Automated Computer Analysis of Radiographic Images

Meyers, Phillip H.; Nice, Charles M.; Becker, H; Nettleton, Wilson J.; Sweeney, James W.; Meckstroth, George R.

doi:10.1148/83.6.1029

Cited by 78 publications

(22 citation statements)

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“…the average grey-level of its rows, per column, is estimated. Radiographic image profiles have been proved valuable in several radiographic image analysis tasks [1], [4], [6], [7], [10] as they provide a simple though effective means to navigating through a radiograph by using only one-dimensional information and elementary signal processing techniques. The proposed methodology utilizes horizontal profiles of subsequent image samples in order to obtain spatial instances of the radiograph from which the spinal cord and the ribcage boundaries are detectable even if the patient is bent.…”

Section: ) Spinal Cord Detection and Feature Points Of The Outer Lefmentioning

confidence: 99%

“…The first approaches to lung field detection begun to appear in the early sixties [1]. Since then, a variety of approaches have been proposed for automatic detection of the lung fields and approximation of their boundaries.…”

Section: Introductionmentioning

confidence: 99%

“…Section III presents the results of its experimental evaluation on a variety of radiographs from various sources, and the last section summarizes the conclusions of this study. consolidations, since their density, and therefore their greylevel intensity, is generally lower than the density of the spinal cord and of the ribcage [1], [20].…”

Section: Introductionmentioning

confidence: 99%

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Versatile approximation of the lung field boundaries in chest radiographs in the presence of bacterial pulmonary infections

Iakovidis

2008

2008 8th IEEE International Conference on BioInformatics and BioEngineering

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The most common radiographic manifestation of bacterial pulmonary infections are foci of consolidation which are visible as bright shadows interfering with the interior lung intensity. In critically-ill patients this interference can be severe leading to vague or invisible lung field boundaries which are difficultly distinguished even by experienced physicians. This problem is amplified if the radiographs are of low quality as obtained with a portable x-ray device, routinely used in intensive care units. This paper proposes a pioneering methodology that copes with lung field detection in both stationary and portable chest radiographs by combining statistical grey-level intensity information and directional edge maps. The boundaries of the lung fields are approximated by consecutive intuitively manipulated parametric curves. Conventional and state of the art lung field detection approaches address only stationary radiography, and only a few of them cope with pulmonary infections. The proposed methodology features unsupervised operation, it is not iterative, it is not limited by the patients' positioning, and it is tolerant to the presence of consolidations and boundary discontinuities of the lung fields. Its performance is validated on various stationary radiographs and on a set of portable radiographs obtained from patients with bacterial pulmonary infections.

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Section: ) Spinal Cord Detection and Feature Points Of The Outer Lefmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Versatile approximation of the lung field boundaries in chest radiographs in the presence of bacterial pulmonary infections

Iakovidis

2008

2008 8th IEEE International Conference on BioInformatics and BioEngineering

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“…CAD was also proposed in several fields of medicine with non-visual systems such as QMR 1 (quick medical reference) or ILIAD 2 [12,19,23,39]. Early studies on computerized analysis of medical images (mostly in X-ray imaging) were reported in the 1960s [18,22]. These systems aimed at replacing the radiologists as the originators relied on the assumption that computers are better at performing certain tasks than are human beings.…”

Section: Introductionmentioning

confidence: 99%

Case-based lung image categorization and retrieval for interstitial lung diseases: clinical workflows

et al. 2011

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“…The initial attempts to CADx were carried out more than forty years ago in chest X-ray imaging [23,24]. These systems aimed at replacing radiologists as the originators relied on the assumption that computers were better at performing certain tasks than human beings.…”

Section: Introductionmentioning

confidence: 99%

Prototypes for Content-Based Image Retrieval in Clinical Practice

Depeursinge¹,

Fischer²,

Müller³

et al. 2011

TOMINFOJ

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Content-based image retrieval (CBIR) has been proposed as key technology for computer-aided diagnostics (CAD). This paper reviews the state of the art and future challenges in CBIR for CAD applied to clinical practice.We define applicability to clinical practice by having recently demonstrated the CBIR system on one of the CAD demonstration workshops held at international conferences, such as SPIE Medical Imaging, CARS, SIIM, RSNA, and IEEE ISBI. From 2009 to 2011, the programs of CADdemo@CARS and the CAD Demonstration Workshop at SPIE Medical Imaging were sought for the key word “retrieval” in the title. The systems identified were analyzed and compared according to the hierarchy of gaps for CBIR systems.In total, 70 software demonstrations were analyzed. 5 systems were identified meeting the criterions. The fields of application are (i) bone age assessment, (ii) bone fractures, (iii) interstitial lung diseases, and (iv) mammography. Bridging the particular gaps of semantics, feature extraction, feature structure, and evaluation have been addressed most frequently.In specific application domains, CBIR technology is available for clinical practice. While system development has mainly focused on bridging content and feature gaps, performance and usability have become increasingly important. The evaluation must be based on a larger set of reference data, and workflow integration must be achieved before CBIR-CAD is really established in clinical practice.

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Automated Computer Analysis of Radiographic Images

Cited by 78 publications

References 0 publications

Versatile approximation of the lung field boundaries in chest radiographs in the presence of bacterial pulmonary infections

Versatile approximation of the lung field boundaries in chest radiographs in the presence of bacterial pulmonary infections

Case-based lung image categorization and retrieval for interstitial lung diseases: clinical workflows

Prototypes for Content-Based Image Retrieval in Clinical Practice

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