Berthold B. Wein

Content-based image retrieval in medical applications: a novel multistep approach

In the past few years, immense improvement was obtained in the field of content-based image retrieval. Nevertheless, existing systems still fail when applied to medical image databases. Simple feature-extraction algorithms that operate on the entire image for characterization of color, texture, or shape cannot be related to the descriptive semantics of medical knowledge that is extracted from images by human experts.

Statistical framework for model-based image retrieval in medical applications

Recently, research in the field of content-based image retrieval has attracted a lot of attention. Nevertheless, most existing methods cannot be easily applied to medical image databases, as global image descriptions based on color, texture, or shape do not supply sufficient semantics for medical applications. The concept for content-based image retrieval in medical applications (IRMA) is therefore based on the separation of the following processing steps: categorization of the entire image; registration with respect to proto- types; extraction and query-dependent selection of local features; hierarchical blob representation including object identification; and finally, image retrieval. Within the first step of processing, images are classified according to image modality, body orientation, ana- tomic region, and biological system. The statistical classifier for the anatomic region is based on Gaussian kernel densities within a probabilistic framework for multiobject recognition. Special empha- sis is placed on invariance, employing a probabilistic model of vari- ability based on tangent distance and an image distortion model. The performance of the classifier is evaluated using a set of 1617 radiographs from daily routine, where the error rate of 8.0% in this six-class problem is an excellent result, taking into account the dif- ficulty of the task. The computed posterior probabilities are further- more used in the subsequent steps of the retrieval process.

Content-based image retrieval in medical applications for picture archiving and communication systems

Picture archiving and communication systems (PACS) aim to efficiently provide the radiologists with all images in a suitable quality for diagnosis. Modern standards for digital imaging and communication in medicine (DICOM) comprise alphanumerical descriptions of study, patient, and technical parameters. Currently, this is the only information used to select relevant images within PACS. Since textual descriptions insufficiently describe the great variety of details in medical images, content-based image retrieval (CBIR) is expected to have a strong impact when integrated into PACS. However, existing CBIR approaches usually are limited to a distinct modality, organ, or diagnostic study. In this state-of-the-art report, we present first results implementing a general approach to content-based image retrieval in medical applications (IRMA) and discuss its integration into PACS environments. Usually, a PACS consists of a DICOM image server and several DICOM-compliant workstations, which are used by radiologists for reading the images and reporting the findings. Basic IRMA components are the relational database, the scheduler, and the web server, which all may be installed on the DICOM image server, and the IRMA daemons running on distributed machines, e.g., the radiologists’ workstations. These workstations can also host the web-based front-ends of IRMA applications. Integrating CBIR and PACS, a special focus is put on (a) location and access transparency for data, methods, and experiments, (b) replication transparency for methods in development, (c) concurrency transparency for job processing and feature extraction, (d) system transparency at method implementation time, and (e) job distribution transparency when issuing a query. Transparent integration will have a certain impact on diagnostic quality supporting both evidence-based medicine and case-based reasoning.

Extended Query Refinement for Medical Image Retrieval

The impact of image pattern recognition on accessing large databases of medical images has recently been explored, and content-based image retrieval (CBIR) in medical applications (IRMA) is researched. At the present, however, the impact of image retrieval on diagnosis is limited, and practical applications are scarce. One reason is the lack of suitable mechanisms for query refinement, in particular, the ability to (1) restore previous session states, (2) combine individual queries by Boolean operators, and (3) provide continuous-valued query refinement. This paper presents a powerful user interface for CBIR that provides all three mechanisms for extended query refinement. The various mechanisms of man–machine interaction during a retrieval session are grouped into four classes: (1) output modules, (2) parameter modules, (3) transaction modules, and (4) process modules, all of which are controlled by a detailed query logging. The query logging is linked to a relational database. Nested loops for interaction provide a maximum of flexibility within a minimum of complexity, as the entire data flow is still controlled within a single Web page. Our approach is implemented to support various modalities, orientations, and body regions using global features that model gray scale, texture, structure, and global shape characteristics. The resulting extended query refinement has a significant impact for medical CBIR applications.

Recanalization of Thrombosed Dialysis Access with Use of a Rotating Mini-Pigtail Catheter: Follow-up Study

PURPOSE To evaluate the feasibility, efficacy, and safety of mechanical thrombectomy of occluded dialysis access with use of a rotating mini-pigtail catheter. MATERIALS AND METHODS Thrombus was fragmented by mechanical action of the rotating pigtail tip (5-mm diameter), while the guide wire exited a sidehole at the pigtail curvature and served as a fixed rotation axis. Twenty-six procedures were performed in 22 patients (12 men, 10 women; mean age, 55.5 years). Native fistulas were treated in 15 instances, polytetrafluoroethylene (PTFE) grafts were treated in 11 instances. Average occlusion time was 20 hours +/- 13 (range, 5-46 hours), average occlusion length was 25.6 cm +/- 10.1 (range, 6-45 cm). Thrombus fragmentation was followed by balloon angioplasty of underlying stenoses. RESULTS In all 26 procedures, the dialysis access was successfully declotted with subsequent dialysis using the access (clinical success rate, 100%). Handling of the mini-pigtail catheter was simple and rapid, regardless of whether a graft or a native fistula was treated. Average duration of the intervention was 118 minutes +/- 30. Mean primary patency was 165 days +/- 167. Primary patency rate was 82% at 30 days, 65% at 3 months, and 47% at 6 months. There was no evidence of complications due to the thrombus fragmentation procedure. CONCLUSION The results suggest that declotting of occluded dialysis grafts and fistulas with the mini-pigtail catheter is as effective and safe as other more established percutaneous therapies. It may serve as an easy-to-handle, low-budget alternative to current thrombectomy devices.

Quality of DICOM header information for image categorization

The widely used DICOM 3.0 imaging protocol specifies optional tags to store specific information on modality and body region within the header: Body Part Examined and Anatomic Structure. We investigate whether this information can be used for the automated categorization of medical images, as this is an important first step for medical image retrieval. Our survey examines the headers generated by four digital image modalities (2 CTs, 2 MRIs) in clinical routine at the Aachen University Hospital within a period of four months. The manufacturing dates of the modalities range from 1995 to 1999, with software revisions from 1999 and 2000. Only one modality sets the DICOM tag Body Part Examined. 90 out of 580 images (15.5%) contained false tag entries causing a wrong categorization. This result was verified during a second evaluation period of one month one year later (562 images, 15.3% error rate). The main reason is the dependency of the tag on the examination protocol of the modality, which controls all relevant parameters of the imaging process. In routine, the clinical personnel often applies an examination protocol outside its normal context to improve the imaging quality. This is, however, done without manually adjusting the categorization specific tag values. The values specified by DICOM for the tag Body Part Examined are insufficient to encode the anatomic region precisely. Thus, an automated categorization relying on DICOM tags alone is impossible.

Determining the View of Chest Radiographs

Automatic identification of frontal (posteroanterior/anteroposterior) vs. lateral chest radiographs is an important preprocessing step in computer-assisted diagnosis, content-based image retrieval, as well as picture archiving and communication systems. Here, a new approach is presented. After the radiographs are reduced substantially in size, several distance measures are applied for nearest-neighbor classification. Leaving-one-out experiments were performed based on 1,867 radiographs from clinical routine. For comparison to existing approaches, subsets of 430 and 5 training images are also considered. The overall best correctness of 99.7% is obtained for feature images of 32 × 32 pixels, the tangent distance, and a 5-nearest-neighbor classification scheme. Applying the normalized cross correlation function, correctness yields still 99.6% and 99.3% for feature images of 32 × 32 and 8 × 8 pixel, respectively. Remaining errors are caused by image altering pathologies, metal artifacts, or other interferences with routine conditions. The proposed algorithm outperforms existing but sophisticated approaches and is easily implemented at the same time.

Detection and compensation of rib structures in chest radiographs for diagnostic assistance

We developed a new method to compensate the rib structures in digital x-ray images. The intrinsic information of rib structures is eliminated and a higher image quality for the diagnosis of pulmonal structures is achieved. An essential task of the algorithm is the robust detection of the rib borders. In this paper we introduce three algorithms to perform this task. The first, introduced by Schreckenberg and Joswig, uses the hough transform to find rib borders, the second one uses a synergetic classifier to estimate the matching between rib edge templates and rib borders. The last one, the sinking lead algorithm, gives the best classification results by performing a matched template technique in combination with partial methods from the former two algorithms.

Classification of Medical Images Using Local Representations

In medical image retrieval, the images are usually subject to a large range of variability. In order to classify medical images, we therefore propose the use of local representations, which are small square windows taken from the images. This approach is combined with a fast approximate k-nearest neighbor technique and yields state-of-the-art results on a medical image database of 1617 images.

Comparative evaluation of digital radiography versus conventional radiography of fractured skulls.

OBJECTIVES.The authors assessed the relative efficacy of conventional and digital storage-phosphor radiographs for the detection of skull fractures. METHODS.Fifty conventional film-screen radiographs (FSR) and 50 digital storage-phosphor radiographs (DR) with 66 fractures were compared. Five radiologists evaluated image quality and fracture detectability. The results were analyzed by receiver operating characteristic (ROC) curve analysis. RESULTS.With a standard exposure, the ability to evaluate skull fractures was equally good with either technique (ROC area for DR, 0.8954; for FSR, 0.8870). Digital radiography was superior in evaluating nasal bone. For petrosal bone, the DR image simulates an underexposure. This disadvantage compared with FSR can be compensated by image postprocessing. CONCLUSION.In evaluation of skull fractures, radiologists performance with DR is equivalent to FSR.