Image Postprocessing in Digital Radiology—A Primer for Technologists

Seeram, Euclid; Seeram, David

doi:10.1016/j.jmir.2008.01.004

Cited by 28 publications

(17 citation statements)

References 9 publications

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“…Alguns métodos de segmentação de imagem, para seleção de regiões de interesse ou para realçar e retirar estruturas de interesse em função do tipo de órgão presente, são comumente empregados em etapas ou módulos de processamento específicos, disponibilizados pelos fabricantes nas estações de trabalho (workstations) dos equipamentos de imagem, proporcionando aos médicos recursos adicionais para a análise da imagem. Por exemplo, para a visualização apenas dos ossos ou do tecido pulmonar em imagens de CT, usualmente existem ferramentas que utilizam técnicas de limiarização (thresholding) e janelamento das imagens 48 (como no exemplo da Figura 9).…”

Section: Auxílio Computadorizado Ao Diagnósticounclassified

Processamento e Análise de Imagens Médicas

2019

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ResumoEste artigo tem por objetivo apresentar uma abordagem conceitual sobre os principais aspectos envolvidos no processamento e na análise digital de imagens médicas, trazendo exemplos da aplicação na prática clínica e da pesquisa em imagens médicas. Para explorar a temática, o artigo está dividido em seções. A primeira seção apresenta os aspectos relacionados às diferenças entre a imagem adquirida no equipamento e a visualizada nos monitores, levantando alguns elementos relacionados à qualidade da aquisição. A seguir são descritas algumas técnicas de pré-processamento que permitem melhorar e destacar aspectos relevantes das imagens. A próxima seção apresenta os principais métodos de segmentação de objetos de interesse nas imagens. A seguir, duas seções descrevem como representar e descrever de forma quantitativa as características relevantes das imagens, para que elas possam ser analisadas computacionalmente, e os aspectos relativos à análise e ao reconhecimento de padrões em imagens. Finalmente, são apresentados alguns exemplos de esquemas de auxílio computadorizado ao diagnóstico. Palavras-chave: imagens médicas; processamento de imagens; segmentação; auxílio computadorizado ao diagnóstico. AbstractThis article aims to present a conceptual approach on the main aspects involved in digital medical image processing and analysis, bringing examples of applications in clinical practice and research in medical imaging. To explore the theme, the article is divided into sections. The first section presents the aspects related to the differences between the image acquired in the equipment and viewed in displays, raising some elements related to the quality of the acquisition. Up next, some preprocessing techniques that allow to enhance and highlight relevant aspects of the images are described. The next section introduces the main methods of segmenting objects of interest in images. The following two sections describe how to represent and quantitatively describe relevant features of images so that they can be computed and the aspects of image pattern recognition and analysis. Finally, some examples of computer aided diagnostic schemes are presented.

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Section: Auxílio Computadorizado Ao Diagnósticounclassified

Processamento e Análise de Imagens Médicas

2019

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“…Other modern methods involve neural networks [11] or multi-scale image processing [12]. A summary on the basics of X-ray image processing can be found in [13] and [14].…”

Section: Current Radiograph Enhancement Techniquesmentioning

confidence: 99%

Densitometric Radiographic Imaging With Contour Sensors

2019

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We present the technical/physical foundations of a new imaging technique that combines ordinary radiographic information (generated by conventional X-ray settings) with the patient's volume to derive densitometric images. Traditionally, these images provide quantitative information about tissues densities. In our approach, they graphically enhance either soft or bony regions. After measuring the patient's volume with contour recognition devices, the physical traversed lengths within it (as the Roentgen beam intersects the patient) are calculated and pixel-wise associated with the original radiograph (X). In order to derive this map of lengths (L), the camera equations of the X-ray system and the contour sensor are determined. The patient's surface is also translated to the point-of-view of the X-ray beam and all its entrance/exit points are sought with the help of ray-casting methods. The derived L is applied to X as a physical operation (subtraction), obtaining soft tissue-(D S) or bone-enhanced (D B) figures. In the D S type, the contained graphical information can be linearly mapped to the average electronic density (traversed by the X-ray beam). This feature represents an interesting proof-of-concept of associating density data to radiographs, but most important, their intensity histogram is objectively compressed, i.e., the dynamic range is more shrunk (compared against the corresponding X). This leads to other advantages: improvement in the visibility of border/edge areas (high gradient), extended manual window level/width manipulations during screening, and immediate correction of underexposed X instances. In the D B type, high-density elements are highlighted and easier to discern. All these results can be achieved with low-energy beam exposures, saving costs and dose. Future work will deepen this clinical side of our research. In contrast with other image-based modifiers, the proposed method is grounded on the measurement of a physical entity: the span of the X-ray beam within a body while undertaking a radiographic examination. INDEX TERMS Conventional X-ray imaging, contour data, densitometric images, dynamic range, depth information.

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“…One common use of such ordinal scores is in the quality assessment of an image. Clinical image quality is measured in this way for a number of reasons, including the assessment of a change in imaging technique, [1][2][3] to compare imaging systems, 4 to assess a change in computer enhancement or processing (for instance the use of a new reconstruction algorithm in a CT scanner), 5,6 to measure image quality when optimizing the radiographic settings in radiography, 7,8 to assess the performance of a machine vision algorithm 9 or to compare methods of image quality assessment. 10 Viewing sessions are used to collect the subjective scores and usually display either one image at a time (absolute visual grading analysis) or show two images to compare (relative visual grading analysis), 2 with several observers often rating the image set.…”

Section: Introductionmentioning

confidence: 99%

Methods for the analysis of ordinal response data in medical image quality assessment

Keeble

Baxter

Gislason‐Lee

et al. 2016

BJR

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The assessment of image quality in medical imaging often requires observers to rate images for some metric or detectability task. These subjective results are used in optimization, radiation dose reduction or system comparison studies and may be compared to objective measures from a computer vision algorithm performing the same task. One popular scoring approach is to use a Likert scale, then assign consecutive numbers to the categories. The mean of these response values is then taken and used for comparison with the objective or second subjective response. Agreement is often assessed using correlation coefficients. We highlight a number of weaknesses in this common approach, including inappropriate analyses of ordinal data and the inability to properly account for correlations caused by repeated images or observers. We suggest alternative data collection and analysis techniques such as amendments to the scale and multilevel proportional odds models. We detail the suitability of each approach depending upon the data structure and demonstrate each method using a medical imaging example. Whilst others have raised some of these issues, we evaluated the entire study from data collection to analysis, suggested sources for software and further reading, and provided a checklist plus flowchart for use with any ordinal data. We hope that raised awareness of the limitations of the current approaches will encourage greater method consideration and the utilization of a more appropriate analysis. More accurate comparisons between measures in medical imaging will lead to a more robust contribution to the imaging literature and ultimately improved patient care.

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Image Postprocessing in Digital Radiology—A Primer for Technologists

Cited by 28 publications

References 9 publications

Processamento e Análise de Imagens Médicas

Processamento e Análise de Imagens Médicas

Densitometric Radiographic Imaging With Contour Sensors

Methods for the analysis of ordinal response data in medical image quality assessment

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