Ultrasound image texture characterization with Gabor wavelets for cardiac hypertrophy differentiation

Damerjian, Vera; Tankyevych, Olena; Guellich, Aziz; Damy, Thibaud; Petit, Éric

doi:10.1109/isbi.2016.7493208

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…In the context of developing quantifiable measures to compare different pathological states objectively and precisely, the textural analysis may be pertinent as documented in a review article by Somwanshi et al (2017). The utility of textural features lies in characterizing micro- and nanosurface features and distinguishing between seemingly similar surface structures (Hogeweg et al, 2015; Damerjian et al, 2016). Textural attributes of normal and abnormal scars (Liu et al, 2015), heart muscle, and atherosclerotic arteries (Mostaço-Guidolin et al, 2013) from second-harmonic generation images have been studied in computer-aided design systems for gastric diseases (Ali et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Elucidation of Differential Nano-Textural Attributes for Normal Oral Mucosa and Pre-Cancer

et al. 2019

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Computational analysis on altered micro-nano-textural attributes of the oral mucosa may provide precise diagnostic information about oral potentially malignant disorders (OPMDs) instead of an existing handful of qualitative reports. This study evaluated micro-nano-textural features of oral epithelium from scanning electron microscopic (SEM) images and the sub-epithelial connective tissue from light microscopic (LM) and atomic force microscopic (AFM) images for normal and OPMD (namely oral sub-mucous fibrosis, i.e., OSF). Objective textural descriptors, namely discrete wavelet transform, gray-level co-occurrence matrix (GLCM), and local binary pattern (LBP), were extracted and fed to standard classifiers. Best classification accuracy of 87.28 and 93.21%; sensitivity of 93 and 96%; specificity of 80 and 91% were achieved, respectively, for SEM and AFM. In the study groups, SEM analysis showed a significant (p < 0.01) variation for all the considered textural descriptors, while for AFM, a remarkable alteration (p < 0.01) was only found in GLCM and LBP. Interestingly, sub-epithelial collagen nanoscale and microscale textural information from AFM and LM images, respectively, were complementary, namely microlevel contrast was more in normal (0.251) than OSF (0.193), while nanolevel contrast was more in OSF (0.283) than normal (0.204). This work, thus, illustrated differential micro-nano-textural attributes for oral epithelium and sub-epithelium to distinguish OPMD precisely and may be contributory in early cancer diagnostics.

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