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Articles by Sh-Hussain Salleh
Total Records ( 3 ) for Sh-Hussain Salleh
  M.M. Rushaidin , Sh-Hussain Salleh , Tan Tian Swee , J.M. Najeb and Adeela Arooj
  Problem statement: ABR machine is a well known machine that has been used on detection of hearing problem especially for babies or children. In ABR, wave V is the most prominent and robust wave that has been used as indicator of hearing loss. However, a fast detection of the wave V is necessary in order to implement newborn hearing screening. There are various types of signal processing methods introduced by researchers in order to achieve the target. Fast Fourier Transform (FFT) and wavelet transform are well known techniques that have been used in digital signal processing. Approach: In this study, the instantaneous energy of ABR signal had been introduced as a marker to identify the ABR waves. Results: Study showed that the instantaneous energy of auditory brainstem response can be used a marker to identify the ABR waves. Conclusion: This study had proposed a platform for fast hearing screening system.
  Hum Yan Chai , Lai Khin Wee , Tan Tian Swee , Sh-Hussain Salleh , A.K. Ariff and Kamarulafizam
  Problem statement: Currently doctors in orthopedic wards inspect the bone x-ray images according to their experience and knowledge in bone fracture analysis. Manual examination of x-rays has multitude drawbacks. The process is time-consuming and subjective. Approach: Since detection of fractures is an important orthopedics and radiologic problem and therefore a Computer Aided Detection(CAD) system should be developed to improve the scenario. In this study, a fracture detection CAD based on GLCM recognition could improve the current manual inspection of x-ray images system. The GLCM for fracture and non-fracture bone is computed and analysis is made. Features of Homogeneity, contrast, energy, correlation are calculated to classify the fractured bone. Results: 30 images of femur fractures have been tested, the result shows that the CAD system can differentiate the x-ray bone into fractured and non-fractured femur. The accuracy obtained from the system is 86.67. Conclusion: The CAD system is proved to be effective in classifying the digital radiograph of bone fracture. However the accuracy rate is not perfect, the performance of this system can be further improved using multiple features of GLCM and future works can be done on classifying the bone into different degree of fracture specifically.
  Lih-Heng Chan , Sh-Hussain Salleh and Chee-Ming Ting
  Problem statement: In facial biometrics, face features are used as the required human traits for automatic recognition. Feature extracted from face images are significant for face biometrics system performance. Approach: In this thesis, a framework of facial biometric was designed based on two subspace methods i.e., Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). First, PCA is used for dimension reduction, where original face images are projected into lower-dimensional face representations. Second, LDA was proposed to provide a solution of better discriminant. Both PCA and LDA features were presented to Euclidean distance measurement which is conveniently used as a benchmark. The algorithms were evaluated in face identification and verification using a standard face database-AT and T and a locally collected database-CBE. Each database consists of 400 images and 320 images respectively. Results: LDA-based methods outperform PCA for both face identification and verification. For face identification, PCA achieves accuracy of 91.9% (AT and T) and 76.7% (CBE) while LDA 94.2% (AT and T) and 83.1% (CBE). For face verification, PCA achieves Equal Error Rate (EER) of 1.15% (AT and T), 7.3% (CBE) while LDA 0.78% (AT and T) and 5.81% (CBE). Conclusion/Recommendations: This study had proved that, when given sufficient training samples, LDA is able to provide better discriminant ability in feature extraction for face biometrics.
 
 
 
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