Multiatlas‐based segmentation of female pelvic organs: Application for computer‐aided diagnosis of cervical cancer

Atlas‐based segmentation is a high level segmentation technique which has become a standard paradigm for exploiting prior knowledge in image segmentation. Recent multiatlas‐based methods have provided greatly accurate segmentations of different parts of the human body by propagating manual delineations from multiple atlases in a data set to a query subject and fusing them. The female pelvic region is known to be of high variability which makes the segmentation task difficult. We propose, here, an approach for the segmentation of magnetic resonance imaging (MRI) called multiatlas‐based segmentation using online machine learning (OML). The proposed approach allows separating regions which may be affected by cervical cancer in a female pelvic MRI. The suggested approach is based on an online learning method for the construction of the dataset of atlases. The experiments demonstrate the higher accuracy of the suggested approach compared to a segmentation technique based on a fixed dataset of atlases and single‐atlas‐based segmentation technique.     

Characterization of a Completely User-Independent Algorithm for Carotid Artery Segmentation in 2-D Ultrasound Images

The analysis of the carotid artery wall is crucial for the diagnosis of serious cardiovascular pathologies or for the assessment of a subject's cardiovascular risk. Several algorithms have been proposed for the segmentation of ultrasound carotid artery images, but almost all require a certain degree of user interaction. We recently developed a completely user-independent algorithm for the segmentation of the common-carotid-artery wall; specifically, the algorithm traces the contour of the interfaces between the lumen and the intima layer and between the media and adventitia layers. In this paper, we show the characterization of the algorithm in terms of segmentation error. Moreover, we compare the output of the algorithm with the segmentations manually traced by four experts, using the percent statistics test and testing the automatically generated segmentation against the average human segmentations. We show that our algorithm's segmentation is not statistically different from that of a trained operator and that the segmentation error is lower than 1 pixel for both the lumen-intima interface and for the media-adventitia interface.     

Multiview cluster ensembles for multimodal MRI segmentation

It has been shown that the combination of multimodal magnetic resonance imaging (MRI) images can improve the discrimination of diseased tissue. The fusion of dissimilar imaging data for classification and segmentation purposes, however, is not a trivial task, as there is an inherent difference in information domains, dimensionality, and scales. This work proposed a multiview consensus clustering methodology for the integration of multimodal MR images into a unified segmentation aiming at heterogeneity assessment in tumoral lesions. Using a variety of metrics and distance functions this multiview imaging approach calculated multiple vectorial dissimilarity‐spaces for each MRI modality and it maked use of cluster ensembles to combine a set of unsupervised base segmentations into an unified partition of the voxel‐based data. The methodology was demonstrated with simulated data in application to dynamic contrast enhanced MRI and diffusion tensor imaging MR, for which a manifold learning step was implemented in order to account for the geometric constrains of the high dimensional diffusion information. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 56–67, 2015     

Unsupervised tissue classification of brain MR images for voxel‐based morphometry analysis

In this article, a fully unsupervised method for brain tissue segmentation of T1‐weighted MRI 3D volumes is proposed. The method uses the Fuzzy C‐Means (FCM) clustering algorithm and a Fully Connected Cascade Neural Network (FCCNN) classifier. Traditional manual segmentation methods require neuro‐radiological expertise and significant time while semiautomatic methods depend on parameter's setup and trial‐and‐error methodologies that may lead to high intraoperator/interoperator variability. The proposed method selects the most useful MRI data according to FCM fuzziness values and trains the FCCNN to learn to classify brain’ tissues into White Matter, Gray Matter, and Cerebro‐Spinal Fluid in an unsupervised way. The method has been tested on the IBSR dataset, on the BrainWeb Phantom, on the BrainWeb SBD dataset, and on the real dataset “University of Palermo Policlinico Hospital” (UPPH), Italy. Sensitivity, Specificity, Dice and F‐Factor scores have been calculated on the IBSR and BrainWeb datasets segmented using the proposed method, the FCM algorithm, and two state‐of‐the‐art brain segmentation software packages (FSL and SPM) to prove the effectiveness of the proposed approach. A qualitative evaluation involving a group of five expert radiologists has been performed segmenting the real dataset using the proposed approach and the comparison algorithms. Finally, a usability analysis on the proposed method and reference methods has been carried out from the same group of expert radiologists. The achieved results show that the segmentations of the proposed method are comparable or better than the reference methods with a better usability and degree of acceptance.     

Automated knowledge-based segmentation and analysis of the hip bones and cartilages using multi-slice CT data

Abstract

In this research, two independent multi-step methods for automatic segmentation of the hip femoral and acetabular cartilages, femur and pelvis bones from CT images are presented. In data acquisition, by injecting the contrast media in the hip joint, the hip articular space is enhanced in CT images. The hip bones and cartilages are then extracted based on available anatomical assumptions, employing quantitative measures and techniques such as radial differentiation and image bottom hat (IBH) as well as proposing several heuristic techniques. After segmentation, applying a marching cube surface rendering technique, three-dimensional visualisation of segmented cartilages and bones followed by thickness map estimation of the hip cartilages is performed. Manual segmentations of experts were employed as gold standard for evaluating the results. The proposed techniques were effective in the presence of 20 sets (5120 images) of actual in vivo hip CT data.     

Integrated segmentation of brain tumor images for radiotherapy and neurosurgery

Segmentation of brain tumor images is an important task in diagnosis and treatment planning for cancer patients. To achieve this goal with standard clinical acquisition protocols, conventionally, either classification algorithms are applied on multimodal MR images or atlas‐based segmentation is used on a high‐resolution monomodal MR image. These two approaches have been commonly regarded separately. We propose to integrate all the available imaging information into one framework to be able to use the information gained from the tissue classification of the multimodal images to perform a more precise segmentation on the high‐resolution monomodal image by atlas‐based segmentation. For this, we combine a state of the art regularized classification method with an enhanced version of an atlas‐registration approach including multiscale tumor‐growth modeling. This contribution offers the possibility to simultaneously segment subcortical structures in the patient by warping the respective atlas labels, which is important for neurosurgical planning and radiotherapy planning. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 59–63, 2013     

一种边界-区域相结合的图像分割算法

提出了一种模仿人类视觉机制的区域-细节的图像分割算法.首先提取图像边缘,然后将边缘分段切割,得到端点集合,然后从端点集合生成Delaunay三角形网络,以Delatmay三角形为顶点,相邻三角形的属性差异作为边的权重,构造图;9以基于图的分割算法生成最小生成树,划分区域.最后用Snake模型精确确定区域边界,生成准确的区域边缘.实验证明,这种区域分割和边缘检测相结合的方法能准确地分割非纹理图像,较好地克服了块现象和非连续边界,相比单一区域分割或者边缘检测方法有更好的分割结果,并且计算速度比较快.     

A novel approach for MR brain tumor classification and detection using optimal CNN-SVM model

The segmentation of brain tumors in magnetic resonance imaging plays a significant role in the field of image processing. This process has high computational complexity when handled manually by clinical experts. The accuracy in classifying and segmenting the brain tumor depends on the radiologists' experience. The computer-aided diagnosis-based brain tumor segmentation approach is proposed to overcome the existing limitations. The proposed convolutional neural network and support vector machine approach consists of the following stages. In the preprocessing stage, unwanted noise and intensity inhomogeneity are suppressed using an anisotropic diffusion filter. Then, the features are extracted using the deep convolutional neural network, and based on the features; the input brain image is classified into normal or abnormal using a support vector machine classifier. The proposed method gives a more successful accuracy rate of 2.11%. Compared with the other methods, the sensitivity and specificity values are also improved to 4.79% and 1.19%.     

Automatic cervical cancer segmentation in multimodal magnetic resonance imaging using an EfficientNet encoder in UNet++ architecture

Automatic cervical cancer segmentation in multimodal magnetic resonance imaging (MRI) is essential because tumor location and delineation can support patients' diagnosis and treatment planning. To meet this clinical demand, we present an encoder–decoder deep learning architecture which employs an EfficientNet encoder in the UNet++ architecture (E-UNet++). EfficientNet helps in effectively encoding multiscale image features. The nested decoders with skip connections aggregate multiscale features from low-level to high-level, which helps in detecting fine-grained details. A cohort of 228 cervical cancer patients with multimodal MRI sequences, including T2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient imaging, contrast enhancement T1-weighted imaging, and dynamic contrast-enhanced imaging (DCE), has been explored. Evaluations are performed by considering either single or multimodal MRI with standard segmentation quantitative metrics: dice similarity coefficient (DSC), intersection over union (IOU), and 95% Hausdorff distance (HD). Our results show that the E-UNet++ model can achieve DSC values of 0.681–0.786, IOU values of 0.558–0.678, and 95% HD values of 3.779–7.411 pixels in different single sequences. Meanwhile, it provides DSC values of 0.644 and 0.687 on three DCE subsequences and all MRI sequences together. Our designed model is superior to other comparative models, which shows the potential to be used as an artificial intelligence tool for cervical cancer segmentation in multimodal MRI.     

商品房购房群体市场细分研究

目的：将人口统计学变量与生活方式变量结合起来对商品房购房群体进行市场细分研究。方法：采用深度访谈法及问卷法,对北京市商品房购房群体特征及购买偏好进行调查。结果：将商品房购房群体通过生活方式变量划分为四个细分群体,工作积极群、社交休闲群、家庭自我群、中庸理性群。结论：商品房购房群体特点倾向于年龄较大,职业偏向于中高层管理人员且家庭收入较高,家庭状况为已婚。不同细分群体购房者在人口统计变量以及商品房购买偏好中有显著差异。     

Patient doses and image quality in digital chest radiology

Chest X-ray examination is one of the most frequently required procedures used in clinical practice. For studying the image quality of different X-ray digital systems and for the control of patient doses during chest radiological examinations, the standard anthropomorphic lung/chest phantom RSD 330 has been used and exposed in different digital modalities available in Slovakia. To compare different techniques of chest examination, a special software has been developed that enables researchers to compare digital imaging and communications in medicine header images from different digital modalities, using a special viewer. In this paper, this special software has been used for an anonymous correspondent audit for testing image quality evaluation by comparing various parameters of chest imaging, evaluated by 84 Slovak radiologists. The results of the comparison have shown that the majority of the participating radiologists felt that the highest image quality is reached with a flat panel, assessed by the entrance surface dose value, which is approximately 75% lower than the diagnostic reference level of chest examination given in the Slovak legislation. Besides the results of the audit, the possibilities of using the software for optimisation, education and training of medical students, radiological assistants, physicists and radiologists in the field of digital radiology will be described.     

Integration of synthetic aperture radar image segmentation method using Markov random field on region adjacency graph

A novel approach to obtain precise segmentation of synthetic aperture radar (SAR) images using Markov random field model on region adjacency graph (MRF-RAG) is presented. First, to form a RAG, the watershed algorithm is employed to obtain an initially over-segmented image. Then, a novel MRF is defined over the RAG instead of pixels so that the erroneous segmentation caused by speckle in SAR images can be avoided and the number of configurations for the combinatorial optimisation can be reduced. Finally, a modification method based on Gibbs sampler is proposed to correct edge errors, brought by the over-segmented algorithm, in the segmentations obtained by MRF-RAG. The experimental results both on simulated and real SAR images show that the proposed method can reduce the computational complexity greatly as well as increase the segmentation precision.     

B‐spline Based Multi‐organ Detection in Magnetic Resonance Imaging

In the context of the female pelvic medicine, non‐invasive magnetic resonance imaging is widely used for the diagnosis of pelvic floor disorders. Nowadays, in the clinical routine, diagnoses rely largely on human interpretation of medical images, on the experience of physicians, with sometimes subjective interpretations. Hence, image correlation methods would be an alternative way to assist physicians to provide more objective analyses with standard procedures and parametrisation for patient‐specific cases. Moreover, the main symptoms of pelvic system pathologies are abnormal mobilities. The finite element model simulation is a powerful tool for understanding such mobilities. Both the patient‐specific simulation and the image analysis require accurate and smooth geometries of the pelvic organs. This paper introduces a new method that can be classified as a model‐to‐image correlation approach. The method performs fast semi‐automatic detection of the bladder, vagina and rectum from magnetic resonance images for geometries reconstruction and further study of the mobilities. The approach consists of fitting a B‐spline model to the organ shapes in real images via a generated virtual image. We provided efficient, adaptive and consistent segmentation on a dataset of 19 patient images (healthy and pathological).     

On the importance of FIB-SEM specific segmentation algorithms for porous media

A new algorithmic approach to segmentation of highly porous three dimensional image data gained by focused ion beam tomography is described which extends the key-principle of local threshold backpropagation described in Salzer et al. (2012). The technique of focused ion beam tomography has shown to be capable of imaging the microstructure of functional materials. In order to perform a quantitative analysis on the corresponding microstructure a segmentation task needs to be performed. However, algorithmic segmentation of images obtained with focused ion beam tomography is a challenging problem for highly porous materials if filling the pore phase, e.g. with epoxy resin, is difficult. The gray intensities of individual voxels are not sufficient to determine the phase represented by them and usual thresholding methods are not applicable. We thus propose a new approach to segmentation that pays respect to the specifics of the imaging process of focused ion beam tomography. As an application of our approach, the segmentation of three dimensional images for a cathode material used in polymer electrolyte membrane fuel cells is discussed. We show that our approach preserves significantly more of the original nanostructure than a thresholding approach.     

A Novel Defect Inspection Method for Semiconductor Wafer Based on Magneto-Optic Imaging

The defects of semiconductor wafer may be generated from the manufacturing processes. A novel defect inspection method of semiconductor wafer is presented in this paper. The method is based on magneto-optic imaging, which involves inducing eddy current into the wafer under test, and detecting the magnetic flux associated with eddy current distribution in the wafer by exploiting the Faraday rotation effect. The magneto-optic image being generated may contain some noises that degrade the overall image quality, therefore, in this paper, in order to remove the unwanted noise present in the magneto-optic image, the image enhancement approach using multi-scale wavelet is presented, and the image segmentation approach based on the integration of watershed algorithm and clustering strategy is given. The experimental results show that many types of defects in wafer such as hole and scratch etc. can be detected by the method proposed in this paper.     

DIAGNOSIS: A Telematics-Enabled System for Medical Image Archiving, Management, and Diagnosis Assistance

In this paper, a modular system for medical image archiving, management, diagnosis support, and telematic cooperation is presented. The system provides digital imaging and communications in medicine (DICOM)-compatible tools for digital image processing and database management of medical images. The software features algorithms for preprocessing, manual or semi-automatic segmentation, automatic calculation of geometrical/size characteristics, and 3-D visualization of organs or selected regions of interest. Additionally, the system incorporates a database where patient data and information can be stored and retrieved. Access to the database is only permitted to authorized users. The user-friendly interface makes the software handy and accessible to clinicians, whereas the telematic components allow collaboration with remote experts. The pilot system incorporates a computer-aided diagnosis module aiming at providing support in the diagnosis of focal liver lesions from computed tomography images.      

Introduction to constancy check protocols in fluoroscopic systems

Reference dose or guidance levels are a well established approach to the reduction of patient doses in diagnostic radiology. There are two main methods of determining reference doses, one involves patient dose measurements and the other phantom dosimetry. The latter approach lends itself to the development of constancy test protocols, which may be used as part of an acceptance testing programme or to compare the performance of different imaging systems. Various constancy test protocols and procedures have been proposed and these are reviewed. The constancy test protocols developed within the DIMOND concerted action will be described in detail. The advantages and disadvantages of the various methods and approaches are compared and contrasted. The complementary nature of constancy check protocols with patient dosimetry studies is discussed.     

Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery

It is well-known that speckle is a multiplicative noise that degrades the visual evaluation in ultrasound imaging. The recent advancements in ultrasound instrumentation and portable ultrasound devices necessitate the need of more robust despeckling techniques for enhanced ultrasound medical imaging for both routine clinical practice and teleconsultation. The objective of this work was to carry out a comparative evaluation of despeckle filtering based on texture analysis, image quality evaluation metrics, and visual evaluation by medical experts in the assessment of 440 (220 asymptomatic and 220 symptomatic) ultrasound images of the carotid artery bifurcation. In this paper a total of 10 despeckle filters were evaluated based on local statistics, median filtering, pixel homogeneity, geometric filtering, homomorphic filtering, anisotropic diffusion, nonlinear coherence diffusion, and wavelet filtering. The results of this study suggest that the first order statistics filter lsmv, gave the best performance, followed by the geometric filter gf4d, and the homogeneous mask area filter lsminsc. These filters improved the class separation between the asymptomatic and the symptomatic classes based on the statistics of the extracted texture features, gave only a marginal improvement in the classification success rate, and improved the visual assessment carried out by the two experts. More specifically, filters lsmv or gf4d can be used for despeckling asymptomatic images in which the expert is interested mainly in the plaque composition and texture analysis; and filters lsmv, gf4d, or lsminsc can be used for the despeckling of symptomatic images in which the expert is interested in identifying the degree of stenosis and the plaque borders. The proper selection of a despeckle filter is very important in the enhancement of ultrasonic imaging of the carotid artery. Further work is needed to evaluate at a larger scale and in clinical practice the performance of the proposed despeckle filters in the automated segmentation, texture analysis, and classification of carotid ultrasound imaging.     

The analysis of rank ordered preference data based on Bradley-Terry Type Models Die Analyse von Präferenzdaten mit Hilfe von log-linearen Bradley-Terry Modellen

In this article we propose an approach to study the effect of consumer-specific information on (complete) rank ordered preference data by means of Bradley-Terry type models. The main idea is to transform the ranking data into paired comparison data, which can be modelled within the Generalised Linear Model framework by means of a log-linear model for a corresponding contingency table. Therefore, standard software can be used to estimate model parameters and a goodness-of-fit can be assessed in the usual way. This approach allows to simultaneously estimate object-specific parameters which, in the marketing context, can be interpreted as attractions of the analysed objects, as well as subject-object interaction parameters that represent the effects of consumer-specific variables on the attractions. The interaction parameters offer a statistically motivated approach for customer segmentation and market targeting. The outlined methodology is applied to preference judgements within a local daily newspaper market. It is shown that certain socio-economic characteristics of the consumers have significant influences on their preference structures.     

Classification of magnetic resonance images from rabbit renal perfusion

The feasibility of using chemometric techniques for the automatic detection of whether a rabbit kidney is pathological or not is studied. Sequential images of the kidney are acquired using Dynamic Contrast-Enhanced Magnetic Resonance Imaging with contrast agent injection. A segmentation approach based upon principal component analysis (PCA) is used to separate out the cortex from the rest of the kidney including the medulla, the renal pelvic, and the background. Two classifiers (Soft Independent Method of Class Analogy, SIMCA; Partial Least Squares Discriminant Analysis, PLS-DA) are tested for various types of data pre-treatment including segmentation, feature extraction, centering, autoscaling, standard normal variate transformation, Savitsky-Golay smoothing, and normalization. It is shown that (i) the renal cortex contains more discriminating information on kidney perfusion changes than the whole kidney, and (ii) the PLS-DA classifiers outperform the SIMCA classifiers. PLS-DA, preceded by an automated PCA-based segmentation of kidney anatomical regions, correctly classified all kidneys and constitutes a classification tool of the renal function that can be useful for the clinical diagnosis of renovascular diseases.