Speckle noise reduction is an important technique to enhance the quality of ultrasonic image. In this paper, a
despeckling algorithm based on an adaptive block-based singular value decomposition filtering (BSVD) applied on
ultrasonic images is presented. Instead of applying BSVD directly to ultrasonic image, we propose to apply BSVD on
the noisy edge image version obtained from the difference between the logarithmic transformations of the original image
and blur image version of its. The recovered image is performed by combining the speckle noise-free edge image with
blur image version of its. Finally, exponential transformation is applied in order to get the reconstructed image. To
evaluate our algorithm compared with well-know algorithms such as Lee filter, Kuan filter, Homomorphic Wiener filter,
median filter and wavelet soft thresholding, four image quality measurements, which are Mean Square Error (MSE),
Signal to MSE (S/MSE), Edge preservation (β), and Correlation measurement (ρ), are used. From the results, it
clearly shows that the proposed algorithm outperforms other methods in terms of quantitative and subjective
assessments.
Since the natural rocks have quite different textures even they are in the same class, it is very difficult and
challenging task to classify each type of natural rocks. In this paper, we present a method to classify each type of rocks
using the modified version of Spatial Frequency Measurement (SFM). In our approach, each type of color rock images
are firstly transformed into two dimensional intensity features, obtained from the highest and lowest eigenvalues of the
Principle Component Analysis (PCA). The highest and lowest eigenvalues are corresponded to the most and least
significant feature components. Next, the textural contents of each component are measured using the modified version
of SFM, which measures all overall activity level of each component in two directions including vertical, horizontal
directions by shifting one by one pixel for two-neighborhood pixels in both direction. Before applying modified version
of SFM, the edge detection operator, Sobel operator, is applied to the most significant component only. After applying
the modified version of SFM to both components, two textural features are used to define each type of rock. In our
experiments, we test our approach to classify on 14 different classes of rock textures, each class has 30 samples. From
the results, we found that the scatter plots of each type of rock features are obviously grouped and stuck together in the
same class while the different classes are clearly separated.
Accurate measurement of blood vessel diameter on ultrasonic images is a potential part in diagnosis and physiological study. In this paper, a method of blood vessel diameter measurement has been presented. In our approach, we firstly used a histogram equalization technique to enhance the contrast of ultrasonic images. Secondly, a Guassian filter is used to remove the noisy pixels while preserving the important information. Next, we applied Haar wavelet filter to extract the blood vessel shape. Moreover, we used the nonmaxima suppression and thresholding techniques to get the blood vessel shape in the binary format. Finally, the users have to drag a mouse to measure the diameter of blood vessels. From the results, we found that our approach yields promising results compared with conventional methods and other mother wavelets. An ultrasonographer also confirmed the results. Therefore, our approach leads to an effective method for blood vessel diameter measurement.
In this work, we proposed a method to detect roads in aerial imagery. In our approach, we applied the wavelet transform in a multiresolution sense by forming the products of wavelet coefficients at the different scales to locate and identify roads. After detecting possible road pixels, we used a graph searching algorithm to identify roads. We found that our approach leads to an effective method to form the basis of a road extraction approach.
We proposed a new line detection method in noisy images using Mexican hat wavelet filters. In our approach, we applied the wavelet transform in a multiresolution sense by forming the products of wavelet coefficients at the different scales to locate and identify lines at different scales. In addition, we also considered shifting line locations through multiple scales for robust line detection in the presence of noise. We found that our approach leads to an effective method to form the basis of a line detection approach.
We detected roads in aerial imagery based on multiresolution linear feature detection. Our method used the products of wavelet coefficients at several scales to identify and locate linear features. After detecting possible road pixels, we used a shortest-path algorithm to identify roads. The multiresolution approach effectively increased the size of the region we examined when looking for possible road pixels and reduced the effect of noise. We found that our approach leads to an effective method for detecting roads in aerial imagery.
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