Identification of masses in digital mammogram using gray level co-occurrence matrices

Khuzi, Azlindawaty Mohd; Besar, Rosli; Zaki, Wan Mimi Diyana Wan; Ahmad, Nazihah

doi:10.2349/biij.5.3.e17

Cited by 61 publications

(16 citation statements)

References 16 publications

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“…Following this two-step approach, we estimated a total of 28 texture descriptors, including structural, 34,35 co-occurrence, 36 gray-level histogram, 37 and runlength features, 38,39 which have all been previously established for mammographic pattern analysis and breast cancer risk assessment. 7,14,15,23,35,40,41 In Fig. 2(b), we show representative texture maps for each feature category, while detailed descriptions and mathematical notations of the features are available in Appendix A.…”

Section: B Automated Estimation Of Image-derived Quantitative Descmentioning

confidence: 99%

Breast parenchymal patterns in processed versus raw digital mammograms: A large population study toward assessing differences in quantitative measures across image representations

et al. 2016

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Purpose: With raw digital mammograms (DMs), which retain the relationship with x-ray attenuation of the breast tissue, not being routinely available, processed DMs are often the only viable means to acquire imaging measures. The authors investigate differences in quantitative measures of breast density and parenchymal texture, shown to have value in breast cancer risk assessment, between the two DM representations. Methods: The authors report data from 8458 pairs of bilateral raw ("FOR PROCESSING") and processed ("FOR PRESENTATION") DMs acquired from 4278 women undergoing routine screening evaluation, collected with DM units from two different vendors. Breast dense tissue area and percent density (PD), as well as a range of quantitative descriptors of breast parenchymal texture (statistical, co-occurrence, run-length, and structural descriptors), were measured using previously validated, fully automated software. Feature measurements were compared using matched-pairs Wilcoxon signed-ranks test, correlation (r), and linear-mixed-effects (LME) models, where potential interactions with woman-and system-specific factors were also assessed. The authors also compared texture feature correlations with the established risk factors of the Gail lifetime risk score (r G ) and breast PD (r PD ), and evaluated the within woman intraclass feature correlation (ICC), a measure of bilateral breast-tissue symmetry, in raw versus processed images. Results: All density measures and most of the texture features were strongly (r ≥ 0.6) or moderately (0.4 ≤ r < 0.6) correlated between raw and processed images. However, measurements were significantly different between the two imaging formats (Wilcoxon signed-ranks test, p w < 0.05). The association between measurements varied across features and vendors, and was substantially modified by woman-and system-specific image acquisition factors, such as age, BMI, and mAs/kVp, respectively. The strongest correlation, combined with minimal LME-model interactions, was observed for structural texture features. Overall, texture measures from either image representation were weakly associated with Gail lifetime risk (−0.2 ≤ r G ≤ 0.2), weakly to moderately associated with breast PD (−0.6 ≤ r PD ≤ 0.6), and had overall strong bilateral symmetry (ICC ≥ 0.6). Conclusions: Differences in measures from processed versus raw DM depend highly on the feature, the DM vendor, and image acquisition settings, where structural features appear to be more robust across the different DM settings. The reported findings may serve as a reference in the design of future large-scale studies on mammographic features and breast cancer risk assessment involving multiple DM representations. C

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Section: B Automated Estimation Of Image-derived Quantitative Descmentioning

confidence: 99%

Breast parenchymal patterns in processed versus raw digital mammograms: A large population study toward assessing differences in quantitative measures across image representations

et al. 2016

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“…In previous studies using co-occurrence features, the offset length was generally chosen heuristically. For example, in mammographic imaging research, l is often chosen between 1 and 10 pixels with 1 being the most common value, such as in the work by Khuzi et al 30 for mass identification and Li et al 7 for texture analysis of mammographic parenchymal patterns. Our results suggest that if the offset length is gradually increased to 7 pixels or larger, the majority of inherent system effects can be alleviated.…”

Section: Discussionmentioning

confidence: 99%

“…Features are typically calculated using the same offset length l along four directions (0, 45, 90, and 135 deg) and then averaged, based on the assumptions that these features are orientation invariant 39,40 and that one single direction might not give sufficient texture information. 30 With respect to the offset length l, in several studies, it is chosen by default to be equal to 1 pixel, however, this is not always justified based on its actual physical dimensions.…”

Section: Parameters In Texture Feature Generationmentioning

confidence: 99%

Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices

et al. 2015

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Abstract. An analytical framework is presented for evaluating the equivalence of parenchymal texture features across different full-field digital mammography (FFDM) systems using a physical breast phantom. Phantom images (FOR PROCESSING) are acquired from three FFDM systems using their automated exposure control setting. A panel of texture features, including gray-level histogram, co-occurrence, run length, and structural descriptors, are extracted. To identify features that are robust across imaging systems, a series of equivalence tests are performed on the feature distributions, in which the extent of their intersystem variation is compared to their intrasystem variation via the Hodges-Lehmann test statistic. Overall, histogram and structural features tend to be most robust across all systems, and certain features, such as edge enhancement, tend to be more robust to intergenerational differences between detectors of a single vendor than to intervendor differences. Texture features extracted from larger regions of interest (i.e., >63 pixels 2 ) and with a larger offset length (i.e., >7 pixels), when applicable, also appear to be more robust across imaging systems. This framework and observations from our experiments may benefit applications utilizing mammographic texture analysis on images acquired in multivendor settings, such as in multicenter studies of computer-aided detection and breast cancer risk assessment.

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“…The texture features are extracted using grey-level co-occurrence matrices (GLCM) [19]. The matrices are constructed at a distance d = 1 and for directions of 9 given as 0°, 45°, 90° and 135°.…”

Section: Computing Grey-level Co-occurrence Texture Featuresmentioning

confidence: 99%

A region-based segmentation of tumour from brain CT images using nonlinear support vector machine classifier

Nanthagopal

Rajamony²

2012

Journal of Medical Engineering & Technology

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The proposed system provides new textural information for segmenting tumours, efficiently and accurately and with less computational time, from benign and malignant tumour images, especially in smaller dimensions of tumour regions of computed tomography (CT) images. Region-based segmentation of tumour from brain CT image data is an important but time-consuming task performed manually by medical experts. The objective of this work is to segment brain tumour from CT images using combined grey and texture features with new edge features and nonlinear support vector machine (SVM) classifier. The selected optimal features are used to model and train the nonlinear SVM classifier to segment the tumour from computed tomography images and the segmentation accuracies are evaluated for each slice of the tumour image. The method is applied on real data of 80 benign, malignant tumour images. The results are compared with the radiologist labelled ground truth. Quantitative analysis between ground truth and the segmented tumour is presented in terms of segmentation accuracy and the overlap similarity measure dice metric. From the analysis and performance measures such as segmentation accuracy and dice metric, it is inferred that better segmentation accuracy and higher dice metric are achieved with the normalized cut segmentation method than with the fuzzy c-means clustering method.

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Identification of masses in digital mammogram using gray level co-occurrence matrices

Cited by 61 publications

References 16 publications

Breast parenchymal patterns in processed versus raw digital mammograms: A large population study toward assessing differences in quantitative measures across image representations

Breast parenchymal patterns in processed versus raw digital mammograms: A large population study toward assessing differences in quantitative measures across image representations

Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices

A region-based segmentation of tumour from brain CT images using nonlinear support vector machine classifier

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