Mohd Falfazli Mat Jusof

Feature extraction of pus cells detection and counting in sputum slide images

This 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 reliability is above 80% from the validation results.

An Opposition-based Simulated Kalman Filter algorithm for adaptive beamforming

In this paper, a population-based metaheuristic optimization algorithm named Opposition-based Simulated Kalman Filter (OBSKF) is proposed as an adaptive beamforming algorithm. This algorithm is based on Simulated Kalman Filter (SKF), which is a newly introduced optimization algorithm inspired by Kalman filtering. Opposition-based learning is employed in SKF to improve the performance of SKF in adaptive beamforming. Adaptive beamforming algorithm based on OBSKF is compared with SKF and the existing Adaptive Mutated Boolean Particle Swarm Optimization (AMBPSO). Experimental results show that adaptive beamforming algorithm based on OBSKF is better than SKF and AMBPSO.

The detection and summation of squamous epithelial cells for sputum quality testing

Sputum with good quality is important to detect diseases. The quality of sputum is determined using Bartletts Criteria by considering the score of squamous epithelial cells (SEC), pus cell (neutrophils) and macroscopy. For squamous epithelial cells, the score is 0 if SEC is less than 10. Whereas if SEC is within 10 to 25, the score is -1 and the score is -2 if the number of SEC is greater than 25. Currently, the detection of SEC in sputum is manually done by technologists. However, the problem with manual detection is time consuming. So, an automated vision system using image processing technique to detect the sputum quality is desirable. Image processing such as image segmentation is used to detect and count the number of SEC, and then the score is determined. Lastly, the percentage of error for this project is calculated.