A novel multi-scale Hessian based spot enhancement filter for two dimensional gel electrophoresis images.

Two dimensional gel electrophoresis (2DGE) is a useful method for studying proteins in a wide variety of applications including identifying post-translation modification (PTM), biomarker discovery, and protein purification. Computerized segmentation and detection of the proteins are the two main processes that are carried out on the scanned image of the gel. Due to the complexities of 2DGE images and the presence of artifacts, the segmentation and detection of protein spots in these images are non-trivial, and involve supervised and time consuming processes. This paper introduces a new spot filter for enhancing, and separating the closely overlapping spots of protein in 2DGE images based on the multi-scale eigenvalue analysis of the image Hessian. Using a Gaussian spot model, we have derived closed form equations to compute the eigen components of the image Hessian of two overlapping spots in a multi-scale fashion. Based on this analysis, we have proposed a novel filter that suppresses the overlapping area and results in a better spot separation. The performance of the proposed filter has been evaluated on the synthetic and real 2DGE images. The comparison with three conventional techniques and a commercial software package reveals the superiority and effectiveness of the proposed filter.

Improving the diagnostic accuracy of dysplastic and melanoma lesions using the decision template combination method.

BACKGROUND/PURPOSE: Melanoma is the most dangerous type of skin cancer, and early detection of suspicious lesions can decrease the mortality rate of this cancer. In this article, we present a multi-classifier system for improving the diagnostic accuracy of melanoma and dysplastic lesions based on the decision template combination rule. METHODS: First, the lesion is differentiated from the surrounding healthy skin in an image. Next, shape, colour and texture features are extracted from the lesion image. Different subsets of these features are fed to three different classifiers: k-nearest neighbour (k-NN), support vector machine (SVM) and linear discriminant analysis (LDA). The decision template method is used to combine the outputs of these classifiers. RESULTS: The proposed method has been evaluated on a set of 436 dermatoscopic images of benign, dysplastic and melanoma lesions. The final classifier ensemble delivers a total classification accuracy of 80.46%, with 67.73% of dysplastic lesions correctly classified and 83.53% of melanoma lesions correctly classified. CONCLUSION: The results show that the proposed method significantly increases the diagnostic accuracy of dysplastic and melanoma lesions compared with a single classifier. The total classification rate is also improved.

Singular value analysis of the Mueller matrix in comparison with eigenvalue analysis of the coherency matrix in polarization characterization of media.

Singular values of the arbitrary Mueller matrix are determined to be indicators of some polarization properties of the medium such as depolarization and diattenuation. Whereas eigenvalue analysis of the coherency matrix may wrongly characterize media with simultaneous strong depolarization and diattenuation effects. The comparison between the patterns of changes in singular-value and eigenvalue trends of the coherency matrix in experimental Mueller matrices, shows that singular values of the Mueller matrix are more capable of discriminating media with close degrees of depolarization.

Quantitative assessment of pressure sore generation and healing through numerical analysis of high-frequency ultrasound images.

Abstract-This article focuses on the development of a method to quantitatively assess the healing process of artificially induced pressure sores using high-frequency (20 MHz) ultrasound images. We induced sores in guinea pigs and monitored predefined regions on days 3, 7, 14, and 21 after sore generation. We extracted relevant parameters regarding the tissue echographic structure and attenuation properties. We examined tissue healing by defining a healing function that used the extracted parameters. We verified the significance of the extracted features by using analysis of variance and multiple comparison tests. The features displayed ascending/descending behavior during wound generation and reverse behavior during healing. We optimized the parameters of our healing function by using a pattern search method. We tested the efficiency of the optimized values by calculating the healing function value on assessment days and then comparing these results with the expected pattern of changes in the tissue conditions after removing the applied pressure. The results of this study suggest that the methodology developed may be a viable tool for quantitative assessment of pressure sores during their early generation as well as during healing stages.

A data-mining approach to biomarker identification from protein profiles using discrete stationary wavelet transform.

OBJECTIVE: To develop a new bioinformatic tool based on a data-mining approach for extraction of the most informative proteins that could be used to find the potential biomarkers for the detection of cancer. METHODS: Two independent datasets from serum samples of 253 ovarian cancer and 167 breast cancer patients were used. The samples were examined by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). The datasets were used to extract the informative proteins using a data-mining method in the discrete stationary wavelet transform domain. As a dimensionality reduction procedure, the hard thresholding method was applied to reduce the number of wavelet coefficients. Also, a distance measure was used to select the most discriminative coefficients. To find the potential biomarkers using the selected wavelet coefficients, we applied the inverse discrete stationary wavelet transform combined with a two-sided t-test. RESULTS: From the ovarian cancer dataset, a set of five proteins were detected as potential biomarkers that could be used to identify the cancer patients from the healthy cases with accuracy, sensitivity, and specificity of 100%. Also, from the breast cancer dataset, a set of eight proteins were found as the potential biomarkers that could separate the healthy cases from the cancer patients with accuracy of 98.26%, sensitivity of 100%, and specificity of 95.6%. CONCLUSION: The results have shown that the new bioinformatic tool can be used in combination with the high-throughput proteomic data such as SELDI-TOF MS to find the potential biomarkers with high discriminative power.