Morteza Zahedi

Brain tissue segmentation in MR images based on a hybrid of MRF and social algorithms

Effective abnormality detection and diagnosis in Magnetic Resonance Images (MRIs) requires a robust segmentation strategy. Since manual segmentation is a time-consuming task which engages valuable human resources, automatic MRI segmentations received an enormous amount of attention. For this goal, various techniques have been applied. However, Markov Random Field (MRF) based algorithms have produced reasonable results in noisy images compared to other methods. MRF seeks a label field which minimizes an energy function. The traditional minimization method, simulated annealing (SA), uses Monte Carlo simulation to access the minimum solution with heavy computation burden. For this reason, MRFs are rarely used in real time processing environments. This paper proposed a novel method based on MRF and a hybrid of social algorithms that contain an ant colony optimization (ACO) and a Gossiping algorithm which can be used for segmenting single and multispectral MRIs in real time environments. Combining ACO with the Gossiping algorithm helps find the better path using neighborhood information. Therefore, this interaction causes the algorithm to converge to an optimum solution faster. Several experiments on phantom and real images were performed. Results indicate that the proposed algorithm outperforms the traditional MRF and hybrid of MRF-ACO in speed and accuracy.

License plate recognition system based on SIFT features

Abstract Scale invariant feature transform (SIFT) describing local features is a robust and reliable method for many pattern recognition purposes and can be applied to a wide range of problems in which local features are critical and helpful, like recognizing characters of license car plates. This work is based on using SIFT for license plate recognition (LPR) considering the capabilities and flaws of using the method. Some cases of failure or bad recognition are improved with various kinds of image preprocessing, however some kind of failures of car plate detection are essential and need more investigation and substitute techniques. Thus, applying a method based on distribution of vertical edges is employed to detect the car plate position. Numerical rate of success employing the proposed method has been given for our database versus pure SIFT for comparison.

Farsi/Arabic optical font recognition using SIFT features

Abstract Most optical font recognition (OFR) methods have been designed to recognize the font in non-cursive documents. However, the recognition of cursive font scripts like Farsi/Arabic texts has its own challenges. Thus, most of the currently proposed algorithms fail to exhibit an appropriate recognition rate when facing cursive documents. In this paper, a new method for Farsi/Arabic automatic font recognition is proposed which is based on scale invariant feature transform (SIFT) method. As SIFT features are scale-invariant, the final system is robust against variation of size, scale and rotation. The system does not need a pre-processing stage but in the case of low quality images some noise removal processes can be used. Using a database of 1400 text images, an excellent recognition rate of nearly 100% is obtained.

Combining Gabor filter and FFT for fingerprint enhancement based on a regional adaption method and automatic segmentation

Fingerprints are the best biometric identity mark due to the consistency during life time and uniqueness. To increase the classification accuracy of fingerprint images, it is necessary to improve image quality which is a key role for correct recognition. In other words, enhancing the fingerprint images leads us to obtain better results in classification of fingerprint images. Although Gabor filter and fast Fourier transform (FFT) are used to enhance fingerprint images, Gabor filter acts better than FFT in detection of incorrect ridge endings and ridge bifurcation, while FFT tries to connect broken ridges together and fill the created holes. This paper tries to enhance gray-scale fingerprint images by combining the Gabor filter and FFT in order to get benefit from the advantages of each enhancing filter (Gabor filter and FFT). A method is proposed for fingerprint image segmentation based on the image histogram and density. By employing the proposed method which enhances the fingerprint images using the better enhancing filter in each part, the experimental results show that the whole finger print is better enhanced, and consequently, it leads to a better recognition rate.

Improving the unsupervised LBG clustering algorithm performance in image segmentation using principal component analysis

In this paper, a new method for improving unsupervised LBG clustering algorithm has been proposed. This algorithm belongs to the hard and

Brain tumor segmentation in 3D MRIs using an improved Markov random field model

K

Linear merging reduction: A workflow diagram simplification method

K-means vector quantization groups and drive directly from a simpler LBG. The defect of the LBG algorithm is to partition cluster in different iterations blindly. The basic idea of this paper is to use of principal component analysis and eigenvalue for handling this issue. Utilizing the eigenvalue in each step of LBG algorithm, it can either prevent from blindly splitting of vector or aggregation of data points in each cluster undoubtedly. The proficiency of eigenvalue-based LBG (E-LBG) algorithm is tested against other clustering algorithm such as Fuzzy