Rathiah Hashim

Multimodal 3-D reconstruction of human anatomical structures using surlens visualization system

In the medical diagnosis and treatment planning, radiologists and surgeons rely heavily on the slices produced by medical imaging devices. Unfortunately, these image scanners could only present the 3-D human anatomical structure in 2-D. Traditionally, this requires medical professional concerned to study and analyze the 2-D images based on their expert experience. This is tedious, time consuming and prone to error; expecially when certain features are occluding the desired region of interest. Reconstruction procedures was earlier proposed to handle such situation. However, 3-D reconstruction system requires high performance computation and longer processing time. Integrating efficient reconstruction system into clinical procedures involves high resulting cost. Previously, brain’s blood vessels reconstruction with MRA was achieved using SurLens Visualization System. However, adapting such system to other image modalities, applicable to the entire human anatomical structures, would be a meaningful contribution towards achieving a resourceful system for medical diagnosis and disease therapy. This paper attempts to adapt SurLens to possible visualisation of abnormalities in human anatomical structures using CT and MR images. The study was evaluated with brain MR images from the department of Surgery, University of North Carolina, United States and CT abdominal pelvic, from the Swedish National Infrastructure for Computing. The MR images contain around 109 datasets each of T1-FLASH, T2-Weighted, DTI and T1-MPRAGE. Significantly, visualization of human anatomical structure was achieved without prior segmentation. SurLens was adapted to visualize and display abnormalities, such as an indication of walderstrom’s macroglobulinemia, stroke and penetrating brain injury in the human brain using Magentic Resonance (MR) images. Moreover, possible abnormalities in abdominal pelvic was also visualized using Computed Tomography (CT) slices. The study shows SurLens’ functionality as a 3-D Multimodal Visualization System.

Locating abnormalities in brain blood vessels using parallel computing architecture

CT and MRI scans are widely used in medical diagnosis procedures, but they only produce 2-D images. However, the human anatomical structure, the abnormalities, tumors, tissues and organs are in 3-D. 2-D images from these devices are difficult to interpret because they only show cross-sectional views of the human structure. Consequently, such circumstances require doctors to use their expert experiences in the interpretation of the possible location, size or shape of the abnormalities, even for large datasets of enormous amount of slices. Previously, the concept of reconstructing 2-D images to 3-D was introduced. However, such reconstruction model requires high performance computation, may either be time-consuming or costly. Furthermore, detecting the internal features of human anatomical structure, such as the imaging of the blood vessels, is still an open topic in the computer-aided diagnosis of disorders and pathologies. This paper proposes a volume visualization framework using Compute Unified Device Architecture (CUDA), augmenting the widely proven ray casting technique in terms of superior qualities of images but with slow speed. Considering the rapid development of technology in the medical community, our framework is implemented on Microsoft.NET environment for easy interoperability with other emerging revolutionary tools. The framework was evaluated with brain datasets from the department of Surgery, University of North Carolina, United States, containing around 109 MRA datasets. Uniquely, at a reasonably cheaper cost, our framework achieves immediate reconstruction and obvious mappings of the internal features of human brain, reliable enough for instantaneous locations of possible blockages in the brain blood vessels.

New Approach to Image Retrieval Based on Color Histogram

Nowadays a lot of information in the form of digital content is easily accessible but finding the relevant image is a big problem. This is where the Content Based Image Retrieval (CBIR) comes in to solve the image retrieval dilemma. But a CBIR system faces certain problems such as a strong signature development. Also, one of the major challenges of CBIR is to bridge the gap between the low level features and high level semantics. Previously, several researchers have proposed to improve the performance of a CBIR system but they have only answered image retrieval problem to an extent. In this paper, we propose a new CBIR signature that uses color color histogram. The results of the proposed method are compared previous method from the literature. The results of the proposed system demonstrates high accuracy rate than the previous systems in the simulations. The proposed system has significant performance.

Opposition based Particle Swarm Optimization with student T mutation (OSTPSO)

Particle swarm optimization (PSO) is a stochastic algorithm, used for the optimization problems, proposed by Kennedy [1] in 1995. PSO is a recognized algorithm for optimization problems, but suffers from premature convergence. This paper presents an Opposition-based PSO (OPSO) to accelerate the convergence of PSO and at the same time, avoid early convergence. The proposed OPSO method is coupled with the student T mutation. Results from the experiment performed on the standard benchmark functions show an improvement on the performance of PSO.

Color Histogram and First Order Statistics for Content Based Image Retrieval

Content Based Image Retrieval (CBIR) is one of the fastest growing research areas in the domain of multimedia. Due to the increase in digital contents these days, users are experiencing difficulties in searching for specific images in their databases. This paper proposed a new effective and efficient image retrieval technique based on color histogram using Hue-Saturation-Value (HSV) and First Order Statistics (FOS), namely HSV-fos. FOS is used for the extraction of texture features while color histogram deals with color information of the image. Performance of the proposed technique is compared with the Variance Segment and Histogram based techniques and results shows that HSV-fos technique achieved 15% higher accuracy as compared to Variance Segment and Histogram-based techniques. The proposed technique can help the forensic department for identification of suspects.

BBIS: Beacon Bus Information System

Lack of bus information for example bus timetable, status of the bus and messy advertisement on bulletin board at the bus stop will give negative impact to tourist. Therefore, a real-time update bus information bulletin board provides all information needed so that passengers can save their bus information searching time. Supported with Android or iOS, Beacon Bus Information System (BBIS) provides bus information between Batu Pahat and Kluang area. BBIS is a system that implements physical web technology and interaction on demand. It built on Backend-as-a-Service, a cloud solution and Firebase non relational database as data persistence backend and syncs between user client in the real-time. People walk through bus stop with smart device and do not require any application. Bluetooth Beacon is used to achieve smart devices best performance of data sharing. Intellij IDEA 15 is one of the tools that that used to develop the BBIS system. Multi-language included front end and backend supported Integration development environment (IDE) helped to speed up integration process.

CMIS: Crime Map Information System for Safety Environment

Crime Map is an online web based geographical information system that assists the public and users to visualize crime activities geographically. It acts as a platform for the public communities to share crime activities they encountered. Crime and violence plague the communities we are living in. As part of the community, crime prevention is everyones responsibility. The purpose of Crime Map is to provide insights of the crimes occurring around Malaysia and raise the publics awareness on crime activities in their neighbourhood. For that, Crime Map visualizes crime activities on a geographical heat maps, generated based on geospatial data. Crime Map analyse data obtained from crime reports to generate useful information on crime trends. At the end of the development, users should be able to make use of the system to access to details of crime reported, crime analysis and report crimes activities. The development of Crime Map also enable the public to obtain insights about crime activities in their area. Thus, enabling the public to work together with the law enforcer to prevent and fight crime.

Content based image retrieval using MPEG-7 and histogram

Rapid development of multimedia technologies made Content Based Image Retrieval (CBIR) an energetic research area for the researchers of multimedia domain. Texture and color features have been the primal descriptors for images in the field of CBIR. This paper proposed a new CBIR system by combining the both color and texture features. Color Layout Descriptor (CLD) from MPEG-7 is used for the color feature extraction while, Mean, variance, skewness, Kurtosis, energy and entropy are used as texture descriptors. Experiments are performed on Coral Database. The results of the proposed method namely CLD-fos are compared with the four well reputed systems (i.e. SIMPLIcity, Histogram based, FIRM, and Variance Segment etc) from the industry. The results of the CLD-fos demonstrated high accuracy rate than the previous systems during the simulations. The proposed CLD-fos achieved significant performance in terms of accuracy.

Computational Approach for Securing Radiology-Diagnostic Data in Connected Health Network using High-Performance GPU-Accelerated AES.

Diagnostic radiology is a core and integral part of modern medicine, paving ways for the primary care physicians in the disease diagnoses, treatments and therapy managements. Obviously, all recent standard healthcare procedures have immensely benefitted from the contemporary information technology revolutions, apparently revolutionizing those approaches to acquiring, storing and sharing of diagnostic data for efficient and timely diagnosis of diseases. Connected health network was introduced as an alternative to the ageing traditional concept in healthcare system, improving hospital–physician connectivity and clinical collaborations. Undoubtedly, the modern medicinal approach has drastically improved healthcare but at the expense of high computational cost and possible breach of diagnosis privacy. Consequently, a number of cryptographical techniques are recently being applied to clinical applications, but the challenges of not being able to successfully encrypt both the image and the textual data persist. Furthermore, processing time of encryption–decryption of medical datasets, within a considerable lower computational cost without jeopardizing the required security strength of the encryption algorithm, still remains as an outstanding issue. This study proposes a secured radiology-diagnostic data framework for connected health network using high-performance GPU-accelerated Advanced Encryption Standard. The study was evaluated with radiology image datasets consisting of brain MR and CT datasets obtained from the department of Surgery, University of North Carolina, USA, and the Swedish National Infrastructure for Computing. Sample patients’ notes from the University of North Carolina, School of medicine at Chapel Hill were also used to evaluate the framework for its strength in encrypting–decrypting textual data in the form of medical report. Significantly, the framework is not only able to accurately encrypt and decrypt medical image datasets, but it also successfully encrypts and decrypts textual data in Microsoft Word document, Microsoft Excel and Portable Document Formats which are the conventional format of documenting medical records. Interestingly, the entire encryption and decryption procedures were achieved at a lower computational cost using regular hardware and software resources without compromising neither the quality of the decrypted data nor the security level of the algorithms.

Segmentation-based Fractal Texture Analysis and Color Layout Descriptor for Content Based Image Retrieval

Due to the information technology which is rapidly developing, digital content is becoming increasingly difficult to handle. This include images that are kept on digital cameras, CCTV and medical scanners. Areas such as medical and forensic science are using these databases to do critical tasks which include diagnosing of diseases or identification of criminal suspects. However, to manage and search the similar images from these databases are not an easy task. Content Based Image Retrieval (CBIR) is one of the techniques used to manage and search similar images from a database. The performance of CBIR depends on the low level (Texture, Color and Shape) features. In this paper, a new feature vector to represent the image in terms of low level features and to improve the performance of CBIR is proposed. The proposed approach used texture and color feature namely SFTA-CLD. SFTA-CLD is based on Segmentation-based Fractal Texture Analysis (SFTA) and Color Layout Descriptor (CLD). SFTA-CLD is assessed using Coral image gallery and validated by comparing the performance in terms of average precision with previous CBIR techniques.