Electrophotographic (EP) print banding, jitter, and ghosting artifacts are common sources of print quality degradation. Traditionally, the characterization of banding and jitter artifacts relies mainly on the assumption that the defect has either a horizontal or vertical orientation which permits the simple 1-D analysis of the defect profile. However, this assumption can easily be violated if a small amount of printer or scanner skew is introduced to the analyzed images. In some cases, the defect can inherently be neither vertical nor horizontal. In this case, unless the defect orientation has been accurately detected before analysis, the 1-D-based approaches could bias the estimation of the defect severity. In this paper, we present an approach to characterize the jitter and banding artifacts of unrestricted orientation using wavelet filtering and 2-D spectral analysis. We also present a new system for detecting and quantifying ghosting defects. It includes a design for a printed test pattern to emphasize the ghosting defect and facilitate further processing and analysis. Wavelet filtering and a template matching technique are used to detect the ghost location along and across the scanned test pattern. A new metric is developed to quantify ghosting based upon its contrast, shape, and location consistency. Our experimental results show that the proposed approaches provide objective measures that quantify EP defects with a rank ordering correlation coefficient of 0.8 to 0.98, as compared to the subjective assessment of print quality experts.

Electrophotographic (EP) printer banding and jitter defects are main sources of print quality degradation. Traditionally, the characterization of these defects relies mainly on the assumption that the defect has either horizontal or vertical orientation to permit the simple ID analysis of the defect profile. However, this assumption can easily be violated if a small amount of printer or scanner skew is introduced to the analyzed images. In some cases the defect can inherently be neither vertical nor horizontal. Unless the defect orientation has been accurately detected before analysis, the ID-based approaches could bias the estimation of the defect severity. In this paper, we present an approach to characterize the skewed jitter defects of unrestricted orientation using 2D-based analysis of Gabor pre-filtering and spectral analysis. Our experimental results show that the proposed approach provides an objective measure that quantifies the EP jitter defect, and correlates to the subjective assessment of print quality experts with a 0.96 correlation coefficient.

Ghosting is a common print defect in electrophotographic printing. Printers experiencing ghosting defects show repeated images (residual images) of previously printed contents in the paper process direction. Detecting the residual image (ghost) location and contrast provides necessary information about the ghosting source and severity. In this paper, we present a new system for detecting and quantifying the ghosting defect. It includes a design for a printed test pattern to emphasize the ghosting defect and facilitate further processing and analysis. Wavelet filtering and a template matching technique are used to detect the ghost location along and across the scanned test pattern. A new metric is developed to quantify ghosting based on its contrast, shape, and location consistency. Experimental results on 31 samples of various types of ghosting showed 0.95 correlation between the proposed method's ranking and experts' visual ranking.

In this paper, we present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the radio-frequency coils or to problems associated with the acquisition sequences. The result is a slowly varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution toward piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.

In this paper, we present a novel algorithm for adaptive fuzzy segmentation of MRI data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the RF coils or some problems associated with the acquisition sequences. The result is a slowly-varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution towards piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.

This paper describes the application of fuzzy set theory in medical imaging, namely the segmentation of brain images. We propose a fully automatic technique to obtain image clusters. A modified fuzzy c-mean (FCM) classification algorithm is used to provide a fuzzy partition. Our new method, inspired from the Markov random field (MRF), is less sensitive to noise as it filters the image while clustering it, and the filter parameters are enhanced in each iteration by the clustering process. We applied the new method on a noisy CT scan and on a single channel MRI scan. We recommend using a methodology of over segmentation to the textured MRI scan and a user guided-interface to obtain the final clusters. One of the applications of this technique is TBI recovery prediction in which it is important to consider the partial volume. It is shown that the system stabilizes after a number of iterations with the membership value of the region contours reflecting the partial volume value. The final stage of the process is devoted to decision making or the defuzzification process.