograph X-Ray Images

Nowadays, medical imaging has become a major tool in many clinical trials. This is because the technology enables rapid diagnosis with visualization and quantitative assessment that facilitate health practitioners or professionals. Since the medical and healthcare sector is a vast industry that is very much related to every citizen`s quality of life, the image based medical diagnosis has become one of the important service areas in this sector. As such, a medical diagnostic imaging (MDI) software tool for assessing vertebral fracture is being developed which we have named as AVFAS short for Automatic Vertebral Fracture Assessment System. The developed software system is capable of indexing, detecting and classifying vertebral fractures by measuring the shape and appearance of vertebrae of radiograph x-ray images of the spine. This paper describes the MDI software tool which consists of three main sub-systems known as Medical Image Training & Verification System (MITVS), Medical Image and Measurement & Decision System (MIMDS) and Medical Image Registration System (MIRS) in term of its functionality, performance, ongoing research and outstanding technical issues.

Robust Identification of Polyethylene Terephthalate (PET) Plastics through Bayesian Decision

Recycling is one of the most efficient methods for environmental friendly waste management. Among municipal wastes, plastics are the most common material that can be easily recycled and polyethylene terephthalate (PET) is one of its major types. PET material is used in consumer goods packaging such as drinking bottles, toiletry containers, food packaging and many more. Usually, a recycling process is tailored to a specific material for optimal purification and decontamination to obtain high grade recyclable material. The quantity and quality of the sorting process are limited by the capacity of human workers that suffer from fatigue and boredom. Several automated sorting systems have been proposed in the literature that include using chemical, proximity and vision sensors. The main advantages of vision based sensors are its environmentally friendly approach, non-intrusive detection and capability of high throughput. However, the existing methods rely heavily on deterministic approaches that make them less accurate as the variations in PET plastic waste appearance are too high. We proposed a probabilistic approach of modeling the PET material by analyzing the reflection region and its surrounding. Three parameters are modeled by Gaussian and exponential distributions: color, size and distance of the reflection region. The final classification is made through a supervised training method of likelihood ratio test. The main novelty of the proposed method is the probabilistic approach in integrating various PET material signatures that are contaminated by stains under constant lighting changes. The system is evaluated by using four performance metrics: precision, recall, accuracy and error. Our system performed the best in all evaluation metrics compared to the benchmark methods. The system can be further improved by fusing all neighborhood information in decision making and by implementing the system in a graphics processing unit for faster processing speed.

On analyzing optic disc extraction through weighted colour models

Optic disc segmentation is a crucial step in automated glaucoma detection system through Cup-to-Disc ratio measurement. Recent approaches focus on deterministic algorithm of RGB or grey model only. In this paper, we proposed a statistically integrated approach by combining various colour models. The driving motivation is the ability of each colour model to work accurately in certain environments or cases. Histogram of ach colour model of HSV, RGB and grey will be tabulated to approximate the colour distribution. The ratio between the highest and the second highest will be the contribution weightage of the fused output. The performance is simulated by using RIM-One database. The average sensitivity and specificity of the detection are 0.912 and 0.832 respectively.

Shape-based boundary segmentation assessment for cervical vertebrae classification

Shape-based boundary representation is a critical component in medical imaging-based diagnosis system. Furthermore, accurate boundary representation is also essential in getting good segmentation, feature indexing and classification. This work concentrates on cervical vertebra distortion across the anterior boundary to detect anterior osteophytes and identify its severity. A suitable shape representation, which defines the vertebrae shape boundary must be determined prior to the cervical vertebra model can be constructed. Therefore, two different shape-based representations for vertebral segmentation using active shape model are analysed, which are 9-anatomical point representation (9-APR) and B-spline representation (BSR). The aims is to compare and quantify the precision and accuracy performance of the segmented vertebra with the help of orientation histogram-based indexing and multi-layer perception based on ten-fold cross validation (MLP-TFCV) classifier. Experimental results are assessed based on the segmented boundary outline by using ROC and AUC curves. The results show that the performance of both boundary representations are almost similar where B-SR has a small advantage compared to the 9-APR. The system can be further improved by introducing a statistical sampling method of the anterior points instead of the whole vertebra.

An adaptive nonlinear enhancement method using sigmoid function for iris segmentation in pterygium cases

Pterygium is an eye related disease affected by the fibrovascular tissue that encroaches into the corneal region. Recently, image processing techniques have been explored in the development of pterygium detection system. An iris segmentation module is needed to develop an automatic pterygium detection system of the anterior segment photographed images (ASPI). Qualitatively, the invasion of the pterygium tissues on the iris will result in the imperfect circular iris feature. Thus, an adaptive nonlinear enhancement method using sigmoid function have been proposed in this work to enhance the ASPI. The cutoff and gain factor of the sigmoid function are adaptively calculated based on the tested images. Fifty eight ASPI of various sizes contributed by RAFAEL have been tested using the proposed enhancement method. The proposed method proves to give better visual results, later contributes to more accurate segmented iris regions with accuracy and specificity values of 0.9353 and 0.8818, respectively.

Iris segmentation method of pterygium anterior segment photographed image

Pterygium is an eye disease that commonly affects people living in areas near the equator such as Malaysia, Indonesia etc. and who are expose to excessive wind, sunlight, or sand. It is a form of tissue overgrowth found in the eye. Recently, anterior segment photographed images (ASPIs) have been used for early detection of the disease by incorporating digital image processing (DIP) algorithms techniques which has triggered our interest to investigate such possibilities. As such, this paper reports the early results of iris segmentation of ASPIs that can be used later for pterygium detection. The work involves using the normalized HSV colour space of the iris ASPIs. By using the subtraction method, the iris threshold value was calculated to segment the iris. It is found out that the proposed algorithm can correctly segment the iris with pterygium cases.

Toward under-specified queries enhancement using retrieval and classification platforms

Radiography images are used usually for diseases detection and fracture that can be visible on lateral view. Poor contrast of x-ray images do not provide momentous information concerning pathologies that are of interest to the radiologist. Magnification of contrast and sharpness of x-ray images will afford plenty and satisfactory visual information to radiologist and clinician and thus, allow better segmentation and indexing subsequent modules in the computer aided diagnosis (CAD) system for an autonomous disease diagnosis. Therefore, in this paper it intends to describe a new strategy to cater for under-specified queries enhancement using retrieval and classification platforms. In the retrieval platform (RPF), gamma correction (GC) function was employed on the under-specified query (USQ) image to generate dispersion versus location (DL) descriptor that measures the relationship between the local contrast and the local brightness, measured respectively with the help of estimators of location and dispersion. Subsequently, it employs appropriate near optimal search between the DL features of the USQ image and the corresponding similarity measurement in the archive database. In the classification platform (CPF), an approach was examined to predict the gain value of GC function using statistical pixel-level (SPL) features extracted from the radiography images along with the ANNs model classifier. The quality of the retrieved image is determined by referring to the USQ image. In addition, the problem of gain value estimation is transformed to a classification problem solved using an ANN model with three different measurement modes. Results indicated that the proposed approach can significantly improved image quality as confirmed by the DL descriptor which shown a more balance condition.