Detection of malaria parasites in blood smear image using color-intensity feature extraction

Malaria is one of the life-threatening diseases that affect millions of innocent lives each year, mainly in tropical areas where the most serious illness is caused by the species of Plasmodium falciparum. The conventional microscopy used in the diagnosis of malaria disease has proved to be inefficient since the process is time-consuming and the result is difficult to be reproduced. The alternative diagnosis techniques which yield the superior standard results are expensive and hence inaccessible to poor countries and rural areas. Therefore, this study aims to develop a prototype system that detects malaria parasites automatically from microscopic images by using the color-intensity feature extraction. Two objectives had been made for this study which is to develop an automatic malaria parasite detection system and to detect the malaria parasites in the microscopic blood images using color-intensity feature extraction. The input image is processed with image processing algorithms which include image sharpening, image segmentation (Canny Edge Detection and Watershed segmentation), and feature extraction of the malaria parasites (color-intensity feature extraction). Overall, the accuracy test of the proposed system achieved 98.7% when tested in 300 blood smear images

Plate number recognition systems based on a contours and character recognition approach

License plate recognition system (LPR) plays an important role in intelligent traffic control system. However, most of the existing LPR are complex and hard to implement. The aim of this project is to improve the LPR techniques in terms of speed and accuracy by applying the Connected Component Analysis (CCA) and K-Nearest Neighbour algorithm (KNN). The LPR is divided into three stages which are image pre-processing, character segmentation, and character recognition. First, the input plate image will undergo some image property functions such as omission of noise to enhance the quality of the image. The CCA is applied to segment the characters by drawing rectangle boxes on each character, based on contours to extract the characters into smaller images. These images are then used as query images in character recognition stage. The images are fed to a pre-defined KNN classifier to determine the features of each image and to identify them. Five experiments were carried out to validate the proposed system. Ten Malaysia single row plate images and two foreign plate images were used as the input images on these tests. The findings show that the proposed system has an 80.0% success rate in segmentation, 92.21% accuracy rate in recognition, the optimal K value is 1, and the input image must be in a single row and comprises of a black background and white characters namely letters and digits. In conclusion, a prototype for plate number recognition has been developed with a high success rate in segmentation and a high accuracy in character recognition. Suggested future studies include a focus on segmenting double row license plates and recognizing similar characters

Real-time pre-placed marker-less square-ROI verification system based on contour-corner approach for breast augmentation

This paper aims to enhance the current contour and corner detection approach by applying smoothing and adaptive thresholding techniques to the stream input and then use subpixel corner detection to obtain better and more accurate interest point. There are two main steps involved in AR application, first - detect and extract local features and second – visualization and rendering. Our focus is the first part of the whole operation – features. We proposed marker-less approach as to avoid the needs to prepare the target environment and to make our approach more flexible. The proposed method starts with first getting an input from the real environment through a camera as visual sensor. On receiving an input image, the proposed system will process the image, finds and detects strong interest point from the ROI by applying enhanced contour-corner detection. From the ROI, features such as number of corners and vertices can be extracted and later can be used to determine a marker. For testing purposes, a mannequin as an input is used. Based on the experiment, the proposed method manage to capture the environment, convert captured frame into grey-scale image, detect corners and contours and also able to identify and verify a marker

Environmental geochemistry of radioactive contamination.

This report attempts to describe the geochemical foundations of the behavior of radionuclides in the environment. The information is obtained and applied in three interacting spheres of inquiry and analysis: (1) experimental studies and theoretical calculations, (2) field studies of contaminated and natural analog sites and (3) model predictions of radionuclide behavior in remediation and waste disposal. Analyses of the risks from radioactive contamination require estimation of the rates of release and dispersion of the radionuclides through potential exposure pathways. These processes are controlled by solubility, speciation, sorption, and colloidal transport, which are strong functions of the compositions of the groundwater and geomedia as well as the atomic structure of the radionuclides. The chemistry of the fission products is relatively simple compared to the actinides. Because of their relatively short half-lives, fission products account for a large fraction of the radioactivity in nuclear waste for the first several hundred years but do not represent a long-term hazard in the environment. The chemistry of the longer-lived actinides is complex; however, some trends in their behavior can be described. Actinide elements of a given oxidation state have either similar or systematically varying chemical properties due to similarities in ionic size, coordination number, valence, and electron structure. In dilute aqueous systems at neutral to basic pH, the dominant actinide species are hydroxy- and carbonato-complexes, and the solubility-limiting solid phases are commonly oxides, hydroxides or carbonates. In general, actinide sorption will decrease in the presence of ligands that complex with the radionuclide; sorption of the (IV) species of actinides (Np, Pu, U) is generally greater than of the (V) species. The geochemistry of key radionuclides in three different environments is described in this report. These include: (1) low ionic strength reducing waters from crystalline rocks at nuclear waste research sites in Sweden; (2) oxic water from the J-13 well at Yucca Mountain, Nevada, the site of a proposed repository for high level nuclear waste (HLW) in tuffaceous rocks; and (3) reference brines associated with the Waste Isolation Pilot Plant (WIPP). The transport behaviors of radionuclides associated with the Chernobyl reactor accident and the Oklo Natural Reactor are described. These examples span wide temporal and spatial scales and include the rapid geochemical and physical processes important to nuclear reactor accidents or industrial discharges as well as the slower processes important to the geologic disposal of nuclear waste. Application of geochemical information to remediating or assessing the risk posed by radioactive contamination is the final subject of this report. After radioactive source terms have been removed, large volumes of soil and water with low but potentially hazardous levels of contamination may remain. For poorly-sorbing radionuclides, capture of contaminated water and removal of radionuclides may be possible using permeable reactive barriers and bioremediation. For strongly sorbing radionuclides, contaminant plumes will move very slowly. Through a combination of monitoring, regulations and modeling, it may be possible to have confidence that they will not be a hazard to current or future populations. Abstraction of the hydrogeochemical properties of real systems into simple models is required for probabilistic risk assessment. Simplifications in solubility and sorption models used in performance assessment calculations for the WIPP and the proposed HLW repository at Yucca Mountain are briefly described

Integrated approach to recognize square-ROI in breast cancer augmentation

The ultimate goal of an AR system is to create a mixed-digital environment such that the computer–generated objects mixed into the real-world environment as one of its entities. Thus, the object must be visually registered in every point the user sees. To maintain the user’s illusion that the virtual objects are part of the real world requires a consistent registration of the virtual world with the real world, and this stringent requirement of the system is one of the challenges in developing an AR system (Klein, 2006; Xu et al., 2003; Azuma, 1997)

A real-time fiducial marker based on vision-based techniques to visualize the coexistense of the real and synthetic _3d breast cancer model in the identical real space

Various studies have been conducted to explore the potential of Augmented Reality (AR) technology in mainstream application such as in medicine, visualization, maintenance, path planning, and education. With the rapid development in computing technology, AR technology can be used to aid the surgeon and the patient to have in depth visualization about their sickness in which human senses are not able to detect. An AR system combines the field of Computer Vision (CV) areas such as marker, markertess tracking and feature detection in order to fully utilize the AR potential. Although there exist methods for patient diagnosis through anatomic landmark or fiducial marker as the region of interest (ROI), this method, however, required an additional surgery in order to insert the fiducial marker to the patient It is not only time-consuming, but also invasive and might cause trauma to the patient. Thus, innovations in the current techniques for the insertion of the fiducial marker may improve its accuracy and reduce its risks, and enhancing comfort for the patient. Therefore, this project will investigate the feasibility of real­time markertess square-ROI recognition (RPMS) based on the integration of contour-corner approach as the fundamental component in registering the virtual imagery with its real object without the needs to use conventional marker. To enhance the conventional contour and corner approach, a smoothing and adaptive thresholding are performed to the captured input stream and then use subpixel corner detection to obtain better and accurate corner points. The RPMS technique starts with first getting an input from the real environment through a web camera. It will then, process the input, finds and detects strong interest points from the manually drawn Square-ROI. From the ROI, features such as number of corners and vertices will be extracted and later used to determine a marker. For testing purposes, two sets of experiments have been conducted to evaluate the RPMS technique. The first test evaluate the RPMS performance accuracy in identifying the hand-drawn ROI on an A4 size paper as a marker, followed by the used of a mannequin in the second experiment. For each experiment, the evaluation is repeated independently with eight different sizes of ROI, ranging from 3 x 3 to 10 x 10 cm with 1.0 mm border line's thickness. initially, in the experiments, the visual sensor (webcam) is positioned at 60 cm from the hand-drawn square-ROI in order to determine the best square-ROl's size and the optimal viewing distance. From the experiments, the best execution times obtained are 0.39 ms (A4 paper) and 0.81 ms (mannequin) with 6 x 6 cm as the best square-ROI size. It is found that for the size of 6 x 6 cm, the optimal viewing distance is from 7 cm to 23 an. In these experiments, it shows that the RPMS technique takes 10.09 ms to detect comers and 1.38 ms to detect the square-ROI. These indicate that, the RPMS technique is efficient, accurate and robust within the experiments environment and could be portable to any desired target area or domain