Improved Spatial Gray Level Dependence Matrices for Texture Analysis

Sassi, O.

doi:10.5121/ijcsit.2012.4615

Cited by 15 publications

(12 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Textures represent the spatial distribution of intensity in terms of pixels’ gray levels in a region; and hence a change in the spatial distribution of intensity directly reflects the change in texture of a sample. Several statistical approaches have been proposed till date to define textural parameters 29,30,36 . In this work, Gray-level co-occurrence matrix (GLCM), also known as spatial gray-level dependence matrix (SGLDM), was used to obtain Haralick’s textural features 30 .…”

Section: Methodsmentioning

confidence: 99%

“…3D cross-sectional images and the corresponding attenuation coefficient (of light) showed remarkable microstructural changes among these different colored leaves. Further, to quantify these changes, we have applied textural analysis algorithms (Spatial-gray level dependent matrix; SGLDM) to extract fourteen quantitative parameters to distinguish senescing leaves from non-senescing 29,30 . Our results further show lower total chlorophyll content in the senescing leaves.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Elucidation of microstructural changes in leaves during senescence using spectral domain optical coherence tomography

Anna¹,

Chakraborty

Cheng³

et al. 2019

Sci Rep

View full text Add to dashboard Cite

Leaf senescence provides a unique window to explore the age-dependent programmed degradation at organ label in plants. Here, spectral domain optical coherence tomography (SD-OCT) has been used to study in vivo senescing leaf microstructural changes in the deciduous plant Acer serrulatum Hayata. Hayata leaves show autumn phenology and change color from green to yellow and finally red. SD-OCT image analysis shows distinctive features among different layers of the leaves; merging of upper epidermis and palisade layers form thicker layers in red leaves compared to green leaves. Moreover, A-scan analysis showed a significant (p < 0.001) decrease in the attenuation coefficient (for wavelength range: 1100–1550 nm) from green to red leaves. In addition, the B-scan analysis also showed significant changes in 14 texture parameters extracted from second-order spatial gray level dependence matrix (SGLDM). Among these parameters, a set of three features (energy, skewness, and sum variance), capable of quantitatively distinguishing difference in the microstructures of three different colored leaves, has been identified. Furthermore, classification based on k-nearest neighbors algorithm (k-NN) was found to yield 98% sensitivity, 99% specificity, and 95.5% accuracy. Following the proposed technique, a portable noninvasive tool for quality control in crop management can be anticipated.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Elucidation of microstructural changes in leaves during senescence using spectral domain optical coherence tomography

Anna¹,

Chakraborty

Cheng³

et al. 2019

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Both k-means clustering and active contour models ("snakes") have been implemented in other medical image segmentation applications [23][24][25]. However, these techniques have not been used for segmentation tasks involving periodontal high-frequency ultrasound images.…”

Section: Discussionmentioning

confidence: 99%

Computer-assisted identification of the gingival sulcus and periodontal epithelial junction on high-frequency ultrasound images

et al. 2015

View full text Add to dashboard Cite

The primary aim of this study was to demonstrate that periodontal ultrasonography is a reliable method with which to identify and evaluate the attachment level of the gingival junctional epithelium. A secondary aim was to devise an automated computer-assisted method that allows the examiner to more easily identify the gingival sulcus contour on ultrasound images. Material and methods: This in vitro study was carried out on 36 sites on the lingual surface of eight pig mandibles. For each site, periodontal ultrasonography was performed by the same examiner, using DermaScan C Cortex Technology (Denmark) with a 20-MHz transducer. Subsequently, the mandibles were sectioned with a microtome and examined by direct microscopy. To facilitate identification of the gingival sulcus on ultrasound images, a computational method was adopted. Results: Computer processing of the ultrasound images slightly modified the contour of the gingival sulcus. The absolute mean differences in the linear measurements of the Dermascan-automated computer-generated values and the corresponding values of microscopy, which is the gold standard, varied between 0.06 and 1.75 mm. Statistical analysis showed that with respect to the gingival sulcus height, the correlation between the computer-processed ultrasound images and the direct microscopy images was stronger than the correlation between the non-processed ultrasound images and those from direct microscopy. Conclusions: Ultrasonographic examination of the periodontal tissues allows the examiner to localize the gingival epithelial attachment level and provides substantial data regarding the soft gingival tissues.

show abstract

“…So, the textural analysis should be perfect-invariant. An Upgraded SGLDM method [1] is suggested in this paper for whole image segmentation of the CRC. This paper is organized as I.…”

Section: Introductionmentioning

confidence: 99%

Upgraded Spatial Gray Level Dependence Matrices for Textural Analysis in Colon Cancer Tissues

Saroja¹,

Priyadharson²

2018

IJET

View full text Add to dashboard Cite

Colon or Bowel or Colorectal Cancer (CRC) is commonly determined by diagnosing a sample of colon tissue and further analysed by medical imaging. The colon tissue classification method count on specific changes between texture features extracted from benign and malignant regions. The variations in the image acquisition methods effects the colon tissue analysis. In this paper, an Upgraded Spatial Gray Level Dependence Matrices (U-SGLDM) is emphasized to extract textural features. The licensed image set of all applicable types of tissues within colon cancer are used for experimentation. Several texture feature sets are extracted to show the significant differences among the eight colon cancer biopsy images in the image data set. The fractal dimension-Hurst Coefficient is added to U-SGLDM for long range assessment. The Prominence of the analysis evoked in the representation of histopathological image structure over longer periods.

show abstract

Improved Spatial Gray Level Dependence Matrices for Texture Analysis

Cited by 15 publications

References 18 publications

Elucidation of microstructural changes in leaves during senescence using spectral domain optical coherence tomography

Elucidation of microstructural changes in leaves during senescence using spectral domain optical coherence tomography

Computer-assisted identification of the gingival sulcus and periodontal epithelial junction on high-frequency ultrasound images

Upgraded Spatial Gray Level Dependence Matrices for Textural Analysis in Colon Cancer Tissues

Contact Info

Product

Resources

About