Standardized evaluation methodology and reference database for evaluating IVUS image segmentation

This paper describes an evaluation framework that allows a standardized and quantitative comparison of IVUS lumen and media segmentation algorithms. This framework has been introduced at the MICCAI 2011 Computing and Visualization for (Intra)Vascular Imaging (CVII) workshop, comparing the results of eight teams that participated.We describe the available data-base comprising of multi-center, multi-vendor and multi-frequency IVUS datasets, their acquisition, the creation of the reference standard and the evaluation measures. The approaches address segmentation of the lumen, the media, or both borders; semi- or fully-automatic operation; and 2-D vs. 3-D methodology. Three performance measures for quantitative analysis have been proposed. The results of the evaluation indicate that segmentation of the vessel lumen and media is possible with an accuracy that is comparable to manual annotation when semi-automatic methods are used, as well as encouraging results can be obtained also in case of fully-automatic segmentation. The analysis performed in this paper also highlights the challenges in IVUS segmentation that remains to be solved.     

A validation framework for brain tumor segmentation

RATIONALE AND OBJECTIVES: We introduce a validation framework for the segmentation of brain tumors from magnetic resonance (MR) images. A novel unsupervised semiautomatic brain tumor segmentation algorithm is also presented. MATERIALS AND METHODS: The proposed framework consists of 1) T1-weighted MR images of patients with brain tumors, 2) segmentation of brain tumors performed by four independent experts, 3) segmentation of brain tumors generated by a semiautomatic algorithm, and 4) a software tool that estimates the performance of segmentation algorithms. RESULTS: We demonstrate the validation of the novel segmentation algorithm within the proposed framework. We show its performance and compare it with existent segmentation. The image datasets and software are available at http://www.brain-tumor-repository.org/. CONCLUSIONS: We present an Internet resource that provides access to MR brain tumor image data and segmentation that can be openly used by the research community. Its purpose is to encourage the development and evaluation of segmentation methods by providing raw test and image data, human expert segmentation results, and methods for comparing segmentation results.     

An electrostatic deformable model for medical image segmentation.

A new deformable model, the charged fluid model (CFM), that uses the simulation of charged elements was used to segment medical images. Poisson's equation was used to guide the evolution of the CFM in two steps. In the first step, the elements of the charged fluid were distributed along the propagating interface until electrostatic equilibrium was achieved. In the second step, the propagating front of the charged fluid was deformed in response to the image gradient. The CFM provided sub-pixel precision, required only one parameter setting, and required no prior knowledge of the anatomy of the segmented object. The characteristics of the CFM were compared to existing deformable models using CT and MR images. The results indicate that the CFM is a promising approach for the segmentation of anatomic structures in a wide variety of medical images across different modalities.     

A fast snake model based on non-linear diffusion for medical image segmentation.

In this paper, the traditional snake model and gradient vector flow (GVF) snake model are studied, which are believed to be quite slow due to the need to compute inverse matrix. Actually, the GVF in the latter snake model is formed by a biased linear diffusion procedure, and there would be oscillations around the edge of the object. Based on GVF generated through non-linear diffusion, we present a fast GVF (FGVF) snake model which is much faster than the traditional snake model and GVF snake model, and would cause no degradation of stability and flexibility, meanwhile, it could reduce the oscillations around the edges. The segmentation results using FGVF and error analysis on simulated images are presented. Finally, the demonstration of FGVF applied to Computed Tomography and Magnetic Resonance images are shown, the segmentation results are satisfactory visually with much less computation time in comparison with former snakes.     

Applications of hierarchical image segmentation techniques: aorta segmentation.

The segmentation of objects from complex images is difficult due to indistinct boundaries between objects and similarity of objects. We have used a hierarchical segmentation approach to accurately distinguish between objects and identify the corresponding boundaries. This approach has been used successfully to extract the aorta from transverse magnetic resonance (MR) images of the abdomen. The procedure to segment the abdominal aorta involves three progressive steps: aorta detection, aorta extraction, and estimation of the aorta wall boundary. Comparison of hierarchical segmentation techniques with single-step segmentation methods (e.g., region-growing, edge-detection) shows that hierarchical segmentation yields more reliable results.     

Fuzzy c-means clustering with spatial information for image segmentation.

A conventional FCM algorithm does not fully utilize the spatial information in the image. In this paper, we present a fuzzy c-means (FCM) algorithm that incorporates spatial information into the membership function for clustering. The spatial function is the summation of the membership function in the neighborhood of each pixel under consideration. The advantages of the new method are the following: (1) it yields regions more homogeneous than those of other methods, (2) it reduces the spurious blobs, (3) it removes noisy spots, and (4) it is less sensitive to noise than other techniques. This technique is a powerful method for noisy image segmentation and works for both single and multiple-feature data with spatial information.     

A framework for evaluating the cost-effectiveness of MRI screening for breast cancer

European Radiology -     

A unified set of analysis tools for uterine cervix image segmentation

Segmentation is a fundamental component of many medical image-processing applications, and it has long been recognized as a challenging problem. In this paper, we report our research and development efforts on analyzing and extracting clinically meaningful regions from uterine cervix images in a large database created for the study of cervical cancer. In addition to proposing new algorithms, we also focus on developing open source tools which are in synchrony with the research objectives. These efforts have resulted in three Web-accessible tools which address three important and interrelated sub-topics in medical image segmentation, respectively: the Boundary Marking Tool (BMT), Cervigram Segmentation Tool (CST), and Multi-Observer Segmentation Evaluation System (MOSES). The BMT is for manual segmentation, typically to collect “ground truth” image regions from medical experts. The CST is for automatic segmentation, and MOSES is for segmentation evaluation. These tools are designed to be a unified set in which data can be conveniently exchanged. They have value not only for improving the reliability and accuracy of algorithms of uterine cervix image segmentation, but also promoting collaboration between biomedical experts and engineers which are crucial to medical image-processing applications. Although the CST is designed for the unique characteristics of cervigrams, the BMT and MOSES are very general and extensible, and can be easily adapted to other biomedical image collections.     

A flexible image segmentation prior to parametric estimation

A flexible method based on spatial and temporal pixel variance to compute parametric images in positron emission tomography (PET) is reported. For [¹⁸F]fluorodeoxyglucose and [¹⁵O]water brain studies, images were segmented based on coefficients of variation and correlation coefficients of neighboring pixels, and kinetic parameters were estimated by dynamic (DYN) and autoradiographic (ARG) fitting. For comparison, regional glucose metabolism (rCMRGlc) and blood flow (rCBF) in both DYN and ARG were estimated from segmented and usual images. The maximal relative error was found to be 4, 10 and 17% for ARG and DYN rCMRGlc and DYN rCBF, respectively.     

Improving image contrast using principal component analysis for subsequent image segmentation

This article presents a technique for improving MR image contrast by linearly combining multiple MR images with different tissue contrast. The weighting coefficients of the linear combination are derived using principal component analysis. The contrast-enhanced composite image is segmented subsequently using gray level-based 1D segmentation methods. The technique reduces a multispectral image set to composite eigenimages and allows application of appropriate 1D segmentation methods that do not have equivalent counterparts in multispectral methods.     

SketchSnakes: sketch-line initialized Snakes for efficient interactive medical image segmentation.

We present an intuitive, fast and accurate 2D interactive segmentation method that combines a general subdivision-curve Snake possessing powerful editing capabilities, with a novel sketch-line user initialization process, and a pen input device. Using the pen (or a mouse), the Snake is quickly and precisely initialized with a few quick sketch lines drawn across the width of the target object. The smooth contour constructed using these lines is extremely close to the position and shape of the object boundary. This makes the Snake's task of snapping to the object boundary much simpler and hence more likely to succeed in noisy images with minimal user editing. We apply our Snake to the segmentation of several 2D medical images to demonstrate it's efficiency, accuracy and robustness. We also compare SketchSnakes to Adobe Photoshop's Magnetic Lasso (Adobe Systems Inc., Adobe Photoshop User Guide, 2002) as well as a recent graph-cut based image cutout tool known as Snap (Digital Film Tools LLC, Snap User Guide, 2007) in order to highlight SketchSnakes effectiveness.     

Live-wire-based segmentation using similarities between corresponding image structures.

A live-wire-based segmentation method that exploits similarities of corresponding object contours is presented. The method accelerates the segmentation process transferring anchor points of segmented reference contours to unsegmented target slices automatically. The target contours are created using the live-wire algorithm trained by features of the reference contours. An automatic contour correction improves the segmentation. Only few user interactions with an intuitive contour editor are necessary. The evaluation using intra- and interpatient transfer shows that 51-73% of interaction time can be saved compared to normal live-wire preserving the segmentation quality.     

Medical image segmentation with knowledge-guided robust active contours.

Medical image segmentation techniques typically require some form of expert human supervision to provide accurate and consistent identification of anatomic structures of interest. A novel segmentation technique was developed that combines a knowledge-based segmentation system with a sophisticated active contour model. This approach exploits the guidance of a higher-level process to robustly perform the segmentation of various anatomic structures. The user need not provide initial contour placement, and the high-level process carries out the required parameter optimization automatically. Knowledge about the anatomic structures to be segmented is defined statistically in terms of probability density functions of parameters such as location, size, and image intensity (eg, computed tomographic [CT] attenuation value). Preliminary results suggest that the performance of the algorithm at chest and abdominal CT is comparable to that of more traditional segmentation techniques like region growing and morphologic operators. In some cases, the active contour-based technique may outperform standard segmentation methods due to its capacity to fully enforce the available a priori knowledge concerning the anatomic structure of interest. The active contour algorithm is particularly suitable for integration with high-level image understanding frameworks, providing a robust and easily controlled low-level segmentation tool. Further study is required to determine whether the proposed algorithm is indeed capable of providing consistently superior segmentation.     

FORTRAN algorithms for evaluating Fourier transforms of line spread functions

FORTRAN IV algorithms are presented for calculating the modulation transfer function, the phase transfer function, the modulation transfer function area, an information transfer function, and the optimum frequency response as well as plotting the curves for these matrices. An important feature of the programs is an editing routine.     

一种新的用于宫颈癌粘连细胞图像分割的分水岭算法

目的：探讨一种能有效分割宫颈癌粘连细胞图像的算法,完成各粘连细胞边缘的准确分割。方法利用水平集算法从背景区域中提取目标细胞图像,应用极小值的距离变换算法将图像归一化后与感兴趣区域的梯度图像点乘来抑制无用的梯度信息,然后运用标记分水岭算法对粘连细胞图像进行分割。结果与结论实验结果表明,该算法实现了染色不均的粘连宫颈癌细胞的有效分割,在粘连细胞的边缘建立了较传统的分水岭分割方法更准确的分割线,具有显著的临床应用价值。     

Cluster analysis for automatic image segmentation in dynamic scintigraphies

An original and entirely automatic algorithm is proposed to select regions of interest (ROIs) on dynamic scintigrams. This algorithm is based on factor analysis and on cluster analysis. It consists of first extracting the orthogonal factor images of the series using factor analysis of correspondence. These factor images are then automatically segmented in ROIs using a hierarchical ascendant classification procedure. The distance used for the classification is the 'minimum added intra-class variance' distance. This algorithm has been implemented on a fast computer dedicated to nuclear medicine (Nodecrest Micas V system). The time of calculation on 1000 pixels from 40 images is less than 5 min when three factor images are used. This algorithm is validated using a numerical phantom and is illustrated using renal (99Tcm DTPA) and cardiac (equilibrium gated angiography) dynamic scintigraphies. The results show that the algorithm is able to recognize the bladder, the renal cavities and the renal parenchyma on the renal series, and the ventricules and the atria on the cardiac series.     

Methods for evaluating image quality in magnetic resonance imaging

A quality control program for magnetic resonance imaging is necessary to evaluate the major imaging characteristics and to monitor image performance constancy. The methods used to evaluate and monitor imaging quality characteristics and image quality variables are discussed. These imaging characteristics and methods may be utilized by the trained radiology technologist to assist in the continuation of a quality control program for magnetic resonance imaging.