Browsing by Author "Asyali, Musa Hakan"

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Citation - WoS: 13
Design and Implementation of a Voice-Controlled Prosthetic Hand
(Tubitak Scientific & Technological Research Council Turkey, 2011) Asyali, Musa Hakan; Yilmaz, Mustafa; Tokmakci, Mahmut; Sedef, Kanber; Aksebzeci, Bekir Hakan; Mittal, Rohin; 01. Abdullah Gül University
Current hand prostheses are mostly driven by electromyography (EMG) signal, and existing experiments have proved that multichannel EMG signal controls are not suitable due to early fatigue problems and high effort, requirements to perform even simple activities. Therefore, in this study we present a new voice-controlled active hand prosthesis to perform several basic tasks. We first designed a novel multifingered prosthetic hand with the ability of picking up and releasing objects. The prosthetic hand employs 3 DC motors and gears to transfer motion to the linked parts of the fingers. We used flexible thin-film, resistive force sensors at the fingertips of the prosthetic hand to adjust the grip force at the fingers. The second part of the study involves the use of speech recognition to control the prosthetic hand. The control circuit that we designed consisted of an HM2007 speech recognition IC and a PIC microcontroller to drive the DC motors moving the fingers. We implemented both the prosthetic hand and its speech recognition-based control electronics. As of now, we have programmed the control hardware to recognize simple pick up and release operations and have successfully tested them. In a future study, we will include more voice commands for the operation of the hand, such as a realistic handshake, and improve the cosmetics of the hand in order to make it look more natural.
Citation - WoS: 28
Citation - Scopus: 31
Liver Fibrosis Staging Using CT Image Texture Analysis and Soft Computing
(Elsevier, 2014) Kayaalti, Omer; Aksebzeci, Bekir Hakan; Karahan, Ibrahim Okkes; Deniz, Kemal; Ozturk, Mehmet; Yilmaz, Bulent; Asyali, Musa Hakan; 01. Abdullah Gül University
Liver biopsy is considered to be the gold standard for analyzing chronic hepatitis and fibrosis; however, it is an invasive and expensive approach, which is also difficult to standardize. Medical imaging techniques such as ultrasonography, computed tomography (CT), and magnetic resonance imaging are non-invasive and helpful methods to interpret liver texture, and may be good alternatives to needle biopsy. Recently, instead of visual inspection of these images, computer-aided image analysis based approaches have become more popular. In this study, a non-invasive, low-cost and relatively accurate method was developed to determine liver fibrosis stage by analyzing some texture features of liver CT images. In this approach, some suitable regions of interests were selected on CT images and a comprehensive set of texture features were obtained from these regions using different methods, such as Gray Level Co-occurrence matrix (GLCM), Laws' method, Discrete Wavelet Transform (DWT), and Gabor filters. Afterwards, sequential floating forward selection and exhaustive search methods were used in various combinations for the selection of most discriminating features. Finally, those selected texture features were classified using two methods, namely, Support Vector Machines (SVM) and k-nearest neighbors (k-NN). The mean classification accuracy in pairwise group comparisons was approximately 95% for both classification methods using only 5 features. Also, performance of our approach in classifying liver fibrosis stage of subjects in the test set into 7 possible stages was investigated. In this case, both SVM and k-NN methods have returned relatively low classification accuracies. Our pairwise group classification results showed that DWT, Gabor, GLCM, and Laws' texture features were more successful than the others; as such features extracted from these methods were used in the feature fusion process. Fusing features from these better performing families further improved the classification performance. The results show that our approach can be used as a decision support system in especially pairwise fibrosis stage comparisons. (C) 2014 Elsevier B.V. All rights reserved.
Citation - Scopus: 13
Staging of the Liver Fibrosis From CT Images Using Texture Features
(2012) Kayaaltı, Ömer; Aksebzeci, Bekir Hakan; Karahan, Ökkeş Ibrahim; Deniz, Kemal; Öztürk, Menmet; Yilmaz, Bulent; Asyali, Musa Hakan; 0000-0003-2954-1217; 0000-0001-7476-8141; AGÜ; Aksebzeci, Bekir Hakan; Yilmaz, Bülent; Asyali, Musa Hakan; 01. Abdullah Gül University
Even though liver biopsy is critical for evaluating chronic hepatitis and fibrosis, it is an invasive, costly, and difficult to standardize approach. The developments in medical image processing and artificial intelligence methods have advanced the potential of using computer-aided diagnosis techniques in the classification of liver tissues. The aim of this study was to develop a non-invasive, cost-effective, and fast approach to specify fibrosis stage using the texture properties of computed tomography images of liver. Gray level co-occurrence matrix, discrete wavelet transform, and discrete Fourier transform were the image analysis tools in the feature extraction phase. Following dimension reduction of the texture features support vector machines and k-nearest neighbor methods were used in the classification phase of this study. Our results showed that our approach is feasible in fibrosis staging especially in pairwise stage comparisons with success rate of approximately 90%. © 2012 IEEE. © 2012 Elsevier B.V., All rights reserved.