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Large scale validation of the M5L lung CAd on heterogeneous CT datasets

MEdICAL PHYSICS 2015年第4期42卷 1477-1489页

作者： Lopez Torres, E. Fiorina, E. Pennazio, F. Peroni, C. Saletta, M. Camarlinghi, N. Fantacci, M. E. Cerello, P. CEADEN Havana 11300 Cuba Ist Nazl Fis Nucl Sez Torino I-10125 Turin Italy Univ Turin Dept Phys I-10125 Turin Italy Univ Pisa Dept Phys I-56127 Pisa Italy Ist Nazl Fis Nucl Sez Pisa I-56127 Pisa Italy

Purpose: M5L, a fully automated computer-aided detection (CAd) system for the detection and segmentation of lung nodules in thoracic computed tomography (CT), is presented and validated on several image datasets. Methods: M5L is the combination of two independent subsystems, based on the Channeler Ant Model as a segmentation tool [ lung channeler ant model (lungCAM)] and on the voxel-based neural approach. The lungCAM was upgraded with a scan equalization module and a new procedure to recover the nodules connected to other lung structures;its classification module, which makes use of a feed-forward neural network, is based of a small number of features (13), so as to minimize the risk of lacking generalization, which could be possible given the large difference between the size of the training and testing datasets, which contain 94 and 1019 CTs, respectively. The lungCAM (standalone) and M5L (combined) performance was extensively tested on 1043 CT scans from three independent datasets, including a detailed analysis of the full Lung Image database Consortium/Image database Resource Initiative database, which is not yet found in literature. Results: The lungCAM and M5L performance is consistent across the databases, with a sensitivity of about 70% and 80%, respectively, at eight false positive findings per scan, despite the variable annotation criteria and acquisition and reconstruction conditions. A reduced sensitivity is found for subtle nodules and ground glass opacities (GGO) structures. A comparison with other CAd systems is also presented. Conclusions: The M5L performance on a large and heterogeneous dataset is stable and satisfactory, although the development of a dedicated module for GGOs detection could further improve it, as well as an iterative optimization of the training procedure. The main aim of the present study was accomplished: M5L results do not deteriorate when increasing the dataset size, making it a candidate for supporting radiologists on large

关键词： lung CT computer aided detection (CAd) image processing 3-d segmentation LIdC IdRI ANOdE09 screening

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A new parametric model-based technique in bone tumour analysis

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COMPUTERIZEd MEdICAL IMAGING ANd GRAPHICS 2014年第5期38卷 315-325页

作者： Czajkowska, Joanna Pietka, Ewa Silesian Tech Univ Dept Comp Sci & Med Equipment Fac Biomed Engn PL-41800 Zabrze Poland Univ Siegen Inst Vis & Graph D-53076 Siegen Germany

The study presents a new statistical model based segmentation technique dedicated to inhomogeneous bone tumours structure analysis. The presented 3-d segmentation procedure applies a statistic description of the structure based on Gaussian mixture model and an adaptive model-based relative fuzzy connectedness technique. It has been tested on 94 different MR series of 38 young patients. The final segmentation results have been evaluated using two different verification techniques and compared with other segmentation methods. The developed technique yields higher bone tumours segmentation accuracy compared to results obtained with conventional fuzzy connectedness approach and different segmentation methods presented in the literature, and based on active contour models or statistical analysis. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Magnetic resonance imaging Bone tumours 3-d segmentation Gaussian mixture model Fuzzy connectedness analysis

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detection of 3-d Individual Trees in Urban Areas by Combining Airborne LidAR data and Imagery

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IEEE GEOSCIENCE ANd REMOTE SENSING LETTERS 2013年第6期10卷 1355-1359页

作者： Yao, Wei Wei, Yuzhang Munich Univ Appl Sci Dept Geoinformat D-80333 Munich Germany Beijing Normal Univ State Key Lab Remote Sensing Sci Beijing 100875 Peoples R China Chinese Acad Sci Inst Remote Sensing Applicat Beijing 100875 Peoples R China

An automated approach to extracting 3-d individual trees in urban areas is developed based on jointly analyzing airborne LidAR data and imagery. First, the spectral, geometric, and spatial context attributes are defined and integrated at the LidAR point level. Then, a binary AdaBoost classifier is used to separate points belonging to trees from other urban objects. Once the classification is completed, a spectral clustering method by applying the normalized cuts to a graph structure of point clouds of the vegetation class is performed to segment single trees. The geometric and spectral attributes play an important role in establishing the weight matrix, which measures the similarity between every two graph nodes and determines the cut function. The performance of the approach is validated by real urban data sets, which were acquired over two European cities. The results show that 3-d individual trees can be detected with mean accuracy of up to 0.65 and 0.12 m for tree position and height. Based on the results of this work, geometric and biophysical properties of individual trees can be further retrieved.

关键词： AdaBoost airborne point cloud imagery tree detection urban areas 3-d segmentation

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VECTOR-BASEd ACTIVE SURFACES FOR segmentation OF dYNAMIC PET IMAGES

VECTOR-BASED ACTIVE SURFACES FOR SEGMENTATION OF DYNAMIC PET...

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IEEE 10th International Symposium on Biomedical Imaging - From Nano to Macro (ISBI)

作者： Jaouen, Vincent Gonzalez, Paulo Stute, Simon Guilloteau, denis Buvat, Irene Tauber, Clovis Univ Tours INSERM UMRS U930 Tours France Univ Paris 11 Orsay France Univ Paris 07 CNRS IN2P3 IMNCUMR 8165 Orsay France Univ Catolica Maule Talca Chile

ISBN: (纸本)9781467364553

dynamic PET imaging enables the study of radiotracers concentration kinetics along time. However, PET images suffer from limited spatial resolution, low signal-to-noise ratio, and time dependent contrast between tissues, making segmentation of biological volumes difficult. deformable models are of great interest due to their inherent boundary continuity, but their applications to PET images still remains challenging due to high sensitivity to noise. To address these limitations, we propose a method to perform 3-d+time dynamic PET image segmentation using parametric active surfaces based on a gradient of the vectorial image. This method takes advantage of both spatial and temporal consistency of the signal along the acquisition. We validate our method using GATE Monte Carlo simulations and compare it with single frame based active surface models. We show significant improvement of several figures of merit.

关键词： 3-d segmentation Active surface dynamic PET Vector field convolution

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AUTOMATIC BONE LOCALIZATION ANd FRACTURE dETECTION FROM VOLUMETRIC ULTRASOUNd IMAGES USING 3-d LOCAL PHASE FEATURES

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ULTRASOUNd IN MEdICINE ANd BIOLOGY 2012年第1期38卷 128-144页

作者： Hacihaliloglu, Ilker Abugharbieh, Rafeef Hodgson, Antony J. Rohling, Robert N. Guy, Pierre Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada Univ British Columbia Dept Mech Engn Vancouver BC V6T 1Z4 Canada Univ British Columbia Dept Orthopaed Vancouver BC V6T 1Z4 Canada

This article presents a novel method for bone segmentation from three-dimensional (3-d) ultrasound images that derives intensity-invariant 3-d local image phase measures that are then employed for extracting ridge-like features similar to those that occur at soft tissue/bone interfaces. The main contributions in this article include: (1) the extension of our previously proposed phase-symmetry-based bone surface extraction from two-dimensional (2-d) to 3-d images using 3-d Log-Gabor filters;(2) the design of a new framework for accuracy evaluation based on using computed tomography as a gold standard that allows the assessment of surface localization accuracy across the entire 3-d surface;(3) the quantitative validation of accuracy of our 3-d phase-processing approach on both intact and fractured bone surfaces using phantoms and ex vivo 3-d ultrasound scans;and (4) the qualitative validation obtained by scanning emergency room patients with distal radius and pelvis fractures. We show a 41% improvement in surface localization error over the previous 2-d phase symmetry method. The results demonstrate clearly visible segmentations of bone surfaces with a localization accuracy of <0.6 mm and mean errors in estimating fracture displacements below 0.6 mm. The results show that the proposed method is successful even for situations when the bone surface response is weak due to shadowing from muscle and fascia interfaces above the bone, which is a situation where the 2-d method fails. (E-mail: rafeef@***) (C) 2012 World Federation for Ultrasound in Medicine & Biology.

关键词： Ultrasound Local phase features Phase symmetry Bone localization 3-d segmentation Log Gabor filters Orthopaedic surgery

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Geometrically Induced Force Interaction for Three-dimensional deformable Models

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2011年第5期20卷 1373-1387页

作者： Yeo, Si Yong Xie, Xianghua Sazonov, Igor Nithiarasu, Perumal Swansea Univ Coll Engn Swansea SA2 8PP W Glam Wales Swansea Univ Dept Comp Sci Swansea SA2 8PP W Glam Wales

In this paper, we propose a novel 3-d deformable model that is based upon a geometrically induced external force field which can be conveniently generalized to arbitrary dimensions. This external force field is based upon hypothesized interactions between the relative geometries of the deformable model and the object boundary characterized by image gradient. The evolution of the deformable model is solved using the level set method so that topological changes are handled automatically. The relative geometrical configurations between the deformable model and the object boundaries contribute to a dynamic vector force field that changes accordingly as the deformable model evolves. The geometrically induced dynamic interaction force has been shown to greatly improve the deformable model performance in acquiring complex geometries and highly concave boundaries, and it gives the deformable model a high invariancy in initialization configurations. The voxel interactions across the whole image domain provide a global view of the object boundary representation, giving the external force a long attraction range. The bidirectionality of the external force field allows the new deformable model to deal with arbitrary cross-boundary initializations, and facilitates the handling of weak edges and broken boundaries. In addition, we show that by enhancing the geometrical interaction field with a nonlocal edge-preserving algorithm, the new deformable model can effectively overcome image noise. We provide a comparative study on the segmentation of various geometries with different topologies from both synthetic and real images, and show that the proposed method achieves significant improvements against existing image gradient techniques.

关键词： deformable model geometric potential field level set method object localization 3-d segmentation

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3-d segmentation of brain volume thresholding method

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Wuhan Ligong daxue Xuebao/Journal of Wuhan University of Technology 2006年第4期28卷 122-124页

作者： Cai, Hua-Jie Tian, Jin-Wen Image Institute Huazhong University of Science and Technology Wuhan 430074 China

3-d segmentation of brain volume data is one of the most important technologies, especially, how to determine the threshold is the key which affects the results. Three ways which determine threshold were described. Then they were put into practice. different performances indicate that the way of fuzzy maximum entropy based on genetic algorithm is more excellent, which gives better performance and little impaction by noise.

关键词： 3-d segmentation Fuzzy maximum entropy Genetic algorithm Threshold

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On 3d partial matching of meaningful parts

On 3D partial matching of meaningful parts

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IEEE International Conference on Image Processing (ICIP 2007)

作者： Mademlis, A. daras, P. Tzovaras, d. Strintzis, M. G. Aristotle Univ Thessaloniki Informat Proc Lab Dept Elect & Comp Engn GR-54006 Thessaloniki Greece Informat & Telemat Inst Thessaloniki Greece

ISBN: (纸本)9781424414369

In this paper a method suitable for partial matching between 3d objects is presented. The 3d objects are firstly segmented into meaningful parts extending a method which is based on the medial surface of the objects. Then, geometric features are extracted for each part using the Spherical Trace Transform. The extracted features are combined and their covariance matrix is computed as a descriptor of each part. The contribution of the proposed approach is that a meaningful segmentation of 3d objects based on medial surface is achieved and that partial matching is performed on meaningful parts, in a rotation, translation and scaling invariant manner. The experimental results performed, proved that the proposed approach achieves accurate partial matching results in terms of distinct meaningful parts as well as satisfactory overall accuracy.

关键词： 3-d processing 3-d feature extraction 3-d segmentation

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Three-dimensional display and arbitrary region interactive segmentation of high-resolution virus capsids from cryo-electron microscopy single particle reconstruction

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JOURNAL OF ELECTRON MICROSCOPY 2005年第5期54卷 455-460页

作者： Li, J Liu, Z Li, KP Cui, JM Zhang, QF Li, YY Zhang, JQ Zhongshan Univ State Key Lab Biocontrol Div Struct Biol Guangzhou 510275 Peoples R China Kunming Gen Hosp Dept Equipment Kunming 650032 Peoples R China

Recent advances in cryo-electron microscopy (cryo-EM) instrumentation and single particle reconstruction have created opportunities for high-throughput and high-resolution three-dimensional (3-d) structure determination of virus. In order to visualize and effectively understand the 3-d structure, we present a display method based on surface rendering, which has the function of 3-d arbitrary region interactive segmentation and quantitative analysis, and integrate them into a software package called CEM-3dVdSS (cryo-EM 3-d virus display and arbitrary region segmentation system). CEM-3dVdSS consists of a complete set of modular programs for 3-d display and segmentation of icosahedral virus, which is organized under a graphical user interface and provides user-friendly options. First, we convert volume data in the MRC format obtained by cryo-EM single particle reconstruction to the format of our own software;in the preprocessing step, the original volume data are compressed and a better vector dimension is found for controlling the speed and detail of display. Then, the new volume data can be displayed and segmented using CEM-3dVdSS. We demonstrate the applicability of CEM-3dVdSS by displaying the 3-d structures of 2.5 nm (resolution) BmCPV (Bombyx mori cytoplasmic polyhedrosis virus), 2.5 nm CSBV (Chinese Sacbrood bee virus) and 1.4 nm C6/36dNV (densonucleosis virus). As a result, both the 3-d display speed and signal-to-noise ratio of CEM-3dVdSS are improved compared with the original method, and the segmentation results become precise and more intact with additional function of quantitative analysis of 3-d structure.

关键词： cryo-electron microscopy 3-d display 3-d segmentation icosahedral virus

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Automatic colon segmentation with dual scan CT colonography

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JOURNAL OF dIGITAL IMAGING 2005年第1期18卷 42-54页

作者： Li, H Santago, P Wake Forest Univ Bowman Gray Sch Med Dept Biomed Engn Winston Salem NC 27157 USA

We present a fully automated three-dimensional (3-d) segmentation algorithm to extract the colon lumen surface in CT colonography. Focusing on significant-size polyp detection, we target at an efficient algorithm that maximizes overall colon coverage, minimizes the extracolonic components, maintains local shape accuracy, and achieves high segmentation speed. Two-dimensional (2-d) image processing techniques are employed first, resulting in automatic seed placement and better colon coverage. This is followed by near-air threshold 3-d region-growing using an improved marching-cubes algorithm, which provides fast and accurate surface generation. The algorithm constructs a well-organized vertex-triangle structure that uniquely employs a hash table method, yielding an order of magnitude speed improvement. We segment two scans, prone and supine, independently and with the goal of improved colon coverage. Both segmentations would be available for subsequent polyp detection systems. Segmenting and analyzing both scans improves surface coverage by at least 6% over supine or prone alone. According to subjective evaluation, the average coverage is about 87.5% of the entire colon. Employing near-air threshold and elongation criteria, only 6% of the data sets include extracolonic components (EC) in the segmentation. The observed surface shape accuracy of the segmentation is adequate for significant-size (6 mm) polyp detection, which is also verified by the results of the prototype detection algorithm. The segmentation takes less than 5 minutes on an AMd 1-GHz single-processor PC, which includes reading the volume data and writing the surface results. The surface-based segmentation algorithm is practical for subsequent polyp detection algorithms in that it produces high coverage, has a low EC rate, maintains local shape accuracy, and has a computational efficiency that makes real-time polyp detection possible. A fully automatic or computer-aided polyp detection system using this

关键词： computed tomographic colonography polyp detection 3-d segmentation region-growing marching-cubes

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