This work addresses the issue of variable selection within the context of breast cancer classification with mammography. A comprehensive repository of feature vectors was used including a hybrid subset gathering image-based and clinical features. It aimed to gather experimental evidence of variable selection in terms of cardinality, type and find a classification scheme that provides the best performance over the Area Under Receiver Operating Characteristics Curve (AUC) scores using the ranked features subset. We evaluated and classified a total of 300 subsets of features formed by the application of Chi-Square Discretization, Information-Gain, One-Rule and RELIEF methods in association with Feed-Forward Backpropagation Neural Network (FFBP), Support Vector Machine (SVM) and Decision Tree J48 (DTJ48) Machine Learning Algorithms (MLA) for a comparative performance evaluation based on AUC scores. A variable selection analysis was performed for Single-View Ranking and Multi-View Ranking groups of features. Features subsets representing Microcalcifications (MCs), Masses and both MCs and Masses lesions achieved AUC scores of 0.91, 0.954 and 0.934 respectively. Experimental evidence demonstrated that classification performance was improved by combining image-based and clinical features. The most important clinical and image-based features were StromaDistortion and Circularity respectively. Other less important but worth to use due to its consistency were Contrast, Perimeter, Microcalcification, Correlation and Elongation.
In medical image processing and analysis it is often required to perform segmentation for quantitative measures
of extent, volume and shape.
The validation of new segmentation methods and tools usually implies comparing their various outputs among
themselves (or with a ground truth), using similarity metrics. Several such metrics are proposed in the literature
but it is important to select those which are relevant for a particular task as opposed to using all metrics and
therefore avoiding additional computational cost and redundancy.
A methodology is proposed which enables the assessment of how different similarity and discrepancy metrics
behave for a particular comparison and the selection of those which provide relevant data.
Detailed morphological analysis of pulmonary structures and tissue, provided by modern CT scanners, is of
utmost importance as in the case of oncological applications both for diagnosis, treatment, and follow-up. In this
case, a patient may go through several tomographic studies throughout a period of time originating volumetric
sets of image data that must be appropriately registered in order to track suspicious radiological findings.
The structures or regions of interest may change their position or shape in CT exams acquired at different
moments, due to postural, physiologic or pathologic changes, so, the exams should be registered before any
follow-up information can be extracted. Postural mismatching throughout time is practically impossible to
avoid being particularly evident when imaging is performed at the limiting spatial resolution. In this paper, we
propose a method for intra-patient registration of pulmonary CT studies, to assist in the management of the
oncological pathology. Our method takes advantage of prior segmentation work. In the first step, the pulmonary
segmentation is performed where trachea and main bronchi are identified. Then, the registration method proceeds
with a longitudinal alignment based on morphological features of the lungs, such as the position of the carina, the
pulmonary areas, the centers of mass and the pulmonary trans-axial principal axis. The final step corresponds to
the trans-axial registration of the corresponding pulmonary masked regions. This is accomplished by a pairwise
sectional registration process driven by an iterative search of the affine transformation parameters leading to
optimal similarity metrics. Results with several cases of intra-patient, intra-modality registration, up to 7 time
points, show that this method provides accurate registration which is needed for quantitative tracking of lesions
and the development of image fusion strategies that may effectively assist the follow-up process.
KEYWORDS: Picture Archiving and Communication System, Medical imaging, Teleradiology, Radiology, Legal, Medicine, Data communications, X-rays, Equipment and services, Lead
The asymmetric distribution of PACS equipment and service providers across countries leads typically to the need to
hire third party service professionals outside the institutions where the exams were made. In this paper we present a
brokerage mechanism that puts customers and remote providers together in a seamless way.
The proposed solution, asserted with a case study for the Portuguese national health system, addresses the problems that
now impair the optimal provision of those services, enabling a more agile relationship between buyers and sellers,
optimizing administrative work and complying with clinical and legal requirements under discussion in the European
Union for the free movement of patients and professional health workers.
In this document, the detailed process and technical description of the broker functioning is made, and the main benefits
for the participants are also evaluated from a technical and economical point of view.
Finally, in the discussion chapter, an assessment of the creation of a spot market for imaging studies is made and the
integration with other similar markets is discussed.
KEYWORDS: Picture Archiving and Communication System, Medical imaging, Image compression, Internet, Web services, Diagnostics, Image quality, Medicine, Computer programming, Cardiology
During the last years, the ubiquity of web interfaces have pushed practically all PACS suppliers to develop client
applications in which clinical practitioners can receive and analyze medical images, using conventional personal
computers and Web browsers. However, due to security and performance issues, the utilization of these software
packages has been restricted to Intranets. Paradigmatically, one of the most important advantages of digital image
systems is to simplify the widespread sharing and remote access of medical data between healthcare institutions.
This paper analyses the traditional PACS drawbacks that contribute to their reduced usage in the Internet and describes
a PACS based on Web Services technology that supports a customized DICOM encoding syntax and a specific
compression scheme providing all historical patient data in a unique Web interface.
A segmentation method is a mandatory pre-processing step in many automated or semi-automated analysis tasks such as region identification and densitometric analysis, or even for 3D visualization purposes. In this work we present a fully automated volumetric pulmonary segmentation algorithm based on intensity discrimination and morphologic procedures. Our method first identifies the trachea as well as primary bronchi and then the pulmonary region is identified by applying a threshold and morphologic operations. When both lungs are in contact, additional procedures are performed to obtain two separated lung volumes. To evaluate the performance of the method, we compared contours extracted from 3D lung surfaces with reference contours, using several figures of merit. Results show that the worst case generally occurs at the middle sections of high resolution CT exams, due the presence of aerial and vascular structures. Nevertheless, the average error is inferior to the average error associated with radiologist inter-observer variability, which suggests that our method produces lung contours similar to those drawn by radiologists. The information created by our segmentation algorithm is used by an identification and representation method in pulmonary emphysema that also classifies emphysema according to its severity degree. Two clinically proved thresholds are applied which identify regions with severe emphysema, and with highly severe emphysema. Based on this thresholding strategy, an application for volumetric emphysema assessment was developed offering new display paradigms concerning the visualization of classification results. This framework is easily extendable to accommodate other classifiers namely those related with texture based segmentation as it is often the case with interstitial diseases.
Related to the demand for fast and efficient tomographic reconstruction methods, the interest for Direct Fourier (DF) methods, which have a reduced computational complexity, has been growing. In this paper we present a new NUFFT-based DF reconstruction method which can be directly applied to fan-beam CT data sets avoiding the interpolation in Radon space as well as the interpolation in Fourier space. The performance of the new algorithm, in ideal and noisy conditions, is compared to those of other well known reconstruction methods, revealing an excellent behavior, specially in noisy conditions.
This paper presents a Cardiology oriented information system that provides permanent availability of all clinical history, including alphanumeric and image data, with time and cost-effective transmission (reduced download time), without loss of image diagnosis quality and based on a Web Multimedia Integrated Access Interface. This implies the integration of HIS and PACS in a unique access interface, providing on-line and fast access to authorized healthcare professionals. The benefits obtained from the HIS-PACS integration and from the availability of all historical patient data are unquestionable to practitioners but also to the patients. Moreover, the system includes a telematic platform capable of establishing cooperative telemedicine sessions where our most impressive utilization is a transcontinental work platform for cardiovascular ultrasound. The key point of our approach starts with the construction of a DICOM private transfer syntax that is prepared to support any video encoder installed on a Windows-based station. With this structure it is possible to select the best encoder to a specific modality and work scenario. Good trade-off between compression ratio and diagnostic quality, low network traffic load, backup facilities and data portability are other achievements of this system.
Quantitative evaluation of the performance of segmentation algorithms on medical images is crucial before their clinical use can be considered. We have quantitatively compared the contours obtained by a pulmonary segmentation algorithm to contours manually-drawn by six expert imaiologists on the same set of images, since the ground truth is unknown. Two types of variability (inter-observer and intra-observer) should be taken into account in the performance evaluation of segmentation algorithms and several methods to do it have been proposed. This paper describes the quantitative evaluation of the performance of our segmentation algorithm using several figures of merit, exploratory and multivariate data analysis and non parametric tests, based on the assessment of the inter-observer variability of six expert imagiologists from three different hospitals and the intra-observer variability of two expert imagiologists from the same hospital. As an overall result of this comparison we were able to claim that the consistency and accuracy of our pulmonary segmentation algorithm is adequate for most of the quantitative requirements mentioned by the imagiologists. We also believe that the methodology used to evaluate the performance of our algorithm is general enough to be applicable to many other segmentation problems on medical images.
Bubble emphysema is a disease characterized by the presence of air bubbles within the lungs. With the purpose of identifying pulmonary air bubbles, two alternative methods were developed, using High Resolution Computer Tomography (HRCT) exams. The search volume is confined to the pulmonary volume through a previously developed pulmonary contour detection algorithm. The first detection method follows a slice by slice approach and uses selection criteria based on the Hounsfield levels, dimensions, shape and localization of the bubbles. Candidate regions that do not exhibit axial coherence along at least two sections are excluded. Intermediate sections are interpolated for a more realistic representation of lungs and bubbles. The second detection method, after the pulmonary volume delimitation, follows a fully 3D approach. A global threshold is applied to the entire lung volume returning candidate regions. 3D morphologic operators are used to remove spurious structures and to circumscribe the bubbles.
Bubble representation is accomplished by two alternative methods. The first generates bubble surfaces based on the voxel volumes previously detected; the second method assumes that bubbles are approximately spherical. In order to obtain better 3D representations, fits super-quadrics to bubble volume. The fitting process is based on non-linear least squares optimization method, where a super-quadric is adapted to a regular grid of points defined on each bubble.
All methods were applied to real and semi-synthetical data where artificial and randomly deformed bubbles were embedded in the interior of healthy lungs. Quantitative results regarding bubble geometric features are either similar to a priori known values used in simulation tests, or indicate clinically acceptable dimensions and locations when dealing with real data.
We've developed a Multi-slice Spiral CT Simulator modeling the acquisition process of a real tomograph over a 4-dimensional phantom (4D MCAT) of the human thorax. The simulator allows us to visually characterize artifacts due to insufficient temporal sampling and a priori evaluate the quality of the images obtained in cardio-pulmonary studies (both with single-/multi-slice and ECG gated acquisition processes). The simulating environment allows both for conventional and spiral scanning modes and includes a model of noise in the acquisition process. In case of spiral scanning, reconstruction facilities include longitudinal interpolation methods (360LI and 180LI both for single and multi-slice). Then, the reconstruction of the section is performed through FBP. The reconstructed images/volumes are affected by distortion due to insufficient temporal sampling of the moving object. The developed simulating environment allows us to investigate the nature of the distortion characterizing it qualitatively and quantitatively (using, for example, Herman's measures). Much of our work is focused on the determination of adequate temporal sampling and sinogram regularization techniques. At the moment, the simulator model is limited to the case of multi-slice tomograph, being planned as a next step of development the extension to cone beam or area detectors.
Segmentation of thoracic X-Ray Computed Tomography images is a mandatory pre-processing step in many automated or semi- automated analysis tasks such us region identification, densitometric analysis, or even for 3D visualization purposes when a stack of slices has to be prepared for surface or volume rendering. In this work, we present a fully automated and fast method for pulmonary contour extraction and region identification. Our method combines adaptive intensity discrimination, geometrical feature estimation and morphological processing resulting into a fast and flexible algorithm. A complementary but not less important objective of this work consisted on a quality assessment study of the developed contour detection technique. The automatically extracted contours were statistically compared to manually drawn pulmonary outlines provided by two radiologists. Exploratory data analysis and non-parametric statistical tests were performed on the results obtained using several figures of merit. Results indicate that, besides a strong consistence among all the quality indexes, there is a wider inter-observer variability concerning both radiologists than the variability of our algorithm when compared to each one of the radiologists. As an overall conclusion we claim that the consistence and accuracy of our detection method is more than acceptable for most of the quantitative requirements mentioned by the radiologists.
KEYWORDS: Picture Archiving and Communication System, Cardiology, Data modeling, Video, Asynchronous transfer mode, Local area networks, Visualization, Cardiac imaging, Medical imaging, Telecommunications
This paper describes an integrated system designed to provide efficient means for DICOM compliant cardiac imaging archival, transmission and visualization based on a communications backbone matching recent enabling telematic technologies like Asynchronous Transfer Mode (ATM) and switched Local Area Networks (LANs). Within a distributed client-server framework, the system was conceived on a modality based bottom-up approach, aiming ultrafast access to short term archives and seamless retrieval of cardiac video sequences throughout review stations located at the outpatient referral rooms, intensive and intermediate care units and operating theaters.
This article presents a method to combine data from two sensorial modalities, a stereoscopic vision based sensor and a ring of ultrasonic sensors, in a grid based framework for obstacle detection and avoidance with mobile robots. The sensors' data is combined using Dempster-Shafer theory. This theory allows the combination of multiple sensorial data sources in a way that they are mutually enhanced and validated. A connectionist grid is used to support these operations and the environment modeling. Each grid node maps a configuration in a discrete subset of the robot's configuration space. The process used to obtain obstacle presence information from the stereoscopic setup and ultrasonic sensors is explained in detail. Detected obstacles result in sets of restricted configurations. The grid dynamic behavior allows the iterative computation of a repulsive potential field, which rises in the vicinity of the restricted configurations. As new information is collected by the sensors during the robot's motion so new configurations are marked as restrict and the potential field changes accordingly. Since this process occurs in real time, the computed potential field can be used to navigate the robot avoiding the detected obstacles. Experimental results are presented to support the used sensor models, the integration procedure and the control strategy.
This paper describes a software package based on a set of integrated tools intended to be used in nuclear medicine imaging environments. These tools, following a functionally consistent and open architecture, aim to provide an efficient and user-friendly way for handling the analysis and interpretation of nuclear medicine images in a broad range of applications. The Image, Graphics, and Colors tools are the basic building blocks. Besides basic image handling facilities, the Image tool was designed to accomplish both conventional and special purposed processing tasks. Among these, the interactive definition of organ shaped regions of interest, functional imaging (e.g., mean transit time images in ventilatory lung studies) and activity quantitation should be pointed out as the most intensively used facilities. The Graphics tool is used mainly to display and analyze the activity/time curves resulting from parametric related studies. As intensity color coding has gained wide acceptance in nuclear medicine it was thought convenient to implement a Colors tool intended to provide interactive intensity manipulation. The X Window graphics interface system is the basis for the implementation of this set of independent but intercommunicating tools which are intended to run on all UNIX workstations provided with, at least, an 8 bit depth frame buffer.
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