For efficient and accurate diagnosis of ultrasound images, the appropriate time gain compensation (TGC) and dynamic range (DR) control of ultrasound echo signals are important. TGC is used for compensating the attenuation of ultrasound echo signals along the depth, and DR is for controlling the image contrast. In recent ultrasound systems, those two factors are automatically set by a system and/or manually adjusted by an operator to obtain the desired image quality on the screen. In this paper, we propose an algorithm to find the optimized parameter values for TGC and DR automatically. In TGC optimization, we determine the degree of attenuation compensation along the depth by reliably estimating the attenuation characteristic of ultrasound signals. For DR optimization, we define a novel cost function by properly using the characteristics of ultrasound images. Experimental results are obtained by applying the proposed algorithm to a real ultrasound (US) imaging system. The results prove that the proposed algorithm automatically sets values of TGC and DR in real-time so that the subjective quality of the enhanced ultrasound images may become good enough for efficient and accurate diagnosis.
For 2-dimensional B-mode ultrasound images, we propose an image enhancement algorithm based on a multi-resolution approach. In the proposed algorithm, we perform the directional filtering and noise reducing procedures from the coarse to fine resolution images that are obtained from the wavelet-transformed data. For directional filtering, the structural feature at each pixel is examined through the eigen-analysis. Then, if the pixel belongs to the edge region, we perform two-step directional filtering, namely, directional smoothing along the tangential direction of the edge to improve its continuity, and directional sharpening along the normal direction to enhance the contrast. Meanwhile, speckle noise is alleviated by reducing the wavelet coefficients corresponding to the homogeneous region. The reducing rate of the wavelet coefficients is determined by considering the frequency characteristics of speckle. Thereby, the algorithm reduces speckle noise efficiently without affecting the edge sharpness and enhances edges regardless their size. Note that the proposed speckle reduction scheme is based on the structural information rather than the statistics of the magnitude of wavelet coefficients as in the existing methods. The proposed algorithm is compared to the algorithm based on nonlinear anisotropic diffusion filtering and the one based on the wavelet shrinkage scheme. The experimental results show that the proposed algorithm considerably improves the subjective image quality without providing any noticeable artifact.
Angiographic investigation using high-resolution CT images has become a useful tool for the sensitive examination of vascular diseases. However, CT angiography (CTA) does not allow a straightforward understanding of vascular structure, since some of interesting parts can be located near the high intensity skull base region. In this paper, a novel method for fast and automatic 3D digital subtraction CT angiography (DS-CTA) is presented to generate artifact-free angiograms. The proposed method consists of 3D registration to align a CT image to the CTA image, and subtraction and refinement to extract blood vessels. For efficient and accurate 3D registration, an NMI (normalized mutual information) based algorithm is adopted and its fast version is developed by introducing a new measure. The speed-up ratio of the proposed fast registration algorithm is about 1.74~3.01 compared with the conventional registration method. And to improve the subtracted image quality in the second step, a novel 3D refinement algorithm is suggested to effectively remove unwanted residuals. Experimental results of seven clinical CT/CTA head datasets demonstrate that cerebral vessels are well extracted from CTA images with almost no loss. The typical processing time is about 3~9 minutes depending on the image size in a PC with a 2.4GHz CPU.