@inproceedings {256, title = {Organ recognition using Gabor filters}, booktitle = {7th Canadian Conference on Computer and Robot Vision}, year = {2010}, abstract = {

The aim of this research is to investigate the possibility of using medical image information to extract unique features and classify different patients\&$\#$39; organ tissues, such as the prostate, based on concepts related to what is already done in iris recognition. This paper therefore presents a new approach in medical imaging, an organ recognition system, tested on a standard database of grey scale prostate images in order to validate its performance. In this research, features of the prostate image were encoded by convolving the normalized organ region with a 2D Gabor filter and then quantizing its phase in order to produce a bit-wise biometric template. Our experiments prove that prostate patterns have a low degree of freedom to be used in organ recognition systems and inter-class and intraclass distributions are highly correlated. However, there are still open issues that need to be addressed for future work on organ recognition, including precise segmentation and intensive computation cost.

}, keywords = {2D Gabor filter, bit wise biometric template, Feature Extraction, Gabor filters, grey scale prostate image, grey systems, medical image information, medical image processing, object detection, organ recognition system, patient treatment, patients organ tissues classification}, doi = {http://dx.doi.org/10.1109/CRV.2010.19}, author = {S Zaboli and A Tabibazar and P Fieguth} } @inproceedings {789, title = {Average cell orientation, shape and size estimated from tissue images}, booktitle = {2nd Annual Canadian Conference on Computer and Robot Vision}, year = {2005}, month = {05/2005}, address = {Victoria, B.C., Canada}, abstract = {

Four computer vision algorithms to measure the average orientation, shape and size of cells in images of biological tissue are proposed and tested. These properties, which can be embodied by an elliptical \&$\#$39;composite cell\&$\#$39; are crucial for biomechanical tissue models. To automatically determine these properties is challenging due to the diverse nature of the image data, with tremendous and unpredictable variability in illumination, cell pigmentation, cell shape, and cell boundary visibility. First, a simple edge detection routine is performed on the raw images to locate cell edges and remove pigmentation variation. The edge map is then converted into the magnitude spatial-frequency domain where the spatial patterns of the cells appear as energy impulses. Four candidate methods that analyze the spatial-frequency data to estimate the properties of the composite cell are presented and compared. These methods are: least squares ellipse fitting, correlation, area moments and Gabor filters. Robustness is demonstrated by successful application on a wide variety of real images.

}, keywords = {area moments, average cell orientation, average cell shape, average cell size, biological tissue, biological tissues, biology computing, biomechanical tissue models, cell boundary visibility, cell geometry, cell pigmentation, Computer Vision, edge detection, elliptical composite cell, embryology, Gabor filters, least squares ellipse fitting, magnitude spatial-frequency domain, medical image processing, morphogenesis, shape detection, simple edge detection, statistical analysis, tissue images}, doi = {http://dx.doi.org/10.1109/CRV.2005.22}, author = {P Iles and D A. Clausi and S Puddister and G W. Brodland} } @inproceedings {421, title = {A Gabor based technique for image denoising}, booktitle = {18th Canadian Conference on Electrical and Computer Engineering}, year = {2005}, address = {Saskatoon}, abstract = {

As an alternative to the wavelet, Gabor function has been used as an efficient representation of two dimensional signals. We are interested in BayesShrink techniques for image denoising, and have shown in our previous work that BayesShrink Ridgelet performs better than VisuShrink ridgelet and VisuShrink wavelet. In this paper, a dyadic Gabor filter bank is combined with BayesShrink method for image denoising. In the proposed method, the noisy image is decomposed to different channels in several levels by a dyadic Gabor filter bank. To recover the image, the corrupting noise is removed by applying the proposed BayesShrink method on the noisy Gabor coefficients. The noise variance is estimated in Gabor domain and the estimated noise is then used to dynamically calculate an individual threshold for each spatio-frequency channel. Finally denoised coefficients are transformed back to reconstruct the image

}, keywords = {Bayes methods, BayesShrink ridgelet, channel bank filters, dyadic Gabor filter banks, Gabor based technique, Gabor filters, Image Denoising, image reconstruction, image representation, noise variance, spatiofrequency channels, two-dimensional signal representation, VisuShrink ridgelet, wavelet transforms}, doi = {http://dx.doi.org/10.1109/CCECE.2005.1557140}, author = {N Nexamoddin Kachouie and P Fieguth} } @article {589, title = {K-means Iterative Fisher (KIF) unsupervised clustering algorithm applied to image texture segmentation}, journal = {Pattern Recognition}, volume = {35}, year = {2002}, pages = {1959 - 1972}, abstract = {

The K-means Iterative Fisher (KIF) algorithm is a robust, unsupervised clustering algorithm applied here to the problem of image texture segmentation. The KIF algorithm involves two steps. First, K-means is applied. Second, the K-means class assignments are used to estimate parameters required for a Fisher linear discriminant (FLD). The FLD is applied iteratively to improve the solution. This combined K-means and iterative FLD is referred to as the KIF algorithm. Two KIF implementations are presented: a mixture resolving approach is extended to an unsupervised binary hierarchical approach. The same binary hierarchical KIF algorithm is used to properly segment images even though the number of classes, the class spatial boundaries, and the number of samples per class vary. The binary hierarchical KIF algorithm is fully unsupervised, requires no a priori knowledge of the number of classes, is a non-parametric solution, and is computationally efficient compared to other methods used for clustering in image texture segmentation solutions. This unsupervised methodology is demonstrated to be an improvement over other published texture segmentation results using a wide variety of test imagery. Gabor filters and co-occurrence probabilities are used as texture features.

}, keywords = {Binary hierarchical clustering, Co-occurrence probabilities, Computer Vision, Fisher linear discriminant, Gabor filters, image segmentation, K-means, texture segmentation}, issn = {0031-3203}, doi = {http://dx.doi.org/10.1016/S0031-3203(01)00138-8}, url = {http://www.sciencedirect.com/science/article/pii/S0031320301001388}, author = {D A. Clausi} } @inproceedings {532, title = {Improved texture recognition of SAR sea ice imagery by data fusion of MRF features with traditional methods}, booktitle = {IEEE Geoscience and Remote Sensing Symposium}, year = {2001}, abstract = {

Image texture interpretation is an important aspect of the computer-assisted discrimination of SAR sea ice imagery. Co-occurrence probabilities are the most common approach to solve this problem. However, other texture feature extraction methods exist that have not been fully studied for their ability to interpret SAR sea ice imagery. Gabor filters and Markov random fields (MRF) are two such methods considered. Classification and significance level testing shows that co-occurrence probabilities classify the data with the highest classification rate, with Gabor filters a close second. MRF results significantly lag Gabor and co-occurrence results. However, the MRF features are uncorrelated with respect to co-occurrence and Gabor features. The fused co-occurrence/MRF feature set achieves higher performance

}, keywords = {classification rate, Data fusion, Gabor filters, image texture, image texture interpretation, Markov Random Fields, occurrence probabilities, oceanographic techniques, remote sensing by radar, SAR sea ice imagery, sea ice, sensor fusion, significance level testing, synthetic aperture radar, texture feature extraction methods, texture recognition}, doi = {http://dx.doi.org/10.1109/IGARSS.2001.976781}, author = {D A. Clausi} }