Detection Technique of Squamous Epithelial Cells in Sputum Slide Images using Image Processing Analysis

A good quality sputum is important to detect diseases. The presence of squamous epithelial cells (SEC) in sputum slide images is important to determine the quality of sputum. The presence of overlapping SEC in sputum slide images causes the process become complicated and tedious. Therefore this paper discusses on technique of detection and summation for Squamous Epithelial Cell (SEC) in sputum slide image. We addressed the detection problem by combining K-means and color thresholding algorithm. The design of aided system is evaluated using 200 images and the proposed technique is capable to detect and count each SEC from overlapping SEC image. Total of 200 images were clustered to 10 groups, labelled as Group Cell 1 to group Cell 10 that correspond to the number of cells in the image. Therefore, each group will contain 20 images. The accuracy of the algorithm to detect SEC was also measured, and results show that in 91% which provides a correct SEC detection and summation

Detection Technique of Squamous Epithelial Cells in Sputum Slide Images Using Image Processing Analysis

A good quality sputum is important to detect diseases. The presence of squamous epithelial cells (SEC) in sputum slide images is important to determine the quality of sputum. The presence of overlapping SEC in sputum slide images causes the process become complicated and tedious. Therefore this paper discusses on technique of detection and summation for Squamous Epithelial Cell (SEC) in sputum slide image. We addressed the detection problem by combining K-means and color thresholding algorithm. The design of aided system is evaluated using 200 images and the proposed technique is capable to detect and count each SEC from overlapping SEC image. Total of 200 images were clustered to 10 groups, labelled as Group Cell 1 to group Cell 10 that correspond to the number of cells in the image. Therefore, each group will contain 20 images. The accuracy of the algorithm to detect SEC was also measured, and results show that in 91% which provides a correct SEC detection and summation

Analysis of Unclean Hand System Detection Using Template Matching Technique

The aim of this project is to audit the handwashing technique of hospital staff that may cause infection to the patients. This project is to detect unclean washed hands using image processing technique specifically template matching. The detection and recognition of palm in images is the key methodology of this paper. The prototype used for capturing hand images is a dark box with UV light and a camera. Target will need to apply Glogerm on their hands that imitate bacteria. Hence, when they wash their hands inappropriately, Glogerm can be seen in the captured images under the UV light as the unwanted stain on washed hands, the target handwashing technique needs to be improved. Templates of the missed area of washed hands are used to compare the correctness of hand washed techniques by the target. Data of 100 images were taken, results are; 100% accuracy of the hand image without Glogerm, 56.67% of the image that did not wash using water after applying the Glogerm and 45.45% accurate when user wash their hand by using water after applying Glogerm. The overall efficiency of the system in detecting the missed part is 51% accuracy As a summary, this project accurately detects stain percentage that represents the missed part when applying the template matching technique

Automatic Gram Staining for Sputum Slide

The Gram stain is the most important and universally used staining technique in the bacteriology laboratory. Gram staining method is used to do staining of the clinical material or the bacteria from colonies on laboratory media and provide a direct visualization of the morphology of the organisms based on their reactions to the chemical present in stains. A sputum sample slide need to be stained before the quality of the sputum sample is determined. However, due to human inconsistency, some of the slides are heavily stained with dark color whereas some of it is lightly stained. This inconsistency would create a difficulty for automated sputum quality system using image processing. Therefore, an automated gram staining for sputum slide is needed in order to standardize the slide staining. The automated Gram-staining will undergo staining, washing, and drying process. Each process periods are controlled by a timer built in the microcontroller. The analysis is done on the accuracy of the position of the slide stain, the consistency of the amounts of staining solution drop on the slide and the time efficiency of this automated system is compared to manual operation

Robust Object Tracking System via Sparse Representation: A Review

Object tracking is a challenging issue in computer vision and it has been studied by numerous researchers, with several approaches are introduced to improve the tracking performance. The challenges arise in object tracking such as occlusion, background clutter, illumination conditions and appearance changes have motivated researchers to explore a robust tracking system which can handle these problem. One of the recent tracking technique is sparse representation method where it has been exploited in many tracking-based applications as it is proven to be robust to the challenges stated earlier as compared to other tracking method. This paper reviews the recent sparse technique in several applications, focusing on the robustness and efficiency of the proposed method. More specifically, the advantages and disadvantages of the proposed algorithm in each application will be pointed out by comparing with other tracking methods. At the end of the discussion, a new tracking-based application deploying the robust sparse technique will be proposed

Enhancement on stain detection for automatic handwashing audit vision system

Hand hygiene of the health care worker is critical to prevent infectious disease such as airborne diseases, nosocomial infections, and Hepatitis A among patients. Currently, the handwashing audit among hospital staffs are done manually by observation from an expert. There is a need for automation ease the process and accuracy in detection using vision system. This paper focus on the enhancement of the established prototype and the detection system by the additional third template. The prototype is enhanced by size reduction, sturdier material and hands placement base. The vision system uses a robust threshold to detect the discolored stain with an enhancement on stain templates. The detection and recognition of palm in images is a key research topic that has attracted attention owing to an unveiling human perception mechanism. The system accuracy has increased by 40% from previous work by the enhancement which for three cases the system accurately detects stains on washed hands with Glogerm by 71% and unwashed hand with GloGerm by 81%

Detection Technique of Squamous Epithelial Cells in Sputum Slide Images using Image Processing Analysis

A good quality sputum is important to detect diseases. The presence of squamous epithelial cells (SEC) in sputum slide images is important to determine the quality of sputum. The presence of overlapping SEC in sputum slide images causes the process become complicated and tedious. Therefore this paper discusses on technique of detection and summation for Squamous Epithelial Cell (SEC) in sputum slide image. We addressed the detection problem by combining K-means and color thresholding algorithm. The design of aided system is evaluated using 200 images and the proposed technique is capable to detect and count each SEC from overlapping SEC image. Total of 200 images were clustered to 10 groups, labelled as Group Cell 1 to group Cell 10 that correspond to the number of cells in the image. Therefore, each group will contain 20 images. The accuracy of the algorithm to detect SEC was also measured, and results show that in 91% which provides a correct SEC detection and summation

Detection And Summation Of Pus Cells And Epithelial Cells For Sputum Quality Grading Using K-Means Clustering And Color Thresholding

Sputum with good quality is important in the diagnosis of many diseases. Some factor like the number of neutrophils (pus cell), squamous epithelial cell and the appearance of macroscopic should be considered to determine the sputum quality based on Bartlett's Criteria. Hence, a vision system which is able to detect and count the existence of pus cell and epithelial cell for sputum quality testing is developed. This vision system process four sputum image for each sputum sample and determine the average number of pus cells and epithelial cells in that sample. This system also able to determine either the sputum sample is positive or negative. The percentage of error for average number of pus cell is 26.3% whereas error for epithelial cell is 27.78%. However, the final grading of either the sputum sample is positive or negative is 100% accurate

Automated Detection And Counting Of Pus Cells On Sputum Images

his paper discusses on a feature extraction of pus cell in sputum slide image. This invention is developed to analyze and count the content of cells specifically pus cell within a biological sample, and more particularly sputum sample which is useful for sputum quality grading. This pus cell detection is addressed by mean intensity and area for single and overlapping pus cells. It is found that mean intensity and area for single pus cell are ranges from 130-163 and 35-81 respectively. Whereas, with considering other elements exist in sputum image such as epithelial cells and artifacts, the mean intensity for overlapping pus cells are ranges from 130-45 with area of 65-300. This system has the sensitivity of 92.11%, with specificity of 81.82%, accuracy of 87.32% and precision of 85.37

Hardware development of auto focus microscope

The scientific instrument technology has growth faster than we all could imagine, there are many research team keeping their momentum in creating new innovation in scientific instrumentation technologies. The optical microscopes are still being used widely in the scientific research especially by researcher and medical practitioners. Manually deal with the microscope could make the user spend so much time to obtain the result of cleared image. It could cost hours to obtain the desire result. From this problem, this study proposes the development of hardware system for auto focused of an optical microscope. The proposed system consists of two stepper motors that will move the fine focus knob and the course focus knob on a microscope. The timing belts are being used as mounting between the stepper motor and the fine / course focus knob. The motor will moves step by step in same degree given from the command of a program. The motor are able to be controlled and it moves slowly to perform an auto focus task. Additionally, it is able to move in a small angle to find the proper exposure of the images scan. The hardware implementation of auto focus on the optical microscope has been tested and it worked perfectly. The result presented in this study shows that the proposed system is able to do auto focus in precise step which is 5° step