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A DEEP LEARNING SOLUTION FOR AUTOMATIC FETAL NEUROSONOGRAPHIC DIAGNOSTIC PLANE VERIFICATION USING CLINICAL STANDARD CONSTRAINTS

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ULTRASOUND IN MEDICINE AND BIOLOGY 2017年第12期43卷 2925-2933页

作者： Yaqub, Mohammad Kelly, Brenda Papageorghiou, Aris T. Noble, J. Alison Univ Oxford Inst Biomed Engn Dept Engn Sci Old Rd Campus Res Bldg Oxford OX3 7DQ England Univ Oxford Nuffield Dept Obstet & Gynaecol Oxford England

During routine ultrasound assessment of the fetal brain for biometry estimation and detection of fetal abnormalities, accurate imaging planes must be found by sonologists following a well-defined imaging protocol or clinical standard, which can be difficult for non-experts to do well. This assessment helps provide accurate biometry estimation and the detection of possible brain abnormalities. We describe a machine-learning method to assess automatically that transventricular ultrasound images of the fetal brain have been correctly acquired and meet the required clinical standard. We propose a deep learning solution, which breaks the problem down into three stages: (i) accurate localization of the fetal brain, (ii) detection of regions that contain structures of interest and (iii) learning the acoustic patterns in the regions that enable plane verification. We evaluate the developed methodology on a large real-world clinical data set of 2-D mid-gestation fetal images. We show that the automatic verification method approaches human expert assessment. (C) 2017 World Federation for Ultrasound in Medicine & Biology. All rights reserved.

关键词： Fetal ultrasound Neurosonography Pregnancy Trans-ventricular plane Convolutional neural networks knowledge-based image analysis

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A thermographic visual inspection system for crack detection in metal parts exploiting a robotic workcell

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ROBOTICS AND AUTONOMOUS SYSTEMS 2015年第PartB期74卷 351-359页

作者： Ghidoni, Stefano Antonello, Mauro Nanni, Loris Menegatti, Emanuele Univ Padua Dept Informat Engn I-35129 Padua Italy

Cracks are the main source of failure in the production of metal parts: systems for checking their presence are therefore crucial for defect-free production. In this paper, an autonomous system for performing this quality control is presented. The system is equipped with a heating tool, a thermocamera, and a robot to handle the part. The inspection process is based on the observation of the propagation of thermal waves through the inspected part, a method that can highlight very small cracks with high reliability. A knowledge-based approach to visual inspection is exploited for detecting the cracks: all the system parameters are known by means of an accurate calibration of the workcell. The system was tested on a large dataset and demonstrated its capability of detecting tiny production defects, that can lead to dangerous failures when the metal components are put under strong mechanical stress. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Computer vision Automated visual inspection Thermographic image analysis knowledge-based image analysis Pattern recognition

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A thermographic visual inspection system for crack detection in metal parts exploiting a robotic workcell

A thermographic visual inspection system for crack detection...

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13th International Conference on Intelligent Autonomous Systems (IAS)

作者： Ghidoni, Stefano Antonello, Mauro Nanni, Loris Menegatti, Emanuele Univ Padua Dept Informat Engn I-35129 Padua Italy

关键词： Computer vision Automated visual inspection Thermographic image analysis knowledge-based image analysis Pattern recognition

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Automated updating of road databases from aerial images

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2005年第3-4期6卷 199-213页

作者： Baltsavias, E Zhang, CS ETH Honggerberg Inst Geodesy & Photogrammetry Swiss Fed Inst Technol CH-8093 Zurich Switzerland

This paper presents a practical system for automated 3-D road network reconstruction from aerial images using knowledge-based image analysis. The system integrates processing of color image data and information from digital spatial databases, extracts and fuses multiple object cues, takes into account context information, employs existing knowledge, rules and models, and treats each road subclass accordingly. The key of the system is the use of knowledge as much as possible to increase success rate and reliability of the results, working in 2-D images and 3-D object space, and use of 2-D and 3-D interaction when needed. Another advantage of the developed system is that it can correctly and reliably handle problematic areas caused by shadows and occlusions. This work is part of a project to improve and update the 1:25,000 vector maps of Switzerland. The system was originally developed to processed stereo images. Recently, it has been modified to work also with single orthoimages. The system has been implemented as a stand-alone software package, and has been tested on a large number of images with different landscape. In this paper, various parts of the developed system are discussed, and the results of our system in the tests conducted independently by our project partner in Switzerland, and the test results with orthoimages in a test site in The Netherlands are presented together with the system performance evaluation. (c) 2004 Elsevier B.V. All rights reserved.

关键词： roads 3-D reconstruction map updating knowledge-based image analysis color context edge matching DSM/DTM roadmarks multiple cue integration performance evaluation semi-automated system

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Graph-supported verification of road databases

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2004年第3-4期58卷 152-165页

作者： Gerke, M Butenuth, M Heipke, C Willrich, F Leibniz Univ Hannover Inst Photogrammetry & Geoinformat D-30167 Hannover Germany

The verification of existing data is an important task in order to ensure a high level of data quality, such as is needed in geographic information systems (GIs). Today, this work is carried out manually by an operator, who compares vector data from databases with remotely sensed imagery. In this paper, a system for automated road data verification using digital image processing for the extraction of roads from aerial imagery and topological analysis in order to optimise the whole process in terms of reliability and efficiency is presented. The main goal is to call the operator's attention only to parts of the network, where the automated process did not find sufficient evidence of a road. The road extraction is supported by the use of prior knowledge on the global level (whether the road is situated in rural, urban or forest areas), and information on the road geometry and its attributes. The road extraction is executed twice. Firstly, with a strict parameter control ensuring the minimization of false positives and a subsequent evaluation, which denotes roads from the database being accepted or rejected. In a second step, a graph-based search algorithm detects connections, which are missing for an optimised road network. If rejected roads are part of these connections, they are checked again using a more tolerant parameter control. A detailed performance analysis of results shows the applicability of the proposed method for quality control of topographic road databases. (C) 2003 Elsevier B.V All rights reserved.

关键词： GIS database verification knowledge-based image analysis road network extraction parameter settings quality control reliability performance analysis

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Towards an operational system for automated updating of road databases by integration of imagery and geodata

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2004年第3-4期58卷 166-186页

作者： Zhang, CS ETH Honggerberg Swiss Fed Inst Technol Inst Geodesy & Photogrammetry CH-8093 Zurich Switzerland

The extraction of road networks from aerial images is one of the current challenges in digital photogrammetry and computer vision. In this paper, we present a practical system for 3-D road network reconstruction from aerial images using knowledge-based image analysis. In contrast to other approaches, the developed system integrates processing of color image data and information from digital spatial databases, extracts and fuses multiple object cues, takes into account context information, employs existing knowledge, rules and models, and treats each road subclass accordingly. The key of the system is the use of knowledge as much as possible to increase success rate and reliability of the results, working in 2-D images and 3-D object space, and use of 2-D and 3-D interaction when needed. Another advantage of the developed system is that it can correctly and reliably handle problematic areas caused by shadows and occlusions. This work is part of a project to improve and update the 1:25,000 vector maps of Switzerland. The system has been implemented as a stand-alone software package, and has been tested on a large number of images with different landscape. In this paper, various parts of the developed system are discussed, and the results of our system in recent benchmark tests conducted independently by our project partner in Switzerland, and the test results with black and white images in a test site in Belgium are presented together with the system performance evaluation. (C) 2003 Elsevier B.V. All rights reserved.

关键词： roads 3-D reconstruction map updating knowledge-based image analysis color context edge matching DSM/DTM roadmarks multiple cue integration performance evaluation semi-automated system

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knowledge-based automatic detection of multi-type lung nodules from multi-detector CT studies

Knowledge-based automatic detection of multi-type lung nodul...

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Medical Imaging 2002 Conference

作者： Qian, JZ Fan, L Wei, GQ Novak, CL Odry, B Shen, H Zhang, L Naidich, DP Ko, JP Rubinowitz, AN McGuinness, G Kohl, G Klotz, E Siemens Corp Res Inc Princeton NJ 08540 USA

ISBN: (纸本)0819444294

Multi-slice computed tomography (CT) provides a promising technology for lung cancer detection and treatment. To optimize automatic detections of a more complete spectrum of lung nodules on CT requires multiple specialized algorithms in a coherently integrated detection system. We have developed a knowledge-based system for automatic lung nodule detection and analysis, which coherently integrates several robust novel detection algorithms to detect different types of nodules, including those attached to the chest wall, nodules adjacent to or fed by vessels, and solitary nodules, simultaneously. The system architecture can be easily extended in the future to include a still greater range of nodule types, most importantly so-called ground-glass opacities (GGOs). In addition, automatic local adaptive histogram analysis, dynamic cross-correlation analysis, and the automatic volume projection analysis by using by data dimension reduction method, are used in nodule detection. The proposed system has been applied to 10 patients screened with low-dose multi-slice CT. Preliminary clinical tests show that (1) the false positive rate averages about 3.2 per study;and (2) by using the system radiologists are able to detect nearly twice the number of nodules as compared with working alone.

关键词： CT lung nodule automatic detection knowledge-based image analysis computer-aided diagnosis

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image analysis for computer-assisted internal fixation of hip fractures

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MEDICAL image analysis 2000年第2期4卷 137-159页

作者： Ozanian, TO Phillips, R Univ Hull Dept Comp Sci Kingston Upon Hull HU6 7RX N Humberside England

This paper focuses on the task of automatic feature detection for intra-operative drilling trajectory planning for computer-assisted internal fixation of hip fractures. The features of interest are the lateral cortex line of the femoral shaft, the femoral neck centre and the femoral head centre, the latter being the most challenging of all. Since the object is known, the detection process is regarded as a localisation task rather than a recognition one. Simple anatomical relationships between bone parts provide a naturally hierarchical approach to searching, allowing refinement of image-derived information based on a priori constraints. Use of knowledge and an unconventional "divide-and-conquer" approach produce more reliable and faster results than the standard global image processing routine. analysis of summed 1D grey level profiles is used as a main segmentation tool to carry out the above strategy. (C) 2000 Elsevier Science B.V. All rights reserved.

关键词： knowledge-based image analysis computer-assisted orthopaedic surgery fluoroscopy

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An expert system for the detection of cervical cancer cells using knowledge-based image analyzer

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ARTIFICIAL INTELLIGENCE IN MEDICINE 1996年第1期8卷 67-90页

作者： Chan, SWK Leung, KS Wong, WSF CHINESE UNIV HONG KONG DEPT COMP SCIHONG KONGHONG KONG UNIV NEW S WALES SCH MEDDEPT OBSTET & GYNAECOLSYDNEYNSW 2052AUSTRALIA

Analyzing for abnormalities of cell images in the cervix uteri provides a basis for reducing deaths and morbidity from cervical cancer through detection of potentially cancerous cells, provision of prompt advice and opportunities for follow-up and treatments. However, cytopathology is usually based on subjective interpretation of morphological features. Arbitrary criteria have to be devised for their classifications. Subjective interpretations of such criteria are likely to result in diagnostic shifts and consequently disagreement occurs between different interpreters. This article presents a novel approach to the composition of segmentation and diagnosis processes for biomedical image analysis. A prototype expert system has been developed to provide an objective and reliable tool to gynaecologists. Special image analyzing techniques are used and a set of knowledge sources is designed. The expert system employs a robust control strategy which minimizes the amount of domain-specific control knowledge. It has been proved to work effectively in the detection of cervical cancer.

关键词： expert system knowledge-based image analysis medical diagnosis cervical cancer

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knowledge-based SEGMENTATION AND STATE-based CONTROL IN image-analysis - 2 EXAMPLES FROM THE BIOMEDICAL DOMAIN

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SIGNAL PROCESSING 1993年第1-2期32卷 201-215页

作者： MOLANDER, S BROMAN, H Department of Applied Electronics Chalmers University of Technology S-412 96 Göteborg Sweden

This paper presents a system for automatic image analysis featuring knowledge-based segmentation and state-based control. It is assumed that the image depicts known scenes with objects characterized by well-defined spatial relations and fuzzy boundaries. The image is preprocessed, the results are stored in a multiresolution pyramid, and segmentation primitives are stored as feature vectors. Regions are labeled with a simple model of direction and distance relations in the scene. The labeling procedure affects the segmentation: if the labeled regions in the image are incompatible with the model, the segmentation and labeling can be reiterated on a higher resolution, or objects can be removed using a maximal inconsistency criterion. A state-based control mechanism is introduced;a scheduler initiates a set of processing states in an execution evaluation cycle, and a diagnosis procedure evaluates the current results and determines which processing state to pursue at the next timestep. Two image domains are described, the delineation of the left ventricle in four-chamber ultrasound cardiac images, and gamma camera images of the heart.

关键词： knowledge-based image analysis BLACKBOARD SYSTEMS STATE-based CONTROL RELAXATION LABELING PYRAMID REPRESENTATION FEATURE VECTORS STATISTICAL SEGMENTATION BLOB SEGMENTATION REGION GROWING ULTRASOUND imageS GAMMA-CAMERA imageS

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