Method for bias field correction of brain T1-weighted magnetic resonance images minimizing segmentation error

This work presents a new algorithm (nonuniform intensity correction; NIC) for correction of intensity inhomogeneities in T1-weighted magnetic resonance (MR) images. The bias field and a bias-free image are obtained through an iterative process that uses brain tissue segmentation. The algorithm was validated by means of realistic phantom images and a set of 24 real images. The first evaluation phase was based on a public domain phantom dataset, used previously to assess bias field correction algorithms. NIC performed similar to previously described methods in removing the bias field from phantom images, without introduction of degradation in the absence of intensity inhomogeneity. The real image dataset was used to compare the performance of this new algorithm to that of other widely used methods (N3, SPM'99, and SPM2). This dataset included both low and high bias field images from two different MR scanners of low (0.5 T) and medium (1.5 T) static fields. Using standard quality criteria for determining the goodness of the different methods, NIC achieved the best results, correcting the images of the real MR dataset, enabling its systematic use in images from both low and medium static field MR scanners. A limitation of our method is that it might fail if the bias field is so high that the initial histogram does not show bimodal distribution for white and gray matterPublicad

A Review on MR Image Intensity Inhomogeneity Correction

Intensity inhomogeneity (IIH) is often encountered in MR imaging, and a number of techniques have been devised to correct this artifact. This paper attempts to review some of the recent developments in the mathematical modeling of IIH field. Low-frequency models are widely used, but they tend to corrupt the low-frequency components of the tissue. Hypersurface models and statistical models can be adaptive to the image and generally more stable, but they are also generally more complex and consume more computer memory and CPU time. They are often formulated together with image segmentation within one framework and the overall performance is highly dependent on the segmentation process. Beside these three popular models, this paper also summarizes other techniques based on different principles. In addition, the issue of quantitative evaluation and comparative study are discussed

Texture analysis of cardiac cine magnetic resonance imaging to detect nonviable segments in patients with chronic myocardial infarction

[EN] Purpose: To investigate the ability of texture analysis to differentiate between infarcted nonviable, viable, and remote segments on cardiac cine magnetic resonance imaging (MRI). Methods: This retrospective study included 50 patients suffering chronic myocardial infarction. The data were randomly split into training (30 patients) and testing (20 patients) sets. The left ventricular myocardium was segmented according to the 17-segment model in both cine and late gadolinium enhancement (LGE) MRI. Infarcted myocardium regions were identified on LGE in short-axis views. Nonviable segments were identified as those showing LGE 50%, and viable segments those showing 0 < LGE < 50% transmural extension. Features derived from five texture analysis methods were extracted from the segments on cine images. A support vector machine (SVM) classifier was trained with different combination of texture features to obtain a model that provided optimal classification performance. Results: The best classification on testing set was achieved with local binary patterns features using a 2D + t approach, in which the features are computed by including information of the time dimension available in cine sequences. The best overall area under the receiver operating characteristic curve (AUC) were: 0.849, sensitivity of 92% to detect nonviable segments, 72% to detect viable segments, and 85% to detect remote segments. Conclusion: Nonviable segments can be detected on cine MRI using texture analysis and this may be used as hypothesis for future research aiming to detect the infarcted myocardium by means of a gadolinium-free approach.This work was supported in part by the Spanish Ministerio de Economia y Competitividad (MINECO) and FEDER funds under grant BFU2015-64380-C2-2-R, by Instituto de Salud Carlos III and FEDER funds under grants FIS PI14/00271 and PIE15/00013 and by the Generalitat Valenciana under grant PROMETEO/2013/007. The first author, Andres Larroza, was supported by grant FPU12/01140 from the Spanish Ministerio de Educacion, Cultura y Deporte (MECD).Larroza, A.; López-Lereu, M.; Monmeneu, J.; Gavara-Doñate, J.; Chorro, F.; Bodi, V.; Moratal, D. (2018). Texture analysis of cardiac cine magnetic resonance imaging to detect nonviable segments in patients with chronic myocardial infarction. Medical Physics. 45(4):1471-1480. https://doi.org/10.1002/mp.12783S14711480454Castellano, G., Bonilha, L., Li, L. M., & Cendes, F. (2004). Texture analysis of medical images. Clinical Radiology, 59(12), 1061-1069. doi:10.1016/j.crad.2004.07.008Hodgdon, T., McInnes, M. D. F., Schieda, N., Flood, T. A., Lamb, L., & Thornhill, R. E. (2015). Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images? Radiology, 276(3), 787-796. doi:10.1148/radiol.2015142215Larroza, A., Moratal, D., Paredes-Sánchez, A., Soria-Olivas, E., Chust, M. L., Arribas, L. A., & Arana, E. (2015). Support vector machine classification of brain metastasis and radiation necrosis based on texture analysis in MRI. Journal of Magnetic Resonance Imaging, 42(5), 1362-1368. doi:10.1002/jmri.24913Thevenot, J., Hirvasniemi, J., Pulkkinen, P., Määttä, M., Korpelainen, R., Saarakkala, S., & Jämsä, T. (2014). Assessment of Risk of Femoral Neck Fracture with Radiographic Texture Parameters: A Retrospective Study. Radiology, 272(1), 184-191. doi:10.1148/radiol.14131390Kassner, A., & Thornhill, R. E. (2010). Texture Analysis: A Review of Neurologic MR Imaging Applications. American Journal of Neuroradiology, 31(5), 809-816. doi:10.3174/ajnr.a2061Pfeiffer, M. P., & Biederman, R. W. W. (2015). Cardiac MRI. Medical Clinics of North America, 99(4), 849-861. doi:10.1016/j.mcna.2015.02.011Flett, A. S., Hasleton, J., Cook, C., Hausenloy, D., Quarta, G., Ariti, C., … Moon, J. C. (2011). Evaluation of Techniques for the Quantification of Myocardial Scar of Differing Etiology Using Cardiac Magnetic Resonance. JACC: Cardiovascular Imaging, 4(2), 150-156. doi:10.1016/j.jcmg.2010.11.015Engan K Eftestøl T Ørn S Kvaloy JT Woie L Exploratory data analysis of image texture and statistical features on myocardium and infarction areas in cardiac magnetic resonance images 2010Kotu LP Engan K Eftestøl T Ørn S Woie L Segmentation of scarred and non-scarred myocardium in LG enhanced CMR images using intensity-based textural analysis 2011Kotu, L., Engan, K., Skretting, K., Måløy, F., Ørn, S., Woie, L., & Eftestøl, T. (2013). Probability mapping of scarred myocardium using texture and intensity features in CMR images. BioMedical Engineering OnLine, 12(1), 91. doi:10.1186/1475-925x-12-91Schofield, R., Ganeshan, B., Kozor, R., Nasis, A., Endozo, R., Groves, A., … Moon, J. C. (2016). CMR myocardial texture analysis tracks different etiologies of left ventricular hypertrophy. 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A Modified Brain MR Image Segmentation and Bias Field Estimation Model Based on Local and Global Information

Because of the poor radio frequency coil uniformity and gradient-driven eddy currents, there is much noise and intensity inhomogeneity (bias) in brain magnetic resonance (MR) image, and it severely affects the segmentation accuracy. Better segmentation results are difficult to achieve by traditional methods; therefore, in this paper, a modified brain MR image segmentation and bias field estimation model based on local and global information is proposed. We first construct local constraints including image neighborhood information in Gaussian kernel mapping space, and then the complete regularization is established by introducing nonlocal spatial information of MR image. The weighting between local and global information is automatically adjusted according to image local information. At the same time, bias field information is coupled with the model, and it makes the model reduce noise interference but also can effectively estimate the bias field information. Experimental results demonstrate that the proposed algorithm has strong robustness to noise and bias field is well corrected

Illumination Correction on Biomedical Images

RF-Inhomogeneity Correction (aka bias) artifact is an important research field in Magnetic Resonance Imaging (MRI). Bias corrupts MR images altering their illumination even though they are acquired with the most recent scanners. Homomorphic Unsharp Masking (HUM) is a filtering technique aimed at correcting illumination inhomogeneity, but it produces a halo around the edges as a side effect. In this paper a novel correction scheme based on HUM is proposed to correct the artifact mentioned above without introducing the halo. A wide experimentation has been performed on MR images. The method has been tuned and evaluated using the simulated Brainweb image database. In this framework, the approach has been compared successfully against the Guillemaud filter and the SPM2 method. Moreover, the method has been successfully applied on several real MR images of the brain (0.18 T, 1.5 T and 7 T). The description of the overall technique is reported along with the experimental results that show its effectiveness in different anatomical regions and its ability to compensate both underexposed and overexposed areas. Our approach is also effective on non-radiological images, like retinal ones

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Retinal blood vessels extraction using probabilistic modelling

© 2014 Kaba et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.This article has been made available through the Brunel Open Access Publishing Fund.The analysis of retinal blood vessels plays an important role in detecting and treating retinal diseases. In this review, we present an automated method to segment blood vessels of fundus retinal image. The proposed method could be used to support a non-intrusive diagnosis in modern ophthalmology for early detection of retinal diseases, treatment evaluation or clinical study. This study combines the bias correction and an adaptive histogram equalisation to enhance the appearance of the blood vessels. Then the blood vessels are extracted using probabilistic modelling that is optimised by the expectation maximisation algorithm. The method is evaluated on fundus retinal images of STARE and DRIVE datasets. The experimental results are compared with some recently published methods of retinal blood vessels segmentation. The experimental results show that our method achieved the best overall performance and it is comparable to the performance of human experts.The Department of Information Systems, Computing and Mathematics, Brunel University

Methodological aspects for improving clinical value of SPECT and MRI

Image processing methods were developed for SPECT and MR images. The methods were validated in clinical environment. Segmentation of SPECT images for region of interest (ROI) analysis was found to be unreliable without accurate attenuation and scatter correction for the original images. The reliability of ROI analysis of brain SPECT images was enhanced using registration with MRI. The method was based on external markers. The registration error was studied using phantom tests and simulations. It was concluded that the registration accuracy was not the limiting factor in ROI analysis of the registered images provided that the external marker system was properly designed and attached. Quality requirements for MRI data from patients with cerebral infarctions were evaluated in order to make segmentation as automatic as possible. Quantitative information from these images could be extracted with e.g. statistical and neural network classifiers, but required more manual work than expected due to the visible intensity nonuniformity in the images. The third application consisted of developing a registration methodology for ictal and interictal SPECT, MRI and EEG for improved localization of the epileptogenic foci. The methodology was based on SPECT transmission imaging. The accuracy of registration was about 3-5 mm. As a conclusion, improved analysis of SPECT and MR images was obtained with the carefully evaluated methodology presented in the thesis. The registration procedure for brain SPECT and MRI as well as the registration procedure for epilepsy surgery candidates are in clinical use for selected patients in Helsinki University Central Hospital (currently Health Care Region of Helsinki and Uusimaa).reviewe