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All Journal International Journal of Evaluation and Research in Education (IJERE) Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Aini Hussain
Universiti Kebangsaan Malaysia
Computer Science & IT Education Electrical & Electronics Engineering Engineering

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Tooth segmentation using dynamic programming-gradient inverse coefficient of variation Anuar Mikdad Muad; Nur Sakinah Mohamed Bahaman; Aini Hussain; Mohd Yusmiaidil Putera Mohd Yusof
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (670.948 KB) | DOI: 10.11591/eei.v8i1.1446

Abstract

Teeth provide meaningful clues of an individual. The growth of the teeth is correlated with the individual age. This correlation is widely used to estimate age of an individual in applications like conducting forensic odontology, immigration, and differentiating juveniles and adolescents. Current forensic dentistry largely depends on laborious investigation process that is performed manually and can be influenced by human factors like fatigue and inconsistency. Digital panoramic radiograph dental images allow noninvasive and automatic investigation to be performed. This paper presents analyses on third molar tooth segmentation for the population in Malaysia, ranging from persons age of 5 years old to 23 years old. Two segmentation techniques: gradient inverse coefficient of variation with dynamic programming (DP-GICOV) and Chan-Vese (CV) were employed and compared. Results demonstrated that the accuracy of DP-GICOV and CV were 95.3%, and 81.6%, respectively.
A Finite State Machine Fall Detection Using Quadrilateral Shape Features Mohd Fadzil Abu Hassan; Mohamad Hanif Md Saad; Mohd Faisal Ibrahim; Aini Hussain
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (560.373 KB) | DOI: 10.11591/eei.v7i3.1184

Abstract

A video-based fall detection system was presented; which consists of data acquisition, image processing, feature extraction, feature selection, classification and finite state machine. A two-dimensional human posture image was represented by 12 features extracted from the generalisation of a silhouette shape to a quadrilateral. The corresponding feature vectors for three groups of human pose were statistically analysed by using a non-parametric Kruskal Wallis test to assess the different significance level between them. From the statistical test, non-significant features were discarded. Four selected kernel-based Support Vector Machine: linear, quadratics, cubic and Radial Basis Function classifiers were trained to classify three human posture groups. Among four classifiers, the last one performed the best in terms of performance matric on testing set. The classifier outperformed others with high achievement ofaverage sensitivity, precision and F-score of 99.19%, 99.25% and 99.22%, respectively. Such pose classification model output was further used in a simple finite state machine to trigger the falling event alarms. The fall detection system was tested on different fall video sets and able to detect the presence offalling events in a frame sequence of videos with accuracy of 97.32% and low computional time.
GA-based Optimisation of a LiDAR Feedback Autonomous Mobile Robot Navigation System Siti Nurhafizah Anual; Mohd Faisal Ibrahim; Nurhana Ibrahim; Aini Hussain; Mohd Marzuki Mustafa; Aqilah Baseri Huddin; Fazida Hanim Hashim
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (385.649 KB) | DOI: 10.11591/eei.v7i3.1275

Abstract

Autonomous mobile robots require an efficient navigation system in order to navigate from one location to another location fast and safe without hitting static or dynamic obstacles. A light-detection-and-ranging (LiDAR) based autonomous robot navigation is a multi-component navigation system consists of various parameters to be configured. With such structure and sometimes involving conflicting parameters, the process of determining the best configuration for the system is a non-trivial task. This work presents an optimisation method using Genetic algorithm (GA) to configure such navigation system with tuned parameters automatically. The proposed method can optimise parameters of a few components in a navigation system concurrently. The representation of chromosome and fitness function of GA for this specific robotic problem are discussed. The experimental results from simulation and real hardware show that the optimised navigation system outperforms a manually-tuned navigation system of an indoor mobile robot in terms of navigation time.
Retinal blood vessel segmentation from retinal image using B-COSFIRE and adaptive thresholding Aziah Ali; Wan Mimi Diyana Wan Zaki; Aini Hussain
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 3: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i3.pp1199-1207

Abstract

Segmentation of blood vessels (BVs) from retinal image is one of the important steps in developing a computer-assisted retinal diagnosis system and has been widely researched especially for implementing automatic BV segmentation methods. This paper proposes an improvement to an existing retinal BV (RBV) segmentation method by combining the trainable B-COSFIRE filter with adaptive thresholding methods. The proposed method can automatically configure its selectivity given a prototype pattern to be detected. Its segmentation performance is comparable to many published methods with the advantage of robustness against noise on retinal background. Instead of using grid search to find the optimal threshold value for a whole dataset, adaptive thresholding (AT) is used to determine the threshold for each retinal image. Two AT methods investigated in this study were ISODATA and Otsu’s method. The proposed method was validated using 40 images from two benchmark datasets for retinal BV segmentation validation, namely DRIVE and STARE. The validation results indicated that the segmentation performance of the proposed unsupervised method is comparable to the original B-COSFIRE method and other published methods, without requiring the availability of ground truth data for new dataset. The Sensitivity and Specificity values achieved for DRIVE and STARE are 0.7818, 0.9688, 0.7957 and 0.9648, respectively.
Co-Authors Anuar Mikdad Muad Aqilah Baseri Huddin Ashrani Aizzuddin Abd Rahni Aziah Ali Fazida Hanim Hashim M. A. Hannan M. M. Mustafa Mardina Abdullah Mohamad Hanif Md Saad Mohd Fadzil Abu Hassan Mohd Faisal Ibrahim Mohd Hairi Mohd Zaman Mohd Marzuki Mustafa Mohd Yusmiaidil Putera Mohd Yusof Nur Sakinah Mohamed Bahaman Nurhana Ibrahim Seri Mastura Mustaza Siti Nurhafizah Anual Wan Mimi Diyana Wan Zaki