Statistical and syntactic pattern recognition

Meyer-Bäse, Anke

doi:10.1016/b978-012493290-6/50009-2

Cited by 16 publications

(32 citation statements)

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“…Many automated methods for disease detection, disease diagnosis, and materials-defect detection from radiographs have been investigated, using approaches such as statistical classifiers, fuzzy clustering, and neural networks [13][14][15][16][17][18]. We used a neural-network approach in developing algorithms to automatically predict the presence and degree of anatomy cutoff and patient motion in digital chest X-ray images.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the Variability in the Assessment of Digital Chest X-ray Image Quality

et al. 2012

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A large database of digital chest radiographs was developed over a 14-month period. Ten radiographic technologists and five radiologists independently evaluated a stratified subset of images from the database for quality deficiencies and decided whether each image should be rejected. The evaluation results showed that the radiographic technologists and radiologists agreed only moderately in their assessments. When compared against each other, radiologist and technologist reader groups were found to have even less agreement than the inter-reader agreement within each group. Radiologists were found to be more accepting of limitedquality studies than technologists. Evidence from the study suggests that the technologists weighted their reject decisions more heavily on objective technical attributes, while the radiologists weighted their decisions more heavily on diagnostic interpretability relative to the image indication. A suite of reject-detection algorithms was independently run on the images in the database. The algorithms detected 4 % of postero-anterior chest exams that were accepted by the technologist who originally captured the image but which would have been rejected by the technologist peer group. When algorithm results were made available to the technologists during the study, there was no improvement in inter-reader agreement in deciding whether to reject an image. The algorithm results do, however, provide new quality information that could be captured within a site-wide, reject-tracking database and leveraged as part of a site-wide QA program.

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Section: Introductionmentioning

confidence: 99%

Investigation of the Variability in the Assessment of Digital Chest X-ray Image Quality

et al. 2012

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“…2), in the case when considering the entire set of Laws' features, the newly resulted recognition rates overpassed the old recognition rates, in all cases. (4). We notice the presence of the CTMCM based homogeneity, energy and contrast, as well as of the third order CTMCM based correlation and contrast, denoting the heterogeneity and complex structure of the tumoral tissue, respectively differences in granularity between the tumoral tissue and the non-tumoral one (through the third order CTMCM correlation).…”

Section: A the Role Of The Ctmcm Matrix In The Recognition Of The Pamentioning

confidence: 99%

“…In this situation, we took into account all the Laws' convolution filters (at step (1)). Besides the Haralick features derived from the CTMCM matrices, we also considered, in our experiments, the following textural features: the Haralick parameters of the second and third order GLCM, respectively of the second and third order EOCM, edge and gradient based features, the autocorrelation index, fractal-based textural features (the Hurst index), the frequency of the textural microstructures, the entropy determined at two resolution levels, after applying the Wavelet transform [4].…”

Section: A the Newly Defined Texture Analysis Methodsmentioning

confidence: 99%

“…{GLCM_variance, Autocorrelation_index, Directional_grad_magnitude, Directional_grad_variance, GLCM5_Entropy, GLCM5_Variance, EOCM3_ Homogeneity, EOCM3_Energy, EOCM3_Entropy, EOCM3_Contrast, EOCM3_Variance, GLCM3_Homogeneity, GLCM3_Entropy, GLCM3_Correlation, CTMCM_Homogeneity, CTMCM_Energy, CTMCM_Contrast, CTMCM3_Correlation, CTMCM3_Contrast} (4) The values of the classification performance parameters obtained in this case are depicted in Table II. As it results from Table II, the maximum recognition rate obtained in this situation, of 97.5%, corresponded to the RF classifier, being slightly lower than the maximum accuracy obtained in the previous case.…”

Section: A the Role Of The Ctmcm Matrix In The Recognition Of The Pamentioning

confidence: 99%

“…Texture is an important property of the body tissues, able to provide subtle information concerning both the tumor structure and the gingival sulcus aspect within ultrasound images [4]. Texture-based methods, in combination with classifiers, were widely used in the scientific research nowadays [5], [6], [7], [8], [9].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The role of the complex textural microstructure co-occurrence matrices, based on Laws’ features, in the characterization and recognition of some pathological structures, from ultrasound images

Mitrea

Nedevschi

Abrudean

et al. 2016

International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems

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Abstract-The non-invasive diagnosis, based on ultrasound images, is a challenge in nowadays research. We develop computerized, texture-based methods, for automatic and computer assisted diagnosis, using the information obtained from ultrasound images. In this work, we defined the cooccurrence matrix of complex textural microstructures determined by using the Laws' convolution filters and we experimented it in order to perform the characterization and recognition of some important anatomical and pathological structures, within ultrasound images. These structures were the colorectal tumors and the gingival sulcus, the properties of the latter being important concerning the diagnosis and monitoring of the periodontal disease. We determined the textural model of these structures, using the classical and the newly defined textural features. For the automatic recognition, we used powerful classifiers, such as the Multilayer Perceptron, the Support-Vector Machines, decision-trees based classifiers such as Random Forest and C4.5, respectively AdaBoost in combination with the C4.5 algorithm.

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Colorectal cancer recognition from ultrasound images, using complex textural microstructure cooccurrence matrices, based on Laws' features

Mitrea

Nedevschi

Abrudean

et al. 2015

2015 38th International Conference on Telecommunications and Signal Processing (TSP)

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The colorectal cancer is a frequent lethal disease nowadays. The most reliable method for diagnosis, the biopsy, is invasive and dangerous. We developed computerized, texturebased methods, for non-invasive cancer diagnosis, using the information obtained from ultrasound images. In this work, we defined the co-occurrence matrix of complex textural microstructures, determined by using the Laws' convolution filters, in conjunction with clustering methods and we experimented it in order to perform automatic diagnosis of the colorectal tumors. These tumors were compared with the Inflammatory Bowel Diseases (IBD), as they have the same appearance with these affections in ultrasound images. We also determined the relevant textural features that characterize the colorectal tumors, by using specific methods. For the automatic recognition, we used powerful classifiers, such as the Multilayer Perceptron (MLP), the Support-Vector Machines (SVM), decision-trees based classifiers such as Random Forest (RF) and C4.5, respectively AdaBoost in combination with the C4.5 algorithm.

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Statistical and syntactic pattern recognition

Cited by 16 publications

References 0 publications

Investigation of the Variability in the Assessment of Digital Chest X-ray Image Quality

Investigation of the Variability in the Assessment of Digital Chest X-ray Image Quality

The role of the complex textural microstructure co-occurrence matrices, based on Laws’ features, in the characterization and recognition of some pathological structures, from ultrasound images

Colorectal cancer recognition from ultrasound images, using complex textural microstructure cooccurrence matrices, based on Laws' features

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