Brain lesion segmentation from diffusion weighted MRI based on adaptive thresholding and gray level co-occurrence matrix

This project presents brain lesion segmentation of diffusion-weighted magnetic resonance images (DWI) based on thresholding technique and gray level co-occurrence matrix (GLCM). The lesions are hyperintense lesion from tumour, acute infarction, haemorrhage and abscess, and hypointense lesion from chronic infarction and haemorrhage. Pre-processing is applied to the DWI for intensity normalization, background removal and intensity enhancement. Then, the lesions are segmented by using two different methods which are thresholding technique and GLCM. For the thresholding technique, image histogram is calculated at each region to find the maximum number of pixels for each intensity level. The optimal threshold is determined by comparing normal and lesion regions. Conversely, GLCM is computed to segment the lesions. Different peaks from the GLCM crosssection indicate the present of normal brain region, cerebral spinal fluid (CSF), hyperintense or hypointense lesions. Minimum and maximum threshold values are computed from the GLCM cross-section. Region and boundary information from the GLCM are introduced as the statistical features for segmentation of hyperintense and hypointense lesions. The proposed technique has been validated by using area overlap (AO), false positive rate (FPR), false negative rate (FNR), misclassified area (MA), mean absolute percentage error (MAPE) and pixels absolute error ratio (rerr). The results are demonstrated in three indexes MA, MAPE and rerr, where 0.3167, 0.1440 and 0.0205 for GLCM, while 0.3211, 0.1524 and 0.0377 for thresholding technique. Overall, GLCM provides better segmentation performance compared to thresholding technique

Automated Region Growing for Segmentation of Brain Lesion in Diffusion-weighted MRI

This paper presents an automatic segmentation of brain lesions from diffusion-weighted magnetic resonance imaging (DW-MRI or DWI) using region growing approach. The lesions are acute infarction, haemorrhage, tumour and abscess. Region splitting and merging is used to detect the lesion region. Then, histogram thresholding technique is applied to automate the seeds selection. The region is iteratively grown by comparing all unallocated neighbour pixels to the seeds. The difference between pixel’s intensity value and the region’s mean is used as the similarity measure. Evaluation is made for performance comparison between automatic and manual seeds selection. Overall, automated region growing algorithm provides comparable results with the semi-automatic segmentation

Detection of heart blocks in ECG signal by spectral estimation techniques

The electrocardiogram (ECG) is a non-invasive test that records the electrical activity of the heart and is important in the investigation of cardiac abnormalities. Each portion of the ECG waveform carries various types of information for the cardiologists analyzing patient's heart condition. ECG interpretation at the present time remains dependent manually in time domain. It is difficult for the cardiologists to make a correct diagnosis of cardiac disorder. A computerized interpretation of ECG is needed in order to make the diagnosis more efficient. This paper discusses the use of digital signal processing approaches for the detection of heart blocks in ECG signals. Spectral estimations such as the periodogram power spectrum, Blackman-Tukey power spectrum and spectrogram time1requency distribution are employed to analyze ECG variations. Window functions are applied to the spectrums which are Boxcar, Hamming and Bartlett window. Seven subjects are identified: normal, first degree heart block, second degree heart block type /, second degree heart block type II, Third degree heart block, right bundle branch block and left bundle branch block. Analysis results revealed that normal ECG subject is able to maintain higher peak frequency range (8 Hz), while heart block subjects revealed a significant low peak frequency range ( < 4 Hz) for both the periodogram and Blackman-Tukey method. The results revealed that the periodogram power spectrum with Boxcar window can be used to differentiate between normal and heart block subjects, while the spectogram time-frequency distribution is used to give better characterization of ECG parameters in term three dimension: time, frequency and power intensity. These analyses can be used to construct ECG monitoring and analyzing system for heart blocks detection

Power quality analysis using spectrogram and gabor transformation

This paper discusses the implementation of time-frequency analysis techniques to analyze power quality disturbances. The approached methods are spectrogram and Gabor transform algorithms. Signal parameters such as time marginal and frequency marginal are extracted from the time-frequency distributions. The parameters are analyzed in terms of correctness measurement of root mean square (RMS), total harmonic distortion (THD), total waveform distortion (TWD) and total interharmonic distortion (TnHD) values. Power quality events that are analyzed are swell, sag, interruption, harmonic, interharmonic, transient, notching and normal voltage. The results show that Gabor transform provides better performance in terms of correctness of parameters measurement, window length, frequency resolution and memory size

Development of an eeg amplifier for real-time acquisition

Electroencephalography (EEG) are primarily use for diagnosis, detect and localize cerebral brain lesions, aid in the studying of epilepsy, diagnosing mental disorders, assist in diagnosing sleep patterns and allowing observation and analysis of brain responses to sensory stimuli. To date, advance researchers have developed system that use EEG signals to decipher thoughts so that a person can communicate by means of ain activity brain activity alone. The purpose of this project is to develop an EEG amplifier that forms part of an EEG signal acquisition system with the intention to form a basis for EEG research. The hardware includes amplifications and filtering circuit and a personal computer (PC) while the software allows to store the recorded EEG data in all-time

Study Of EMG Feature Selection For Hand Motions Classification

In recent days, electromyography (EMG) pattern recognition has becoming one of the major interests in rehabilitation area. However, EMG feature set normally consists of relevant, redundant and irrelevant features. To achieve high classification performance, the selection of potential features is critically important. Thus, this paper employs two recent feature selection methods namely competitive binary gray wolf optimizer (CBGWO) and modified binary tree growth algorithm (MBTGA) to evaluate the most informative EMG feature subset for efficient classification. The experimental results show that CBGWO and MBTGA are not only improves the classification performance, but also reduces the number of features

A New Quadratic Binary Harris Hawk Optimization For Feature Selection

Harris hawk optimization (HHO) is one of the recently proposed metaheuristic algorithms that has proven to be work more effectively in several challenging optimization tasks. However, the original HHO is developed to solve the continuous optimization problems, but not to the problems with binary variables. This paper proposes the binary version of HHO (BHHO) to solve the feature selection problem in classification tasks. The proposed BHHO is equipped with an S-shaped or V-shaped transfer function to convert the continuous variable into a binary one. Moreover, another variant of HHO, namely quadratic binary Harris hawk optimization (QBHHO), is proposed to enhance the performance of BHHO. In this study, twenty-two datasets collected from the UCI machine learning repository are used to validate the performance of proposed algorithms. A comparative study is conducted to compare the effectiveness of QBHHO with other feature selection algorithms such as binary differential evolution (BDE), genetic algorithm (GA), binary multi-verse optimizer (BMVO), binary flower pollination algorithm (BFPA), and binary salp swarm algorithm (BSSA). The experimental results show the superiority of the proposed QBHHO in terms of classification performance, feature size, and fitness values compared to other algorithms

Classification of Myoelectric Signal using Spectrogram Based Window Selection

This paper presents a study of the classification of myoelectric signal using spectrogram with different window sizes. The electromyography (EMG) signals of 40 hand movement types are collected from 10 subjects through NinaPro database. By employing spectrogram, the EMG signals are represented in time-frequency representation.&nbsp; Ten features are extracted from spectrogram for performance evaluation. In this study, two classifiers namely support vector machine (SVM) and linear discriminate analysis (LDA) are used to evaluate the performance of spectrogram features in the classification of EMG signals. To determine the best window size in spectrogram, three different Hanning window sizes are examined. The experimental results indicate that by applying spectrogram with optimize window size and LDA, the highest mean classification accuracy of 91.29% is obtained

A Detail Study of Wavelet Families for EMG Pattern Recognition

Wavelet transform (WT) has recently drawn the attention of the researchers due to its potential in electromyography (EMG) recognition system. However, the optimal mother wavelet selection remains a challenge to the application of WT in EMG signal processing. This paper presents a detail study for different mother wavelet function in discrete wavelet transform (DWT) and continuous wavelet transform (CWT). Additionally, the performance of different mother wavelet in DWT and CWT at different decomposition level and scale are also investigated. The mean absolute value (MAV) and wavelength (WL) features are extracted from each CWT and reconstructed DWT wavelet coefficient. A popular machine learning method, support vector machine (SVM) is employed to classify the different types of hand movements. The results showed that the most suitable mother wavelet in CWT are Mexican hat and Symlet 6 at scale 16 and 32, respectively. On the other hand, Symlet 4 and Daubechies 4 at the second decomposition level are found to be the optimal wavelet in DWT. From the analysis, we deduced that Symlet 4 at the second decomposition level in DWT is the most suitable mother wavelet for accurate classification of EMG signals of different hand movements.

Shape and Level Bottles Detection Using Local Standard Deviation and Hough Transform

This paper presents shape and level analysis using local standard deviation and Hough transform technique to detect the shape and level of the bottle.A 155 sample images are used as a test product to detect shape and level. Local standard deviation is used contrast gain technique to segment the shape of the bottle by enhancing the contrast of the image. The ratio of the area is calculated from the extent parameter. The maximum and minimum water level is created by using Hough transform technique to identify the level of the water. Decision tree is applied to classify the shape and level of the bottle either good or defect condition. From experimental result, 97% and 93% accuracy of shape and level is achieved which shows that the proposed analysis technique is potential to be applied for beverages product inspection system