Selen Ayas

Microscopic image segmentation based on firefly algorithm for detection of tuberculosis bacteria

One third of the world is infected with tuberculosis disease. The disease is diagnosed visually by laboratory technicians. In the microscopy diagnosis with hand-eye control, misdiagnosis rate is quite high. In microscopic imaging, by using computer aided automatic diagnosis methods, the disease is true diagnosed. The robustness of the automatic diagnosis methods depends on accurate segmentation of microscopic images. Image segmentation methods produce a special solution for several problems. In this study, Firefly algorithm based on swarm intelligence as a novel approach in microscopic imaging is proposed to segment images. In the proposed approach, an optimum threshold value in gray-level microscopic images is determined with proposed entropy based Firefly algorithm. Microscopic images are converted to binary format by using obtained optimum threshold value. Segmentation results are compared with expert-guided segmentation results. The performance ratio of segmentation is 96% obtained by using Firefly algorithm based on swarm intelligence.

Image forgery detection based on Colour SIFT

Nowadays several modifications on the digital images are made with the rapid development of image editing tools in recent years. The most common method in the modifications made is copy-move forgery. Majority of the proposed methods to detect copy-move forgery in the literature are based on block and not resistant to various geometric transformation before moving coped image. For this aim the key points of each channel of forged color image are extracted by using Colour SIFT that is the key point-based method. In this study the comparison between SIFT and Colour SIFT is made. More effective forgery detection is made by obtaining more matching points using Colour SIFT than SIFT is seen in the comparison results. Moreover, proposed method detects forged image during rotation, scaling, JPEG compression is shown in the results.

Auto-focusing with multi focus color image fusion based on curvelet transform on microscopic imaging

The fundamental operation before analyzing bacteria on the microscopic system is optimal focusing. Laboratory technicians implement this process with eye-hand coordination. During auto-focus process avoiding the dependence on technicians, auto-focus functions giving a value about focusing of the images are used in literature. At the end of the auto-focusing based on auto-focus functions, some regions in the in-focus image can be blurred. In this paper auto-focusing based color image fusion is implemented to obtain all of region in-focus image. In this study, firstly an image sequence is captured with moving the microscope stage along Z-axis. The reference image with the highest focus value on the sequence is found. Reference image and several images around the reference image are fused with curvelet transform preferred to obtain curve and line information of image. Moreover, various evaluation criteria are utilized to analyze the performance of the proposed auto-focus approach on different color models.

Single image super resolution based on sparse representation using discrete wavelet transform

Single image super resolution (SR) based on sparse representation is a promising technique where the SR problem is solved by searching for the most robust representation of a signal in terms of atoms in a dictionary. However, first and second-order derivatives are always used as features for patches to be trained as dictionaries and super-resolved patches are reconstructed using dictionaries and sparse representation of these features. In this paper, a novel single image SR algorithm based on sparse representation with considering the effect of significant features is proposed. Therefore, high frequency details are preserved using discrete wavelet transform (DWT). In addition, an intermediate process is also proposed to learn finer dictionaries and thereby estimate the sharper and more detailed super-resolved image. The dictionaries are constructed from the distinctive features using K-SVD dictionary learning algorithm. The intermediate process uses approximation subband. Thus, constructed dictionaries contain so much more significant information and the interpolated high frequency components are corrected. Therefore, the intermediate process restores the high frequency details better in super-resolved images. The proposed algorithm was tested on ‘Set14’ dataset. Owing to DWT, the proposed algorithm recovers the edges better as well as improving the computational efficiency. The quantitative and visual results show the superiority and competitiveness of the proposed method over the simplest techniques and state-of-art SR algorithms. Experimental time comparisons with the state-of-art algorithms validate the effectiveness of the proposed approach.

Learning Based Single Image Super Resolution Using Discrete Wavelet Transform

Sparse representation has attracted considerable attention in image restoration field recently. In this paper, we study the implementation of sparse representation on single-image super resolution problem. In recent research, first and second-order derivatives are always used as features for patches to be trained as dictionaries. In this paper, we proposed a novel single image super resolution algorithm based on sparse representation with considering the effect of significant features. Therefore, the super resolution problem is approached from the viewpoint of preservation of high frequency details using discrete wavelet transform. The dictionaries are constructed from the distinctive features using K-SVD dictionary training algorithm. The proposed algorithm was tested on ‘Set14’ dataset. The proposed algorithm recovers the edges better as well as improving the computational efficiency. The quantitative, visual results and experimental time comparisons show the superiority and competitiveness of the proposed method over the simplest techniques and state-of-art SR algorithm.

AUTOMATIC SEGMENTATION OF MYCOBACTERIUM TUBERCULOSIS IN ZIEHL-NEELSEN SPUTUM SLIDE IMAGES USING SUPPORT VECTOR MACHINES

The World Health Organization suggests visual examination of stained sputum smear samples as a preliminary and basic diagnostic technique of tuberculosis disease. The visual examination requires laboratory technicians to spend considerable time, so it increases laboratorians’ workload. In addition, it leads to a misdiagnosis because of requiring mental concentration. This paper presents a novel method for segmentation of tuberculosis bacteria in microscopic images taken from the Ziehl-Neelsen stained samples. Color information of bacterial regions which is taken from pixels and their adjacent pixels is sampled in training process. Multidimensional Gaussian probability density function and support vector machines are used during microscopic image segmentation comparatively. The performance of the implemented system is evaluated using sensitivity, specificity and accuracy criteria.