Multiphase Image Research Articles

Advances of multidetector computed tomography in the characterization and staging of renal cell carcinoma.

Renal cell carcinoma (RCC) accounts for approximately 90%-95% of kidney tumors. With the widespread use of cross-sectional imaging modalities, more than half of RCCs are detected incidentally, often diagnosed at an early stage. This may allow the planning of more conservative treatment strategies. Computed tomography (CT) is considered the examination of choice for the detection and staging of RCC. Multidetector CT (MDCT) with the improvement of spatial resolution and the ability to obtain multiphase imaging, multiplanar and three-dimensional reconstructions in any desired plane brought about further improvement in the evaluation of RCC. Differentiation of RCC from benign renal tumors based on MDCT features is improved. Tumor enhancement characteristics on MDCT have been found closely to correlate with the histologic subtype of RCC, the nuclear grade and the cytogenetic characteristics of clear cell RCC. Important information, including tumor size, localization, and organ involvement, presence and extent of venous thrombus, possible invasion of adjacent organs or lymph nodes, and presence of distant metastases are provided by MDCT examination. The preoperative evaluation of patients with RCC was improved by depicting the presence or absence of renal pseudocapsule and by assessing the possible neoplastic infiltration of the perirenal fat tissue and/or renal sinus fat compartment.

A multiphase image segmentation method based on structure- texture dictionary learning and total variation projection regularization

This paper proposes a novel multiphase variational model for image segmentation based on structuretexture dictionary learning. The proposed model uses total variation projection regularization to preserve the geometric shapes of the segmented regions. According to the geometric significance of the regularization parameters of total variation projection, an adaptive parameter selection method is presented. The proposed method can simultaneously achieve image segmentation, image decomposition, and image denoising. A series of experimental results demonstrate the effectiveness of the proposed method.

Multiphase contrast-enhanced magnetic resonance imaging features of Bacillus Calmette-Guérin-induced granulomatous prostatitis in five patients.

ObjectiveTo evaluate the multiphase contrast-enhanced magnetic resonance (MR) imaging features of Bacillus Calmette-Guérin (BCG)-induced granulomatous prostatitis (GP).Materials and MethodsMagnetic resonance images obtained from five patients with histopathologically proven BCG-induced GP were retrospectively analyzed for tumor location, size, signal intensity on T2-weighted images (T2WI) and diffusion-weighted images (DWI), apparent diffusion coefficient (ADC) value, and appearance on gadolinium-enhanced multiphase images. MR imaging findings were compared with histopathological findings.ResultsBacillus Calmette-Guérin-induced GP (size range, 9-40 mm; mean, 21.2 mm) were identified in the peripheral zone in all patients. The T2WI showed lower signal intensity compared with the normal peripheral zone. The DWIs demonstrated high signal intensity and low ADC values (range, 0.44-0.68 × 10-3 mm2/sec; mean, 0.56 × 10-3 mm2/sec), which corresponded to GP. Gadolinium-enhanced multiphase MR imaging performed in five patients showed early and prolonged ring enhancement in all cases of GP. Granulomatous tissues with central caseation necrosis were identified histologically, which corresponded to ring enhancement and a central low intensity area on gadolinium-enhanced MR imaging. The findings on T2WI, DWI, and gadolinium-enhanced images became gradually obscured with time.ConclusionBacillus Calmette-Guérin-induced GP demonstrates early and prolonged ring enhancement on gadolinium-enhanced MR imaging which might be a key finding to differentiate it from prostate cancer.

Retroperitoneal low-flow vascular malformations: characteristic MRI findings correlated with histopathological findings.

To evaluate the MRI findings of retroperitoneal low-flow vascular malformations (RLVMs) correlated with histopathological findings. Two radiologists reviewed the MRI findings of 4 RLVMs (3 with capillary malformations and 1 with a venous malformation). First, they evaluated the visibility, signal intensities, and signal homogeneity of each lesion on non-fat-suppressed breath-hold T2-weighted single-shot fast spin-echo (non-FS SSFSE) images and fat-suppressed T2-weighted fast spin-echo (FS T2-weighted FSE) images. Second, the kinetic patterns and the internal enhancement patterns were analyzed for each lesion on multi-phasic contrast-enhanced (CE) images. After these image analyses, the MRI findings were correlated with the histopathological findings. Histopathologically, the 4 RLVMs did not exhibit remarkable degeneration and were present in the retroperitoneal fat tissue without clear capsules. On the non-FS SSFSE images, 3 of the 4 RLVMs could not be discriminated from the surrounding retroperitoneal fat tissue (invisible), and the remaining lesion was barely visible with an indistinct margin. On the FS T2-weighted FSE images, however, all the RLVMs were clearly visualized as homogeneous high-signal intensities. On the multi-phasic CE images, all the capillary malformations exhibited fast enhancement, while a venous malformation showed slow enhancement. Furthermore, the RLVMs tended to exhibit a centripetal filling pattern. The RLVMs blended in with the surrounding retroperitoneal fat tissue on non-FS SSFSE images, like phantoms, whereas they were clearly visualized on FS T2-weighted FSE images. On multi-phasic CE images, the RLVMs tended to exhibit a centripetal filling pattern. These imaging features may be useful diagnostic clues for RLVMs.

Wavelet-based fuzzy multiphase image segmentation method

We present a novel wavelet-based fuzzy multiphase image segmentation model.By introducing PCA features data, the proposed model can segment texture images.We formulate a fast iterative shrinkage algorithm for multiphase image segmentation.Experimental results show the effectiveness of the proposed method. This letter proposes a novel fuzzy multiphase image segmentation model. In the model, we introduce wavelet based regularization on the membership functions which are used as indicators of different regions. By using principal component analysis (PCA) features data as descriptors, the proposed model can segment texture and natural images. To efficiently solve the model, we formulate a fast iterative shrinkage algorithm for multiphase image segmentation. Experimental results show that the proposed method achieves better segmentation results compared with some other classical variational segmentation methods.

Renal cell carcinoma subtype differentiation using single-phase corticomedullary contrast-enhanced CT

ObjectiveTo compare multiphase and single-phase corticomedullary contrast-enhanced computed tomographic (CT) imaging in the differentiation of renal cell carcinoma (RCC) subtype. Material and methodsPathology records were reviewed from January 2008 to March 2013. The final cohort consisted of 79 patients (57 men, 22 women; mean age: 64±13). Quantitative tumor percentage enhancement (TE), cortical enhancement, and tumor-to-cortex enhancement (TCI) indexes were calculated. ResultsSingle-phase evaluations showed significantly lower mean TE and TCI for papillary tumors when compared with clear cell and cromophobe tumors (P<.01). Comparison of receiver operating characteristic curve analyses did not show significant differences between both evaluation methods. ConclusionAccuracy of RCC subtype differentiation with single-phase corticomedullary contrast-enhanced CT is comparable to multiphasic imaging.

Free-breathing, zero-TE MR lung imaging.

The investigation of three-dimensional radial, zero-echo time (TE) imaging for high-resolution, free-breathing magnetic resonance (MR) lung imaging using prospective and retrospective motion correction. Zero-TE was implemented similarly to the rotating-ultra-fast-imaging-sequence, providing 3D, isotropic, radial imaging with proton density contrast. Respiratory motion was addressed using prospective triggering (PT), prospective gating (PG) and retrospective gating (RG) with physiological signals obtained from a respiratory belt and interleaved pencil beam and DC navigators. The methods were demonstrated on four healthy volunteers at 3T. 3D, radial zero-TE imaging with high imaging bandwidth and nominally zero echo-time enables efficient capture of short-lived signals from the lung parenchyma and the vessels. Compared to Cartesian encoding, unaccounted for free-breathing respiration resulted in only benign blurring artifacts confined to the origin of motion. Breath holding froze respiration but achieved only limited image resolution (~1.8 mm, 30 s). PT and PG obtained similar quality expiratory-phase images at 1.2 mm resolution in ~6 min scan time. RG allowed multi-phase imaging in ~15 min, derived from eight individually stored averages. Zero-TE appears to be an attractive pulse sequence for 3D isotropic lung imaging. Prospective and retrospective approaches provide high-quality, free-breathing MR lung imaging within reasonable scan time.

A new technique for noise reduction at coronary CT angiography with multi-phase data-averaging and non-rigid image registration.

To investigate the feasibility of a newly developed noise reduction technique at coronary CT angiography (CTA) that uses multi-phase data-averaging and non-rigid image registration. Sixty-five patients underwent coronary CTA with prospective ECG-triggering. The range of the phase window was set at 70-80% of the R-R interval. First, three sets of consecutive volume data at 70%, 75% and 80% of the R-R interval were prepared. Second, we applied non-rigid registration to align the 70% and 80% images to the 75% image. Finally, we performed weighted averaging of the three images and generated a de-noised image. The image noise and contrast-to-noise ratio (CNR) in the proximal coronary arteries between the conventional 75% and the de-noised images were compared. Two radiologists evaluated the image quality using a 5-point scale (1, poor; 5, excellent). On de-noised images, mean image noise was significantly lower than on conventional 75% images (18.3 HU ± 2.6 vs. 23.0 HU ± 3.3, P < 0.01) and the CNR was significantly higher (P < 0.01). The mean image quality score for conventional 75% and de-noised images was 3.9 and 4.4, respectively (P < 0.01). Our method reduces image noise and improves image quality at coronary CTA. • We introduce a new method for image noise reduction at cardiac CT. • Multiple data acquisitions of an object and their averaging yield lower noise. • Our method uses multi-phase images reconstructed from unused redundant imaging data. • It reduces image noise by averaging multi-phase images transformed by non-rigid registration. • This method achieves a 20% image noise reduction at cardiac CT.

A Novel Level Set Method for Remote Sensing Image

Due to the characteristic of remote sensing image, we propose a novel method based on K-means algorithm also with the improved multi-phrase level set model. Comparing with the classical multi-phase C-V model, the improved model considers the region area information, gradient information and edge detection .Proper use of gradient information can overcome the inaccurate edge localization defects in image segmentation. The edge detection is used for keeping the boundary information better in the evolution process .For the reason of picking up the contour’s convergent speed and enable the avoidance of trap .Four stages are constructed. Firstly, a median filtering is applied to smooth the original image and reduce parts of noise. Secondly, the usage of K-means algorithm gains more obvious differences of characteristics. Next, the reconstruction of gradient is obtained by using Sobel operator. Finally, segmentation result is achieved by using an improved method of multi-phase level set image segmentation. Experimental results show that the proposed approach has advantages in rapid and efficient application of remote sensing image segmentation.

Feline chronic kidney disease is associated with upregulation of transglutaminase 2: a collagen cross-linking enzyme.

Chronic kidney disease is a major cause of morbidity and mortality in cats. Transglutaminase 2 (TG2) is a calcium-dependent enzyme proposed to mediate tubulointerstitial fibrosis in the kidney by cross-linking collagen fibrils. Postmortem kidney tissue was obtained from primary renal azotemic (n = 10) and nonazotemic (n = 5) cats (14 domestic short hair, 1 Burmese; aged 9-23.7 years). Extracellular matrix protein deposition was determined by Masson's trichrome staining and collagen immunofluorescence. Total kidney transglutaminase (TG) enzyme activity and TG2 protein were measured in tissue homogenates by putrescine incorporation and Western blotting. Extracellular TG enzyme activity and TG2 protein were determined in situ by immunofluorescence, quantified by multiphase image analysis. Results were compared using the unpaired Student's t-test with Welch's correction. Elevated plasma creatinine, urea, and phosphate concentrations were associated with tubulointerstitial fibrosis but not glomerular fibrosis. Kidney homogenates from azotemic cats showed a 3-fold higher total TG enzyme activity and TG2 protein compared with kidneys from nonazotemic cats. Immunofluorescent studies performed in situ confirmed a 3-fold higher extracellular TG enzyme activity and TG2 protein in cats with azotemia. Tubulointerstitial TG2 showed a positive linear correlation with both renal function and tubulointerstitial fibrosis. In conclusion, for cats with azotemia, both filtration failure and tubulointerstitial fibrosis were associated with the upregulation of TG2, a collagen cross-linking enzyme and the major isoform of transglutaminase in the kidney. TG2 may provide a new therapeutic target for drugs designed to slow the progression of feline chronic kidney disease.

A novel level set method for image segmentation by incorporating local statistical analysis and global similarity measurement

This paper presents a novel level set method for complex image segmentation, where the local statistical analysis and global similarity measurement are both incorporated into the construction of energy functional. The intensity statistical analysis is performed on local circular regions centered in each pixel so that the local energy term is constructed in a piecewise constant way. Meanwhile, the Bhattacharyya coefficient is utilized to measure the similarity between probability distribution functions for intensities inside and outside the evolving contour. The global energy term can be formulated by minimizing the Bhattacharyya coefficient. To avoid the time-consuming re-initialization step, the penalty energy term associated with a new double-well potential is constructed to maintain the signed distance property of level set function. The experiments and comparisons with four popular models on synthetic and real images have demonstrated that our method is efficient and robust for segmenting noisy images, images with intensity inhomogeneity, texture images and multiphase images.

Split Bregman Method for Minimization of Fast Multiphase Image Segmentation Model for Inhomogeneous Images

In this paper, we present a fast multiphase image segmentation model in a variational level set formulation. The proposed model is mainly used for images with inhomogeneity. The newly defined energy functional combines the local intensity information, the global intensity information, and the edge information to deal with the inhomogeneity. We use a weight function varying with locations to control the force of the local and global information dynamically. The special structure of the new energy functional ensures that the split Bregman method can be used for fast minimization. We apply the split Bregman method to minimize the new energy functional and summarize important results in several theorems. Theoretical evidences for these results are given. Several numerical results are also presented.

Locating Liver Lesion with Local C-V Level Set and Image Registration

In this paper, we proposed comprehensive methods to locate the liver lesion in multi-phase CT images. It first construct a local liver lesion image from the image in which liver lesion differs from liver tissue most markedly, then pre-segment the lesion with OTSU method to get the initial contour, and evolve the active contour with local C-V level set method to get the final contour of lesion in the CT image. Finally locate the liver lesion in CT images of other phases by image registration. Experiments showed this method can extract liver tumor efficiently.A well-prepared abstract enables the reader to identify the basic content of a document quickly and accurately, to determine its relevance to their interests, and thus to decide whether to read the document in its entirety. DOI : http://dx.doi.org/10.11591/telkomnika.v12i6.5393 Full Text: PDF

A new continuous max-flow algorithm for multiphase image segmentation using super-level set functions

We propose a graph cut based global minimization method for image segmentation by representing the segmentation label function with a series of nested binary super-level set functions. This representation enables us to use K-1 binary functions to partition any images into K phases. Both continuous and discretized formulations will be treated. For the discrete model, we propose a new graph cut algorithm which is faster than the existing graph cut methods to obtain the exact global solution. In the continuous case, we further improve the segmentation accuracy using a number of techniques that are unique to the continuous segmentation models. With the convex relaxation and the dual method, the related continuous dual model is convex and we can mathematically show that the global minimization can be achieved. The corresponding continuous max-flow algorithms are easy and stable. Experimental results show that our model is very competitive to some existing methods.

Multi-Phase Texture Segmentation Using Gabor Features Histograms Based on Wasserstein Distance

AbstractWe present a multi-phase image segmentation method based on the histogram of the Gabor feature space, which consists of a set of Gabor-filter responses with various orientations, scales and frequencies. Our model replaces the error function term in the original fuzzy region competition model with squared 2-Wasserstein distance function, which is a metric to measure the distance of two histograms. The energy functional is minimized by alternative minimization method and the existence of closed-form solutions is guaranteed when the exponent of the fuzzy membership term being 1 or 2. We test our model on both simple synthetic texture images and complex natural images with two or more phases. Experimental results are shown and compared to other recent results.

Multiphase image segmentation via equally distanced multiple well potential

Variational models for image segmentation, e.g. Mumford–Shah variational model [47] and Chan–Vese model [21,59], generally involve a regularization term that penalizes the length of the boundaries of the segmentation. In practice often the length term is replaced by a weighted length, i.e., some portions of the set of boundaries are penalized more than other portions, thus unbalancing the geometric term of the segmentation functional.In the present paper we consider a class of variational models in the framework of Γ-convergence theory. We propose a family of functionals defined on vector valued functions that involve a multiple well potential of the type arising in diffuse-interface models of phase transitions. A potential with equally distanced wells makes it possible to retrieve the penalization of the true (i.e., not weighted) length of the boundaries as the Γ-convergence parameter tends to zero. We explore the differences and the similarities of behavior of models in the proposed class, followed by some numerical experiments.

An iterative fuzzy region competition algorithm for multiphase image segmentation

This paper presents a new multiphase image segmentation model based on Fuzzy Region Competition. The proposed model approximates image regions by probability density functions and uses a supervised approach in the segmentation process. The strategy of the proposed model is to perform a two-phase Fuzzy Region Competition model iteratively for image segmentation. The hard partition is obtained in each step from a determined fuzzy membership functions consequently, the segmentation process is soft, while the final result is hard, due to the simplicity of avoiding non-overlapping and vacuum regions. Finally, several experiments on multiphase images are presented to demonstrate the efficiency and robustness of the proposed model when dealing with noisy, texturized and natural images.

Image processing of multiphase images obtained via X‐ray microtomography: A review

AbstractEasier access to X‐ray microtomography (μCT) facilities has provided much new insight from high‐resolution imaging for various problems in porous media research. Pore space analysis with respect to functional properties usually requires segmentation of the intensity data into different classes. Image segmentation is a nontrivial problem that may have a profound impact on all subsequent image analyses. This review deals with two issues that are neglected in most of the recent studies on image segmentation: (i) focus on multiclass segmentation and (ii) detailed descriptions as to why a specific method may fail together with strategies for preventing the failure by applying suitable image enhancement prior to segmentation. In this way, the presented algorithms become very robust and are less prone to operator bias. Three different test images are examined: a synthetic image with ground‐truth information, a synchrotron image of precision beads with three different fluids residing in the pore space, and a μCT image of a soil sample containing macropores, rocks, organic matter, and the soil matrix. Image blur is identified as the major cause for poor segmentation results. Other impairments of the raw data like noise, ring artifacts, and intensity variation can be removed with current image enhancement methods. Bayesian Markov random field segmentation, watershed segmentation, and converging active contours are well suited for multiclass segmentation, yet with different success to correct for partial volume effects and conserve small image features simultaneously.

A NUMERICAL METHOD FOR THE MODIFIED VECTOR-VALUED ALLEN-CAHN PHASE-FIELD MODEL AND ITS APPLICATION TO MULTIPHASE IMAGE SEGMENTATION

In this paper, we present an efficient numerical method for multiphase image segmentation using a multiphase-field model. The method combines the vector-valued Allen-Cahn phase-field equation with initial data fitting terms containing prescribed interface width and fidelity constants. An efficient numerical solution is achieved using the recently developed hybrid operator splitting method for the vector-valued Allen-Cahn phase-field equation. We split the modified vector-valued Allen-Cahn equation into a nonlinear equation and a linear diffusion equation with a source term. The linear diffusion equation is discretized using an implicit scheme and the resulting implicit discrete system of equations is solved by a multigrid method. The nonlinear equation is solved semi-analytically using a closed-form solution. And by treating the source term of the linear diffusion equation explicitly, we solve the modified vector-valued Allen-Cahn equation in a decoupled way. By decoupling the governing equation, we can speed up the segmentation process with multiple phases. We perform some characteristic numerical experiments for multiphase image segmentation.

A fast multiphase image segmentation model for gray images

In this paper we propose a fast multiphase image segmentation model for gray images. Our proposed model is based on the piecewise constant multiphase Vese–Chan model, the globally convex segmentation method and the split Bregman method. Our proposed model has the advantages of the Vese–Chan model and can deal with piecewise constant multiphase images well. Besides, our proposed model is much more efficient than the Vese–Chan model by applying the globally convex segmentation method and the split Bregman method. We have tested our proposed model with many synthetic and real images. Experimental results have demonstrated the efficiency and robustness of our proposed model.