From MIP image to MRA segmentation using fuzzy set theory

Inverse Fourier transformation of combined first order derivative and intensity-curvature functional of magnetic resonance angiography of the human brain

BACKGROUND AND OBJECTIVE

This paper reports a novel image processing technique based on inverse Fourier transformation and its validation procedure.

METHODS

Magnetic Resonance Angiography (MRA) data of the human brain is fitted on a pixel-by-pixel basis with bivariate linear model polynomial function. Polynomial fitting allows the formulation of two measures: the first order derivative (FOD), which is an edge finder, and the intensity-curvature functional (ICF), which is a high pass filter. The calculation of FOD and ICF uses knowledge provided by existing research and is performed through resampling. ICF and FOD are direct Fourier transformed, and their k-space is combined through a nonlinear convolution of terms. The resulting k-space is inverse Fourier transformed so to obtain a novel image called Fourier Convolution Image (FCI).

RESULTS

FCI possesses the characteristics of an edge finder (FOD) and a high pass filter (ICF).

CONCLUSIONS

FC images yield the following properties versus MRA: 1. Change of the contrast; 2. Increased sharpness in the proximity of human brain vessels; 3. Increased visualization of vessel connectivity. The implication of this study is to provide FCI as another viable option for MRA evaluation.

Algorithms for segmenting cerebral time-of-flight magnetic resonance angiograms from volunteers and anemic patients

Purpose: To evaluate six cerebral arterial segmentation algorithms in a set of patients with a wide range of hemodynamic characteristics to determine real-world performance. Approach: Time-of-flight magnetic resonance angiograms were acquired from 33 subjects: normal controls ( N = 11 ), sickle cell disease ( N = 11 ), and non-sickle anemia ( N = 11 ) using a 3 Tesla Philips Achieva scanner. Six segmentation algorithms were tested: (1) Otsu's method, (2) K-means, (3) region growing, (4) active contours, (5) minimum cost path, and (6) U-net machine learning. Segmentation algorithms were tested with two region-selection methods: global, which selects the entire volume; and local, which iteratively tracks the arteries. Five slices were manually segmented from each patient by two readers. Agreement between manual and automatic segmentation was measured using Matthew's correlation coefficient (MCC). Results: Median algorithm segmentation times ranged from 0.1 to 172.9 s for a single angiogram versus 10 h for manual segmentation. Algorithms had inferior performance to inter-observer vessel-based ( p < 0.0001 , MCC = 0.65 ) and voxel-based ( p < 0.0001 , MCC = 0.73 ) measurements. There were significant differences between algorithms ( p < 0.0001 ) and between patients ( p < 0.0042 ). Post-hoc analyses indicated (1) local minimum cost path performed best with vessel-based ( p = 0.0261 , MCC = 0.50 ) and voxel-based ( p = 0.0131 , MCC = 0.66 ) analyses; and (2) higher vessel-based performance in non-sickle anemia ( p = 0.0002 ) and lower voxel-based performance in sickle cell ( p = 0.0422 ) compared with normal controls. All reported MCCs are medians. Conclusions: The best-performing algorithm (local minimum cost path, voxel-based) had 9.59% worse performance than inter-observer agreement but was 3 orders of magnitude faster. Automatic segmentation was non-inferior in patients with sickle cell disease and superior in non-sickle anemia.

AN IMPROVED RANDOM WALK SEGMENTATION ON THE LUNG NODULES

In this paper, we proposed a semi-automatic technique with a marker indicating the target to locate and segment nodules. For the lung nodule detection, we develop a Gabor texture feature by FCM (Fuzzy C Means) segmentation. Given a marker indicating a rough location of the nodules, a decision process is followed by applying an ellipse fitting algorithm. From the ellipse mask, the foreground and background seeds for the random walk segmentation can be automatically obtained. Finally, the edge of the nodules is obtained by the random walk algorithm. The feasibility and effectiveness of the proposed method are evaluated with the various types of the nodules to identify the edges, so that it can be used to locate the nodule edge and its growth rate.

A new method for volume segmentation of PET images, based on possibility theory

18F-fluorodeoxyglucose positron emission tomography (18FDG PET) has become an essential technique in oncology. Accurate segmentation and uptake quantification are crucial in order to enable objective follow-up, the optimization of radiotherapy planning, and therapeutic evaluation. We have designed and evaluated a new, nearly automatic and operator-independent segmentation approach. This incorporated possibility theory, in order to take into account the uncertainty and inaccuracy inherent in the image. The approach remained independent of PET facilities since it did not require any preliminary calibration. Good results were obtained from phantom images [percent error =18.38% (mean) ± 9.72% (standard deviation)]. Results on simulated and anatomopathological data sets were quantified using different similarity measures and showed the method was efficient (simulated images: Dice index =82.18% ± 13.53% for SUV =2.5 ). The approach could, therefore, be an efficient and robust tool for uptake volume segmentation, and lead to new indicators for measuring volume of interest activity.

Dijkstra's algorithm applied to 3D skeletonization of the brain vascular tree: evaluation and application to symbolic

This paper describes the methodology and the evaluation of a 3D skeletonization algorithm applied on brain vascular structure. This method is based on the application of the minimum cost-spanning tree using Dijkstra's algorithm and seems well appropriate to tubular objects. We briefly describe the different steps, from the segmentation to the skeleton analysis. Besides, we propose an original evaluation scheme of the method based on digital phantom and clinical data. The final aim of this work is to provide a symbolic description framework applied to cerebro-vascular structures.

A new method based on both fuzzy set and possibility theories for tumor volume segmentation on PET images

A new automatic method for tumor volume segmentation on PET images has been developed. The method introduced in this paper is based on previous works in MRA segmentation and involves both fuzzy set and possibility theories. Visual results prove the method efficiency which is confirmed by obtained Jaccard index.