Razali Tomari

Dendritic cell recognition using template matching based on one-dimensional (1D) Fourier descriptors (FD)

Identification of Dendritic Cell (DC) particularly in the cancer microenvironment is a unique disclosure since fighting tumor from the harnessing immune system has been a novel treatment under investigation. Nowadays, the staining procedure in sorting DC can affect their viability. In this paper, a computer aided system is proposed for automatic classification of DC in peripheral blood mononuclear cell (PBMC) images. Initially, the images undergo a few steps in preprocessing to remove uneven illumination and artifacts around the cells. In segmentation, morphological operators and Canny edge are implemented to isolate the cell shapes and extract the contours. Following that, information from the contours are extracted based on Fourier descriptors, derived from one dimensional (1D) shape signatures. Eventually, cells are classified as DC by comparing template matching (TM) of established template and target images. The results show that the proposed scheme is reliable and effective to recognize DC.

Stabilization of Inverted Pendulum: A Multirate Output Feedback Based Discrete Time Sliding Mode Control

This paper presents the application of multirate output feedback with discrete time sliding mode control (SMC) for controlling the inverted pendulum system. The SMC are well known as completely insensitive to parameter variations and external disturbance. Normally, in order to design controller, the state feedback from the entire state variable is utilized. However not all of the state feedbacks are always available. Therefore, the concept of the multirate output feedback (MROF) in which the output state that are always available at any situation is proposed. The nominal plant model for inverted pendulum system is fourth order with additional of external disturbance. Simulation results verify the proposed controller’s performance even though nonlinearity present in the inverted pendulum system.

Analysis of Non-invasive Video Based Heart Rate Monitoring System obtained from Various Distances and Different Facial Spot

Heart rate (HR) is one of the crucial indicators for human psychological. In recent works, it has been shown that a standard camera is able to detect illumination changes in the face skin due to the human cardiac pulse and this can be used to estimate the human HR. However most of previous systems work on near distance mode with a single face patch, thus the expediency of the camera based remote heart rate estimation for long range distances remains ambiguous. This paper has proposed a solution by analyzing an optimal framework that able to works properly under the mentioned issues. Initially, presumable facial landmarks are estimated by applying cascaded of regression mechanism. Then, the region of interest (ROI) was selected based on the facial landmarks in the location where non rigid motion is minimal. Temporal photoplethysmograph (PPG) signal is extracted from the ROI and the unwanted signal such as environment illumination signal or motion artifact signal is eliminated by using Independent Component Analysis (ICA) filter. Then, PPG signal is further processed using series of temporal filter to exclude frequencies outside the range of interest prior to estimate the HR. Since, the HR is estimated independently from multiple local regions, a histogram analysis is constructed to calculate the average HR estimation accurately. From the experiments, it can be concluded that the HR can be detected up to 5 meters range with 94% accuracy using full face region.

Head Pose Estimation from Undistorted Wide Field of View (WFoV) Kinect for Socially Acceptable Wheelchair

Socially acceptable wheelchairs require information about human location and pose to generate a motion that is safe and comfortable to the people around. A WFoV Kinect camera is one of the prominent sensors to obtain such information from a wide coverage of distant targets. However, the task is challenging since the expanded RGB and depth images suffers from distortion, and the head detection were far less than perfect. In this paper, we propose an empirical framework that can alleviate the mentioned problem using laser assisted undistortion strategy and depth based segmentation procedure. Initially, Kinect RGB and depth images are corrected using an inverse radial distortion model. Next, the depth data is rectified using a neural network filter based on laser-assisted training. Following that, possible human regions are validated by employing geometrical depth features. The verified depth regions are further processed in its corresponding RGB location, to determine plausible head regions using Haar-like features with the Adaboost classifier. Finally, the obtained head regions are fed to the pose estimation stage that constructed using boosted-based particle filter. Experimental results demonstrate the feasibility of the proposed approach.

Non-contact Heart Rate Monitoring Analysis from Various Distances with different Face Regions

Heart rate (HR) is one of vital biomedical signals for medical diagnosis. Previously, conventional camera is proven to be able to detect small changes in the skin due to the cardiac activity and can be used to measure the HR. However, most of the previous systems operate on near distance mode with a single face patch, thus the feasibility of the remote heart rate for various distances remains vague. This paper tackles this issue by analyzing an optimal framework that capable to works under the mentioned issues. Initially, plausible face landmarks are estimated by employing cascaded of regression mechanism. Next, the region of interest (ROI) was constructed from the landmarks in a face location where non rigid motion is minimal. From the ROI, temporal photoplethysmograph (PPG) signal is calculated based on the average green pixels intensity and environmental illumination is separated using Independent Component Analysis (ICA) filter. Eventually, the PPG signal is further processed using series of temporal filter to exclude frequencies outside the range of interest prior to estimate the HR. As a conclusion, the HR can be detected up to 5 meters range with 94% accuracy using lower part of face region.

Alternative input medium development for wheelchair user with severe spinal cord injury

Quadriplegia or tetraplegia patients have restricted four limbs as well as torso movement caused by severe spinal cord injury. Undoubtedly, these patients face difficulties when operating their pow...

White blood cell counting analysis of blood smear images using various segmentation strategies

In white blood cell (WBC) diagnosis, the most crucial measurement parameter is the WBC counting. Such information is widely used to evaluate the effectiveness of cancer therapy and to diagnose several hidden infection within human body. The current practice of manual WBC counting is laborious and a very subjective assessment which leads to the invention of computer aided system (CAS) with rigorous image processing solution. In the CAS counting work, segmentation is the crucial step to ensure the accuracy of the counted cell. The optimal segmentation strategy that can work under various blood smeared image acquisition conditions is remain a great challenge. In this paper, a comparison between different segmentation methods based on color space analysis to get the best counting outcome is elaborated. Initially, color space correction is applied to the original blood smeared image to standardize the image color intensity level. Next, white blood cell segmentation is performed by using combination of several color analysis subtraction which are RGB, CMYK and HSV, and Otsu thresholding. Noises and unwanted regions that present after the segmentation process is eliminated by applying a combination of morphological and Connected Component Labelling (CCL) filter. Eventually, Circle Hough Transform (CHT) method is applied to the segmented image to estimate the number of WBC including the one under the clump region. From the experiment, it is found that G-S yields the best performance.In white blood cell (WBC) diagnosis, the most crucial measurement parameter is the WBC counting. Such information is widely used to evaluate the effectiveness of cancer therapy and to diagnose several hidden infection within human body. The current practice of manual WBC counting is laborious and a very subjective assessment which leads to the invention of computer aided system (CAS) with rigorous image processing solution. In the CAS counting work, segmentation is the crucial step to ensure the accuracy of the counted cell. The optimal segmentation strategy that can work under various blood smeared image acquisition conditions is remain a great challenge. In this paper, a comparison between different segmentation methods based on color space analysis to get the best counting outcome is elaborated. Initially, color space correction is applied to the original blood smeared image to standardize the image color intensity level. Next, white blood cell segmentation is performed by using combination of several ...

Optimization of overlapping dendritic cell segmentation in phase contrast microscopy images

The segmentation of Dendritic Cell (DC) from clumps of overlapping Peripheral Blood Mononuclear Cell (PBMC) in Phase Contrast Microscopy (PCM) images is notoriously challenging for an automated image analysis system. This problem is encountered due to the presence of shade off effect and halo region in the image. In order to improve the performance of DC classification, the methods in pre-processing are enhanced and analysed. The images undergo image normalization process to remove uneven illumination. Initially, Local Contrast Threshold (LCT) has been applied in preprocessing. However, it results in low performance of DC segmentation and identification. Therefore, a hybrid of low and high sigma in Gaussian kernel filtering with Local Adaptive Threshold (H-GLAT) through logical operator AND are proposed. Following that, halo removal is applied to eliminate halo region and post-processed by morphological operators to discriminate the cells from the background. The quantitative assessment demonstrates that proposed framework can successfully address these imaging artifact issues. The test results show that the H-GLAT method is better than the LCT that applied in previous work with classifier performance of 76%, 93.3% and 99.5% precision, recall and accuracy respectively.

Quantitative ultrasound venous valve movement: early diagnosis of deep vein thrombosis

The purpose of this paper is to provide an in-depth analysis of computer aided system for the early diagnosis of Deep Vein Thrombosis (DVT). Normally, patients are diagnosed with DVT through ultrasound examination after they have a serious complication. Thus, this study proposes a new approach to reduce the risk of recurrent DVT by tracking the venous valve movement behaviour. Inspired by image processing technology, several image processing methods namely, image enhancement, segmentation and morphological have been implemented to improve the image quality for further tracking procedure. In segmentation, Otsu thresholding provides a significant result in segmenting valve structure. Subsequently, morphological dilation method is able to enhance the region shape of the valve distinctly and precisely. Lastly, image subtraction method is presented and evaluated to track the valve movement. Based on the experimental results the normal range of valve velocity lies within the range of blood flow velocity (Vb) and occasionally may result in higher values.

Study of Vessel Conditions for Deep Vein Thrombosis (DVT) Diagnosis According to Body Mass Index

In this paper, the clinical experiment study is presented to diagnose Deep Vein Thrombosis (DVT). The diagnosis of DVT is commonly conducted by monitoring the blood velocity and present of thrombus in vessel from B-mode ultrasound image associated with the Doppler ultrasound. Since it is difficult to recognize the vessel condition at the early stage of DVT, this study is proposed to evaluate the vein mechanism based on different BMI categories at the early stage of DVT. The wall displacement and blood flow velocity is considered to be the important parameters to construct a clinical model of DVT risk factor, thereby constitutes an important contribution for predicting probability of Deep Vein Thrombosis (DVT).