The Geometric Local Textural Patterns (GLTP)

Vargas, Sergio Alejandro Orjuela; Puentes, John; Philips, Wilfried

doi:10.1007/978-3-642-39289-4_4

Cited by 4 publications

(2 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other than our proposed textural model, we evaluated the robustness and efficiency of our HPIL model in comparison with other existing texture descriptors using our VELscope ® database, such as gradient directional pattern (GDP) [ 60 ], gradient directional pattern 2 (GDP2), geometric local textural patterns (GLTP) [ 61 ], improved Weber local descriptor (IWLD) [ 62 ], localized angular phase (LAP) [ 63 ], local binary pattern (LBP) [ 64 ], local directional pattern (LDIP) [ 65 ], local directional pattern variance (LDiPv), inverse difference moment standardized (IDN) [ 66 ], local directional number pattern (LDNP) [ 67 ], local gradient increasing pattern (LGIP) [ 68 ], local gradient patterns (LGP) [ 69 ], local phase quantization (LPQ) [ 70 ], local ternary pattern (LTeP) [ 71 ], local tetra pattern (LTrP) [ 72 ], monogenic binary coding (MBC) [ 73 ], local frequency descriptor (LFD) [ 74 ], and local mapped pattern (LMP) [ 75 ]; however, the overall results with these textural descriptors were quite modest (as shown in Table 5 ) as compared to those obtained with our HPIL model, which makes it not suitable for classifying OPMD and standard regions.…”

Section: Discussionmentioning

confidence: 99%

Healthcare Professional in the Loop (HPIL): Classification of Standard and Oral Cancer-Causing Anomalous Regions of Oral Cavity Using Textural Analysis Technique in Autofluorescence Imaging

Awais

Ghayvat

Pandarathodiyil

et al. 2020

Sensors

View full text Add to dashboard Cite

Oral mucosal lesions (OML) and oral potentially malignant disorders (OPMDs) have been identified as having the potential to transform into oral squamous cell carcinoma (OSCC). This research focuses on the human-in-the-loop-system named Healthcare Professionals in the Loop (HPIL) to support diagnosis through an advanced machine learning procedure. HPIL is a novel system approach based on the textural pattern of OML and OPMDs (anomalous regions) to differentiate them from standard regions of the oral cavity by using autofluorescence imaging. An innovative method based on pre-processing, e.g., the Deriche–Canny edge detector and circular Hough transform (CHT); a post-processing textural analysis approach using the gray-level co-occurrence matrix (GLCM); and a feature selection algorithm (linear discriminant analysis (LDA)), followed by k-nearest neighbor (KNN) to classify OPMDs and the standard region, is proposed in this paper. The accuracy, sensitivity, and specificity in differentiating between standard and anomalous regions of the oral cavity are 83%, 85%, and 84%, respectively. The performance evaluation was plotted through the receiver operating characteristics of periodontist diagnosis with the HPIL system and without the system. This method of classifying OML and OPMD areas may help the dental specialist to identify anomalous regions for performing their biopsies more efficiently to predict the histological diagnosis of epithelial dysplasia.

show abstract

Section: Discussionmentioning

confidence: 99%

Healthcare Professional in the Loop (HPIL): Classification of Standard and Oral Cancer-Causing Anomalous Regions of Oral Cavity Using Textural Analysis Technique in Autofluorescence Imaging

Awais

Ghayvat

Pandarathodiyil

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Ultimately, 2994 quantitative radiomic features were extracted from the CECT images. The definition and implementation of the features have been described in recent studies 18,19 …”

Section: Methodsmentioning

confidence: 99%

CT Radiomics for the Prediction of Synchronous Distant Metastasis in Clear Cell Renal Cell Carcinoma

Wen

Huang

Gao

et al. 2021

J Comput Assist Tomogr

View full text Add to dashboard Cite

The aim of this study was to construct and verify a computed tomography (CT) radiomics model for preoperative prediction of synchronous distant metastasis (SDM) in clear cell renal cell carcinoma (ccRCC) patients.Methods: Overall, 172 patients with ccRCC were enrolled in the present research. Contrast-enhanced CT images were manually sketched, and 2994 quantitative radiomic features were extracted. The radiomic features were then normalized and subjected to hypothesis testing. Least absolute shrinkage and selection operator (LASSO) was applied to dimension reduction, feature selection, and model construction. The performance of the predictive model was validated through analysis of the receiver operating characteristic curve. Multivariate and subgroup analyses were performed to verify the radiomic score as an independent predictor of SDM. Results:The patients randomized into a training (n = 104) and a validation (n = 68) cohort in a 6:4 ratio. Through dimension reduction using LASSO regression, 9 radiomic features were used for the construction of the SDM prediction model. The model yielded moderate performance in both the training (area under the curve, 0.89; 95% confidence interval, 0.81-0.97) and the validation cohort (area under the curve, 0.83; 95% confidence interval, 0.69-0.95). Multivariate analysis showed that the CT radiomic signature was an independent risk factor for clinical parameters of ccRCC. Subgroup analysis revealed a significant connection between the SDM and radiomic signature, except for the lower pole of the kidney subgroup. Conclusions:The CT-based radiomics model could be used as a noninvasive, personalized approach for SDM prediction in patients with ccRCC.

show abstract

Copy-Move Detection Based on Different Forms of Local Binary Patterns

Kuznetsov

2018

Lecture Notes in Computer Science

View full text Add to dashboard Cite

The Geometric Local Textural Patterns (GLTP)

Cited by 4 publications

References 41 publications

Healthcare Professional in the Loop (HPIL): Classification of Standard and Oral Cancer-Causing Anomalous Regions of Oral Cavity Using Textural Analysis Technique in Autofluorescence Imaging

Healthcare Professional in the Loop (HPIL): Classification of Standard and Oral Cancer-Causing Anomalous Regions of Oral Cavity Using Textural Analysis Technique in Autofluorescence Imaging

CT Radiomics for the Prediction of Synchronous Distant Metastasis in Clear Cell Renal Cell Carcinoma

Copy-Move Detection Based on Different Forms of Local Binary Patterns

Contact Info

Product

Resources

About