Haryati Jaafar

Automatic syllables segmentation for frog identification system

Automatic recognition of frog sound according to particular species is considered a worthy tool for biological research and environmental monitoring. As a result, automatic recognition of frog sound offers many advantages rather than manual method that depending on physical observation procedure. This study evaluates the accuracy of frog sound identification from 12 species that recorded from Malaysia forest. By applying short time energy and short time average zero crossing rate, the frog sound samples are automatically segmented into syllables. A syllable feature extraction method i.e, Mel-Frequency Cepstrum Coefficients is employed to extract the segmented signal. Finally, nonparametric k-nearest neighbor classifier with Euclidean distance has been employed to recognize the frog species. A comparison between automatic segmentation and manual segmentation is applied and results show that automatic segmentation outperforms to identify the frog species with an accuracy of 97% compared to 82.33% for manual segmentation.

Tuberculosis bacilli detection in Ziehl-Neelsen-stained tissue using affine moment invariants and Extreme Learning Machine

This paper describes an approach to automate the detection and classification of tuberculosis (TB) bacilli in tissue section using image processing technique and feedforward neural network trained by Extreme Learning Machine. It aims to assist pathologists in TB diagnosis and give an alternative to the conventional manual screening process, which is time-consuming and labour-intensive. Images are captured from Ziehl-Neelsen (ZN) stained tissue slides using light microscope as it is commonly used approach for diagnosis of TB. Then colour image segmentation is used to locate the regions correspond to the bacilli. After that, affine moment invariants are extracted to represent the segmented regions. These features are invariant under rotation, scale and translation, thus useful to represent the bacilli. Finally, a single layer feedforward neural network (SLFNN) trained by Extreme Learning Machine (ELM) is used to detect and classify the features into three classes: ‘TB’, ‘overlapped TB’ and ‘non-TB’. The results indicate that the ELM gives acceptable classification performance with shorter training period compared to the standard backpropagation training algorithms.

MFCC based frog identification system in noisy environment

Identification of frog sound is useful tool and competent in biological research and environmental monitoring. In contrast with traditional methods that not practical due to the time consuming, expensive or detrimental to the animals welfare, this study proposes an automatic frog call identification system. 750 data species that recorded from Malaysia forest is used as data signals and have been corrupted by 10dB and 20dB noise to determine the performance of accuracy in noisy environment. MFCC parameter is employed as feature extraction. An analysis of signals for different number of MFCCs (8, 12, 15, 20 and 25) is presented and the results are provided using MFCC, Delta Coefficients (ΔMFCC) and Delta Delta Coefficients (ΔΔMFCC). Subsequently, kNN classifier is applied to evaluate the performance in the frog identification system. The results show the accuracy range from 84.67% to 85.78%, 61.33% to 68.89% and 59.33% to 67.33% in clean environment, 10dB and 20dB, respectively.

Performance comparison of clustering and thresholding algorithms for tuberculosis bacilli segmentation

Image segmentation is a key step in most medical image analysis. However, the process is particularly difficult due to limitation of the imaging equipments and variation in biological system. Therefore, accurate and robust segmentation are important for quantitative assessment of medical images in order to achieve correct clinical diagnosis. This paper studies the performance of clustering and adaptive thresholding algorithms for segmenting the tuberculosis (TB) bacilli in tissue sections. Images are obtained by analyzing the Ziehl-Neelsen (ZN) stained tissue slide and capturing using a digital camera attached to a light microscope. Three clustering algorithms namely k-mean clustering, moving k-mean clustering and fuzzy c-mean clustering, and two adaptive thresholding algorithms, Otsu and iterative thresholding, are evaluated for segmentation of TB bacilli. The saturation component, derived from C-Y colour model is utilised as input to these algorithms as it provides good separation between the TB bacilli and the background. The segmentation results are further compared with the manual-segmentation image. Acceptable segmentation accuracy of up to 99.00% was achieved by using the clustering algorithms and the Otsus thresholding. However, k-mean clustering was chosen as it produced the highest TB segmentation rate.

A robust and fast computation touchless palm print recognition system using LHEAT and the IFkNCN classifier

Mobile implementation is a current trend in biometric design. This paper proposes a new approach to palm print recognition, in which smart phones are used to capture palm print images at a distance. A touchless system was developed because of public demand for privacy and sanitation. Robust hand tracking, image enhancement, and fast computation processing algorithms are required for effective touchless and mobile-based recognition. In this project, hand tracking and the region of interest (ROI) extraction method were discussed. A sliding neighborhood operation with local histogram equalization, followed by a local adaptive thresholding or LHEAT approach, was proposed in the image enhancement stage to manage low-quality palm print images. To accelerate the recognition process, a new classifier, improved fuzzy-based k nearest centroid neighbor (IFkNCN), was implemented. By removing outliers and reducing the amount of training data, this classifier exhibited faster computation. Our experimental results demonstrate that a touchless palm print system using LHEAT and IFkNCN achieves a promising recognition rate of 98.64%.

Robust palm print verification system based on evolution of kernel principal component analysis

Palm print is an emerging type of biometric that attracts researchers in biometrics area. As compared to the other biometric traits such as face, fingerprint and iris, the image quality of a fingerprint is robust with more information can be employed even though it is in low resolution. A new approach in feature extraction called evolution of kernel principal component analysis (Evo-KPCA) was proposed to speed up the processing time in the extraction stage. It used a reduced set density estimate (RSDE) to define a weighted gram matrix. As a result, the Evo-KPCA only extracted the most relevant and important information from a dataset. A total of 2400 palm print images was collected from three types of android mobiles. An experimental evaluation showed that the Evo-KPCA performed well in term of processing and accuracy compared to the region of interest (ROI), principle component analysis (PCA) and kernel principal component (KPCA) with the Genuine Acceptance Rates (GAR) of more than 98% and shorter processing time of less than 0.5s.

Frog Identification System Based on Local Means K-Nearest Neighbors with Fuzzy Distance Weighting

Frog identification based on the vocalization becomes important for biological research and environmental monitoring. As a result, different types of feature extractions and classifiers have been employed. Yet, the k-nearest neighbor (kNN) is one of the popular classifiers and has been applied in various applications. This paper proposes an improvement of kNN in order to evaluate the accuracy of frog sound identification. The recorded sounds of 12 frog species obtained in Malaysia forest have been segmented using short time energy and short time average zero crossing rate while the features are extracted by mel frequency cepstrum coefficient. Finally, a proposed classifier based on local means kNN and fuzzy distance weighting have been employed to identify the frog species. Comparison of the system performances based on kNN, local means kNN and the proposed classifier i.e. fuzzy kNN with manual segmentation and automatic segmentation is evaluated. The results show the proposed classifier outperforms the baseline classifier with accuracy of 94.67 % and 98.33 % for manual and automatic segmentation, respectively.

Peak Finding Algorithm to Improve Syllable Segmentation for Noisy Bioacoustic Sound Signal

Abstract Automated identification of animals based their acoustic sound is now preferable by biologist in assisting them to identify animal species for environmental monitoring work. This approach is gradually replacing manual techniques that claimed to be costly and time-consuming. However, it is a challenging task to execute the automated system when the environment is in noisy condition especially in the presence of non-stationary noises such as insect sounds or multiple animal sounds from different species. In this paper, a combination of enhanced start and end point detection namely short time energy (STE) and short time average zero crossing rates (STAZCR) is proposed to improve the syllable segmentation. In this approach, a novel peak finding algorithm is integrated to iteratively narrow down the numbers of local minima and maxima in order to determine the true local maximum value. In this study, the bioacoustics sound samples from frog call database, consists of six hundred and seventy-five frog call data from 15 frog species, recorded in forests located in Kulim and Baling, Malaysia are used. The experimental results demonstrate that 94.13% of performance is achieved by using the proposed method i.e. combination of STE and STAZCR compared to 81.6% of performance for the baseline method, i.e. the combination of the energy and ZCR.

Finger vein identification using fuzzy-based k-nearest centroid neighbor classifier

In this paper, a new approach for personal identification using finger vein image is presented. Finger vein is an emerging type of biometrics that attracts attention of researchers in biometrics area. As compared to other biometric traits such as face, fingerprint and iris, finger vein is more secured and hard to counterfeit since the features are inside the human body. So far, most of the researchers focus on how to extract robust features from the captured vein images. Not much research was conducted on the classification of the extracted features. In this paper, a new classifier called fuzzy-based k-nearest centroid neighbor (FkNCN) is applied to classify the finger vein image. The proposed FkNCN employs a surrounding rule to obtain the k-nearest centroid neighbors based on the spatial distributions of the training images and their distance to the test image. Then, the fuzzy membership function is utilized to assign the test image to the class which is frequently represented by the k-nearest centroid n...

Automatic Detection of Embolic Signal for Stroke Prevention

Transcranial Doppler (TCD) ultrasound is an essential tool in clinical diagnosis to determine the occurrence of embolism in stroke patients. However, it requires manual attention and the accuracy will deteriorate due to fatigue factor. Instead of depending on human observer as a gold standard to detect the emboli, this study proposes an automated emboli detection system based on three detection methods i.e. time-domain intensity, frequency-domain intensity and time-frequency intensity hybrid. Experimental studies of 240 samples of six data sets were employed. The performance evaluations of each method are measured in term of accuracy percentage and processing speed while human observation is also done as the golden standard for accuracy comparison. The best result is achieved by the time-frequency intensity hybrid method where 90.74 % of the embolic signals and 100 % of the non-embolic signals were successfully identified. The performance of this method is promising as the accuracy achieved by human observation was 87.45 and 100 % for embolic signals and non-embolic signals, respectively.