M. E. Nia

Image noise cross-correlation for signal-to-noise ratio estimation in scanning electron microscope images.

A new and robust parameter estimation technique, named image noise cross-correlation, is proposed to predict the signal-to-noise ratio (SNR) of scanning electron microscope images. The results of SNR and variance estimation values are tested and compared with nearest neighborhood and first-order interpolation. Overall, the proposed method is best as its estimations for the noise-free peak and SNR are most consistent and accurate to within a certain acceptable degree, compared with the others.

Signal-to-noise ratio estimation on SEM images using cubic spline interpolation with Savitzky–Golay smoothing

A new technique based on cubic spline interpolation with Savitzky–Golay noise reduction filtering is designed to estimate signal‐to‐noise ratio of scanning electron microscopy (SEM) images. This approach is found to present better result when compared with two existing techniques: nearest neighbourhood and first‐order interpolation. When applied to evaluate the quality of SEM images, noise can be eliminated efficiently with optimal choice of scan rate from real‐time SEM images, without generating corruption or increasing scanning time.

Noise Variance Estimation Using Image Noise Cross-Correlation Model on SEM Images

A number of techniques have been proposed during the last three decades for noise variance and signal-to-noise ratio (SNR) estimation in digital images. While some methods have shown reliability and accuracy in SNR and noise variance estimations, other methods are dependent on the nature of the images and perform well on a limited number of image types. In this article, we prove the accuracy and the efficiency of the image noise cross-correlation estimation model, vs. other existing estimators, when applied to different types of scanning electron microscope images.

Performance of new signal-to-noise ratio estimation for SEM images based on single image noise cross-correlation.

A new technique for estimation of signal‐to‐noise ratio in scanning electron microscope images is reported. The method is based on the image noise cross‐correlation estimation model recently developed. We derive the basic performance limits on a single image signal‐to‐noise ratio estimation using the Cramer–Rao inequality. The results are compared with those from existing estimation methods including the nearest neighbourhood (the simple method), the first order linear interpolator, and the autoregressive based estimator. The comparisons were made using several tests involving different images within the performance bounds. From the results obtained, the efficiency and accuracy of image noise cross‐correlation estimation technique is considerably better than the other three methods.

Signal-to-noise ratio enhancement on SEM images using a cubic spline interpolation with Savitzky-Golay filters and weighted least squares error.

A new technique based on cubic spline interpolation with Savitzky–Golay smoothing using weighted least squares error filter is enhanced for scanning electron microscope (SEM) images. A diversity of sample images is captured and the performance is found to be better when compared with the moving average and the standard median filters, with respect to eliminating noise. This technique can be implemented efficiently on real‐time SEM images, with all mandatory data for processing obtained from a single image. Noise in images, and particularly in SEM images, are undesirable. A new noise reduction technique, based on cubic spline interpolation with Savitzky–Golay and weighted least squares error method, is developed. We apply the combined technique to single image signal‐to‐noise ratio estimation and noise reduction for SEM imaging system. This autocorrelation‐based technique requires image details to be correlated over a few pixels, whereas the noise is assumed to be uncorrelated from pixel to pixel. The noise component is derived from the difference between the image autocorrelation at zero offset, and the estimation of the corresponding original autocorrelation. In the few test cases involving different images, the efficiency of the developed noise reduction filter is proved to be significantly better than those obtained from the other methods. Noise can be reduced efficiently with appropriate choice of scan rate from real‐time SEM images, without generating corruption or increasing scanning time.

Adaptive noise Wiener filter for scanning electron microscope imaging system

Noise on scanning electron microscope (SEM) images is studied. Gaussian noise is the most common type of noise in SEM image. We developed a new noise reduction filter based on the Wiener filter. We compared the performance of this new filter namely adaptive noise Wiener (ANW) filter, with four common existing filters as well as average filter, median filter, Gaussian smoothing filter and the Wiener filter. Based on the experiments results the proposed new filter has better performance on different noise variance comparing to the other existing noise removal filters in the experiments.

Breast cancer detection from MR images through an auto-probing discrete Fourier transform system

A computer-aided detection auto-probing (CADAP) system is presented for detecting breast lesions using dynamic contrast enhanced magnetic resonance imaging, through a spatial-based discrete Fourier transform. The stand-alone CADAP system reduces noise, refines region of interest (ROI) automatically, and detects the breast lesion with minimal false positive detection. The lesions are then classified and colourised according to their characteristics, whether benign, suspicious or malignant. To enhance the visualisation, the entire analysed ROI is constructed into a 3-D image, so that the user can diagnose based on multiple views on the ROI. The proposed method has been applied to 101 sets of digital images, and the results compared with the biopsy results done by radiologists. The proposed scheme is able to identify breast cancer regions accurately and efficiently.

Improvement to the scanning electron microscope image adaptive canny optimization colorization by pseudo-mapping

An improvement to the previously proposed adaptive Canny optimization technique for scanning electron microscope image colorization is reported. The additional feature, called pseudo-mapping technique, is that the grayscale markings are temporarily mapped to a set of pre-defined pseudo-color map as a mean to instill color information for grayscale colors in chrominance channels. This allows the presence of grayscale markings to be identified; hence optimization colorization of grayscale colors is made possible. This additional feature enhances the flexibility of scanning electron microscope image colorization by providing wider range of possible color enhancement. Furthermore, the nature of this technique also allows users to adjust the luminance intensities of selected region from the original image within certain extent.

Modified HL contrast enhancement technique for breast MR images

Magnetic resonance imaging (MRI) has higher sensitivity than mammography in breast cancer detection. However, the low contrast images produced often process difficulties in segmenting the images into regions of interest. There are various contrast enhancement techniques proposed over the years. Although these techniques shows evident contrast enhancement on general images, most of them are not suitable to apply to breast MRI images due to large portion of dark background and close gray levels between grandular tissues and fatty tissues. In this paper, a modified version of hyperbolic logarithm contrast enhancement technique is introduced. Comparisons are made visually and statistically with several existing contrast enhancement techniques.

Brain Ventricle Detection Using Hausdorff Distance

The brain ventricular system can be often affected by different kinds of brain lesions. Using Hausdorff distance is a simple and effective method to detect various brain structures. In this chapter we develop a model to detect the ventricles using Hausdorff distance. The model is first generated using the boundary values of a ventricle. An image set of brain scans is produced, and the images are then compared with the model using the Hausdorff distance.