Biomedical Image Processing / Medical Image Processing
Sina Shamekhi; Mohammad Hosein Miranbeigi; Ali Gooya
Volume 12, Issue 4 , January 2019, , Pages 265-275
Abstract
Matching of the protein spots in two dimensional gel electrophoresis (2DGE) images is a main process of analyzing these images. Due to the challenges of 2DGE images such as the presence of noise and artifacts, the matching of protein spots is performed under human supervision. This supervision involves ...
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Matching of the protein spots in two dimensional gel electrophoresis (2DGE) images is a main process of analyzing these images. Due to the challenges of 2DGE images such as the presence of noise and artifacts, the matching of protein spots is performed under human supervision. This supervision involves human errors. Therefore, in this work a new automated algorithm has been proposed for spot matching in 2DGE images which is based on a probabilistic model. Due to the complexities of the proposed model, the Variational Bayes has been used to solve the equations of the model. The performance of the proposed algorithm has been evaluated on real and synthetic 2DGE images with some statistical criteria. Protein spots in real image dataset have been matched by the proposed method with angular error of 0.05 and end point error of 1.46 and in synthetic image dataset with angular error of 0.13 and end point error of 0.90. These results reveal higher precision and effectiveness and lower matching error of the proposed method.
Biomedical Image Processing / Medical Image Processing
Sina Shamekhi; Mohammad Hossein Miranbaygi; Ali Gooya; Bahare Azarian
Volume 8, Issue 2 , June 2014, , Pages 183-202
Abstract
Two-dimensional gel electrophoresis (2DGE) is a basic and widely used method in proteomics. In this method, mixtures of proteins are separated due to the differences in their molecular weight and isoelectric points and a final image obtained from the separated protein spots is created. Due to the large ...
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Two-dimensional gel electrophoresis (2DGE) is a basic and widely used method in proteomics. In this method, mixtures of proteins are separated due to the differences in their molecular weight and isoelectric points and a final image obtained from the separated protein spots is created. Due to the large number of the protein spots in a 2DGE image and the importance of separation of overlapping proteins, the image processing of these images is a complex process. 2DGE images pose various noises and artifacts such as cracks, staining artifacts, and streaks that affect the reliability of the analysis. In this work, we have proposed a novel spots filter based on the scale-space second order structural Hessian and its eigenvalues for enhancing and separating the spots from the background. Furthermore, in this work, 2DGE images have been segmented and the locations of the spots have been detected. To evaluate and compare the proposed method, we have implemented three methods: Otsu thresholding, Watershed transform, and the method proposed by Mylona et al. Based on the regional spot volume evaluation, the TPR and FPR of the proposed method are 78.6 and 14.9, the TPR and FPR of the Otsu method are equal to 71.4 and 25.7 percent, and the TPR and FPR of the Watershed algorithm are 53.9 and 8.1 percent, respectively. Also, in the spot counts evaluation, the Precision and TPR of the proposed method are equal to 83.6 and 81.1 percent, and the Precision and TPR of Otsu method are 65.4 and 78.3, respectively. The Watershed transform has detected the spots with Precision and TPR equal to 27.7 and 68.2 percent, and the Precision and TPR of the method proposed by Mylona et al. are 74.0 and 72.7 percent, respectively. The results reveal the accuracy and superiority of the proposed method.
