Automated coronary artery tree segmentation in X-ray angiography using improved Hessian based enhancement and statistical region merging.

Visualization of cerebral blood vessels is vital for stroke diagnosis and surgical planning. A suitable modality for the visualization of blood vessels is very important for the analysis of abnormalities of the cerebrovascular system, as it is the most complex blood circulation system in the human body and vulnerable to bleeding, infection, blood clot, stenosis, and many other forms of damage. Images produced by current imaging modalities are not promising because of noise, artifacts, and the complex structure of cerebral blood vessels. Therefore, there is a requirement for the accurate reconstruction of blood vessels to assist the clinician in making an accurate diagnosis and surgical planning. This paper presents an overall review of modeling techniques that can be classified into the three categories, i.e., image-based modeling, mathematical modeling, and hybrid modeling. Image-based modeling deals directly with medical images and which involves preprocessing, segmentation, feature extraction, and classification. Mathematical modeling exploits existing mathematical laws and equations, an example being an arterial bifurcation, which is assumed to follow a fractal and cube law, and a system of ordinary differential equations are solved to obtain pressure and velocity estimates in a branching network. Whereas, Hybrid modeling incorporates both image-based and mathematical modeling to attempt to produce a more detailed and realistic arterial structure. From the literature review and the analysis of the results, it can be summarized that hybrid models provide a faster and more robust technique, which can significantly help in diagnosis and surgical planning, such as for finding the shortest path for a stenting procedure.

A Review on Segmentation and Modeling of Cerebral Vasculature for Surgical Planning

The status of medical physics in radiotherapy in China.

Vessel enhancement in magnetic resonance angiography (MRA) is an important preprocessing step for stroke surgical planning and further processing. Bilateral filter has been widely used to reduce noise due to its ability for smoothing an image and preserve the edges. It suffers a drawback of over smoothing that leads to discontinuity of blood vessels when applied to MRA image. Hessian-based filter is known to have the ability of enhancing the vessels and preserving the geometrical structure. This paper presents the vessel enhancement technique which combines bilateral and Hessian-based filters to exploit the advantages of them. The bilateral filtered images show that weighted bilateral filter can reduce more noise when comparing the peak signal-to-noise ratio (PSNR) value. Then, Hessian-based filter is performed on several types of preprocessed images to compare the performance of this technique on different types of images. Our method shows a promising result in suppressing the noise that is enhanced by Hessian-based filter.

Cerebral vessel enhancement using bilateral and Hessian-based filter

Image enhancement is very important stages used in image processing. A normal image enhancement process is using the filtering technique. Filtering helps the problems of the image display and can improvise the quality of the image. The problems that always happened in the image is illumination, noise and under-light images. In addition, these problems also caused a few troubles for image recognition for the daily life of certain people for their work. The objective of this study is to explore and compare a few starts of art filtering techniques based on the mathematical algorithm of the filters and then identifying the best method of the filters. There were a few methods that were selected in this project such as a high pass filter, low pass filter, high boost filter and others. All the selected filter experimented on the medical images and document images. The resulting images were evaluated using the Image Quality Assessments (IQA) which is a global contrast factor (GCF) and signal to noise ratio (SNR). Based on the numerical result, homomorphic low pas filter (HLF) provides a better performance among the other filters in terms of GCF (2.066) and SNR (8.907) value of the selected images.

Effect of Different Filtering Techniques on Medical and Document Image

Vessel enhancement in angiogram is an important preprocessing procedure for clinical diagnosis and further processing. Hessian-based methods have been widely used due to its elegant geometrical interpretations. But classical Hessian-based methods suffer the drawbacks of blurred vessel edges and intensity inhomogeneity, especially in vessel junction regions. In this paper, a new vessel enhancement method is proposed to address these problems. The input angiogram is first decomposed into several directional images and each of them is enhanced by traditional Hessian-based method. Then the enhanced direction images are recombined to generate final result. To improve contrast at vessel edges and intensity homogeneity in vessels, a vessel similarity measurement is proposed and used as weight coefficients in the recombination of enhanced directional images. It is calculated based on the intensity and curvature analysis of directional images. Qualitative and quantitative experiments performed on typical angiograms and retinal images in DRIVE database show that the proposed method has better performance than other two Hessian-based enhancement methods.

Hessian-based vessel enhancement combined with directional filter banks and vessel similarity

With robot-controlled linac positioning, robotic radiotherapy systems such as CyberKnife significantly increase freedom of radiation beam placement, but also impose more challenges on treatment plan optimization. The resampling mechanism in the vendor-supplied treatment planning system (MultiPlan) cannot fully explore the increased beam direction search space. Besides, a sparse treatment plan (using fewer beams) is desired to improve treatment efficiency. This study proposes a singular value decomposition linear programming (SVDLP) optimization technique for circular collimator based robotic radiotherapy. The SVDLP approach initializes the input beams by simulating the process of covering the entire target volume with equivalent beam tapers. The requirements on dosimetry distribution are modeled as hard and soft constraints, and the sparsity of the treatment plan is achieved by compressive sensing. The proposed linear programming (LP) model optimizes beam weights by minimizing the deviation of soft constraints subject to hard constraints, with a constraint on the l 1 norm of the beam weight. A singular value decomposition (SVD) based acceleration technique was developed for the LP model. Based on the degeneracy of the influence matrix, the model is first compressed into lower dimension for optimization, and then back-projected to reconstruct the beam weight. After beam weight optimization, the number of beams is reduced by removing the beams with low weight, and optimizing the weights of the remaining beams using the same model. This beam reduction technique is further validated by a mixed integer programming (MIP) model. The SVDLP approach was tested on a lung case. The results demonstrate that the SVD acceleration technique speeds up the optimization by a factor of 4.8. Furthermore, the beam reduction achieves a similar plan quality to the globally optimal plan obtained by the MIP model, but is one to two orders of magnitude faster. Furthermore, the SVDLP approach is tested and compared with MultiPlan on three clinical cases of varying complexities. In general, the plans generated by the SVDLP achieve steeper dose gradient, better conformity and less damage to normal tissues. In conclusion, the SVDLP approach effectively improves the quality of treatment plan due to the use of the complete beam search space. This challenging optimization problem with the complete beam search space is effectively handled by the proposed SVD acceleration.

https://iopscience.iop.org/article/10.1088/1361-6560/aa9b47/pdf

X Xu

Papers

Hessian-based vessel enhancement combined with directional filter banks and vessel similarity

A singular value decomposition linear programming (SVDLP) optimization technique for circular cone based robotic radiotherapy