This paper presents a learning based vessel detection and segmentation method in real-patient ultrasound (US) liver images. We aim at detecting multiple shaped vessels robustly and automatically, including vessels with weak and ambiguous boundaries. Firstly, vessel candidate regions are detected by a data-driven approach. Multi-channel vessel enhancement maps with complement performances are generated and aggregated under a Conditional Random Field (CRF) framework. Vessel candidates are obtained by thresholding the saliency map. Secondly, regional features are extracted and the probability of each region being a vessel is modeled by random forest regression. Finally, a fast levelset method is developed to refine vessel boundaries. Experiments have been carried out on an US liver dataset with 98 patients. The dataset contains both normal and abnormal liver images. The proposed method in this paper is compared with a traditional Hessian based method, and the average precision is promoted by 56 percents and 7.8 percents for vessel detection and classification, respectively. This improvement shows that our method is more robust to noise, therefore has a better performance than the Hessian based method for the detection of vessels with weak and ambiguous boundaries.
Proc. SPIE. 9034, Medical Imaging 2014: Image Processing
KEYWORDS: Image processing algorithms and systems, Optical coherence tomography, Magnetic resonance imaging, Image segmentation, Image processing, 3D modeling, Medical imaging, Neuroimaging, 3D image processing, Brain
Segmentation of 3D medical structures in real-time is an important as well as intractable problem for clinical applications due to the high computation and memory cost. We propose a novel fast evolving active contour model in this paper to reduce the requirements of computation and memory. The basic idea is to evolve the brief represented dynamic contour interface as far as possible per iteration. Our method encodes zero level set via a single unordered list, and evolves the list recursively by adding activated adjacent neighbors to its end, resulting in active parts of the zero level set moves far enough per iteration along with list scanning. To guarantee the robustness of this process, a new approximation of curvature for integer valued level set is proposed as the internal force to penalize the list smoothness and restrain the list continual growth. Besides, list scanning times are also used as an upper hard constraint to control the list growing. Together with the internal force, efficient regional and constrained external forces, whose computations are only performed along the unordered list, are also provided to attract the list toward object boundaries. Specially, our model calculates regional force only in a narrowband outside the zero level set and can efficiently segment multiple regions simultaneously as well as handle the background with multiple components. Compared with state-of-the-art algorithms, our algorithm is one-order of magnitude faster with similar segmentation accuracy and can achieve real-time performance for the segmentation of 3D medical structures on a standard PC.
Automatic segmentation of anatomical structure is crucial for computer aided diagnosis and image guided online
treatment. In this paper, we present a novel approach for fully automatic segmentation of all anatomical structures from a
target liver organ in a coherent framework. Firstly, all regional anatomical structures such as vessel, tumor, diaphragm
and liver parenchyma are detected simultaneously using random forest classifiers. They share the same feature set and
classification procedure. Secondly, an efficient region segmentation algorithm is used to obtain the precise shape of these
regional structures. It is based on level set with proposed active set evolution and multiple features handling which
achieves 10 times speedup over existing algorithms. Thirdly, the liver boundary curve is extracted via a graph-based
model. The segmentation results of regional structures are incorporated into the graph as constraints to improve the
robustness and accuracy. Experiment is carried out on an ultrasound image dataset with 942 images captured with liver
motion and deformation from a number of different views. Quantitative results demonstrate the efficiency and
effectiveness of the proposed algorithm.
A novel noise reduction algorithm is proposed for reducing the noise and enhancing the contrast in 3D Optical
Coherence Tomography (OCT) images. First, the OCT image is divided into two subregions based on the local noise
property: the background area in which the additive noise is dominant and the foreground area in which the
multiplicative noise is dominant. In the background, the noise is eliminated by the 2D linear filtering combined with the
frame averaging. In the foreground, the noise is eliminated by the 3D linear filtering-an extension of the 2D linear
filtering. Therefore, the denoised image is reconstructed according to the combination of the denoised background and
foreground. The above procedure can be formulated with a bi-linear model which can be solved efficiently. The
proposed bi-linear model can dramatically improve image quality in 3D images with heavy noise and the corresponding
linear filter kernel in 2D can be performed in real time.
The filter kernel we used is introduced based on the linear noise model in OCT system. The noise model used in the filter
kernel includes both the multiplicative (speckle) noise and the additive (incoherent) noise, where the latter is not
considered in the most existing linear speckle filters and wavelet filters. Also, the filter kernel can be treated as a low
pass filter and can be applied to frequency extraction. Therefore an image contrast enhancement method is introduced in
the frequency domain based on the frequency decomposing and weighted combination. A set of experiments are carried
out to verify the effectiveness and efficiency of the proposed algorithm.