Diagnóstico auxiliado por computador na radiologia

Azevedo-Marques, Paulo Mazzoncini de

doi:10.1590/s0100-39842001000500008

Cited by 28 publications

(53 citation statements)

References 23 publications

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“…Image Analysis (IA) is a recent tool used in many scientific areas, such as food quality (Li et al 1999, Lu et al 2000, Louka et al 2004, Tan 2004, Faucitano et al 2005, Fernández et al 2005, Vestergaard et al 2005, Yang et al 2005, agriculture (Santos et al 1998, Moreira et al 2004, Throop et al 2005 and as an aid in disease diagnosis (Novelli et al 1997, Azevedo-Marques 2001, Babinski et al 2002, Rosito 2002, Tang et al 2003. The ANN has been widely used in many scientific areas but presently is under-utilized in the poultry industry and has been scarcely reported in the literature.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of follicular lymphoid depletion in the Bursa of Fabricius: an alternative methodology using digital image analysis and artificial neural networks

et al. 2010

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(BF) were examined by conventional optical microscopy and digital images were acquired and processed using Matlab ® 6.5 software. The Artificial Neuronal Network (ANN) was generated using Neuroshell ® Classifier software and the optical and digital data were compared. The ANN was able to make a comparable classification of digital and optical scores. The use of ANN was able to classify correctly the majority of the follicles, reaching sensibility and specificity of 89% and 96%, respectively. When the follicles were scored and grouped in a binary fashion the sensibility increased to 90% and obtained the maximum value for the specificity of 92%. These results demonstrate that the use of digital image analysis and ANN is a useful tool for the pathological classification of the BF lymphoid depletion. In addition it provides objective results that allow measuring the dimension of the error in the diagnosis and classification therefore making comparison between databases feasible.

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Section: Introductionmentioning

confidence: 99%

Evaluation of follicular lymphoid depletion in the Bursa of Fabricius: an alternative methodology using digital image analysis and artificial neural networks

et al. 2010

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“…The increasing use of computer-aided diagnosis (CAD) applications is related to the rapid development of medical algorithms. The CAD objective is to improve the diagnostic accuracy, as well as the consistency in diagnostic images interpretation by means of a diagnostic response suggested by a computer (3) . However, some recognized CAD applications have not been utilized in the clinical routine yet because of their high computational cost, limiting their utilization to those centers that have high capacity computers (4) .…”

Section: Introductionmentioning

confidence: 99%

Grades computacionais na recuperação de imagens médicas baseada em conteúdo

2007

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OBJECTIVE: To utilize the grid computing technology to enable the utilization of a similarity measurement algorithm for content-based medical image retrieval. MATERIALS AND METHODS: The content-based images retrieval technique is comprised of two sequential steps: texture analysis and similarity measurement algorithm. These steps have been adopted for head and knee images for evaluation of accuracy in the retrieval of images of a single plane and acquisition sequence in a databank with 2,400 medical images. Initially, texture analysis was utilized as a pre-selection resource to obtain a set of the 1,000 most similar images as compared with a reference image selected by a clinician. Then, these 1,000 images were processed utilizing a similarity measurement algorithm on a computational grid. RESULTS: The texture analysis has demonstrated low accuracy for sagittal knee images (0.54) and axial head images (0.40). Nevertheless, this technique has shown effectiveness as a filter, pre-selecting images to be evaluated by the similarity measurement algorithm. Content-based images retrieval with similarity measurement algorithm applied on these pre-selected images has demonstrated satisfactory accuracy -0.95 for sagittal knee images, and 0.92 for axial head images. The high computational cost of the similarity measurement algorithm was balanced by the utilization of grid computing. CONCLUSION: The approach combining texture analysis and similarity measurement algorithm for content-based images retrieval resulted in an accuracy of > 90%. Grid computing has shown to be essential for the utilization of similarity measurement algorithm in the content-based images retrieval that otherwise would be limited to supercomputers.

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“…The high false-positive rate (14) and false-negative rate (12) can be explained by the limited number of images used for the network training (n = 68), even though some studies present higher accuracy with the same number of images (13) . PPV and NPV were 62.16% and 63.63%, respectively, with values lower than those of literature, which resulted in an average of 90% in these calculations (26) .…”

Section: Discussionmentioning

confidence: 99%

“…Studies suggest further research on treatment, which currently only offers relief of pain and improvement of joint function, but does not enable the pathological process to be reversed (6)(7)(8) . Therefore, by seeking alternative options for aiding the diagnosis, medical informatics aims to assist doctors in routine diagnoses, and has proven increasingly effective, helping in some clinical tasks, such as warning signs, therapeutic judgments, agents for information retrieval, and image recognition and interpretation, among other models (9)(10)(11)(12) . Among the areas of application of medical informatics, artificial intelligence (AI) uses artificial neural networks (ANN) to search for a computational model based on biological neurons which, by means of mathematical models, generates an artificial neuron, and through interconnections of various artificial neurons, generates an ANN.…”

Section: Introductionmentioning

confidence: 99%