Ultrasonografie je všeobecně uznávanou metodou v diagnostice difuzních i ložiskových onemocnění štítné žlázy. Její hlavní omezení spočívá v neschopnosti lidského zraku analyzovat všechny informace, které ultrasonogram obsahuje; kromě toho je míra subjektivity hodnocení, daná zejména zkušeností vyšetřujícího, větší než u jiných zobrazovacích metod. Počítačová analýza textury ve spojení s automatickou klasifikací může představovat účinný nástroj, schopný bezprostředně po standardním vyšetření určit typ difuzního postižení s přesností až 100 %. Z řady postupů, které jsme v uplynulých letech vyzkoušeli, se nejlépe osvědčila kombinace prostorových, kookurenčních a systematicky konstruovaných příznaků, vybíraná selekční metodou, založenou na velikosti klasifikační chyby. Testováno bylo několik klasifikačních přístupů, z nichž optimální byl Bayesův klasifikátor doplněný o kritérium majority.
Ultrasonography is a generally accepted method for diagnosing both the diffuse and focal (nodal) lesions of the thyroid gland. The main limit of this method is the restricted ability of the human eye to analyse all information included. Moreover, the proportion of subjectivity when evaluating the picture is greater than with other imaging methods. Computer texture analysis in combination with automatic classification may prove a potent tool that could enable – immediately after the standard examination – to assign the finding to a particular type of diffuse disorder, with an accuracy of up to 100 percent. From a variety of procedures, which we have tested until now, the best results were obtained with a combination of spatial, co-occurrence and systematically constructed features, selected by a method that is based on the magnitude of classification error. From several tested ways of classification the Bayes’s classificator in combination with the criterion of majority was found to be the best approach.
... 11 -- 1.1 Introduction 11 -- 1.2 Texture analysis 19 -- 1.3 Approaches to texture description 22 -- 1.4 ... ... Phantoms for texture analysis of MR images 116 -- 5.2.1 Construction of phantom for ?? ... ... texture analysis 137 -- 6.4 The effect of spatial resolution on image texture parameters 144 -- 6.4.1 ... ... anisotropy in trabecular bone using texture analysis 146 -- 6.5 Conclusions 147 -- 7. ... ... Textures in MR images of food products 193 -- 8.1 Introduction 193 -- 8.2 Texture analysis of MR images ...
234 s. : il. (některé barev.) ; 24 cm + 1 CD-ROM
- Keywords
- Magnetická rezonance,
- MeSH
- Magnetic Resonance Imaging MeSH
- Conspectus
- Patologie. Klinická medicína
- NML Fields
- radiologie, nukleární medicína a zobrazovací metody
- NML Publication type
- monografie
Texture analysis is an established technique to assess objectively the textural properties of materials and products, such as hardness, elasticity, burst strength and adhesiveness. With the need for reliable and cost-effective equipment, texture analysis is becoming the method of choice in the pharmaceutical industry to maintain quality standards and provide analytical data on multiple products such as pharmaceutical dosage forms. With the large number of techniques and probes available to assess physical properties of materials, texture analyzers represent innovative equipment for research and industrial purposes.
- Keywords
- textura, texturometr, texturní analýza, mechanické vlastnosti, mukoadhezivní lékové formy,
- MeSH
- Suppositories MeSH
- Drug and Narcotic Control methods MeSH
- Dosage Forms * MeSH
- Ointments MeSH
- Bandages MeSH
- Surface Properties MeSH
- Powders MeSH
- Tablets MeSH
- Materials Testing MeSH
- Capsules MeSH
- Equipment and Supplies MeSH
- Publication type
- Review MeSH
svazky : ilustrace
- MeSH
- Food Analysis MeSH
- Rheology MeSH
- Drug Stability MeSH
- Publication type
- Periodical MeSH
- Conspectus
- Potravinářský průmysl
- NML Fields
- nutriční terapie, dietoterapie a výživa
- fyziologie
- MeSH
- Anatomy, Regional methods trends MeSH
- Artifacts MeSH
- Diagnostic Imaging methods trends utilization MeSH
- Image Interpretation, Computer-Assisted * MeSH
- Humans MeSH
- Statistics as Topic MeSH
- Ultrasonography * methods trends utilization MeSH
- Image Enhancement MeSH
- Imaging, Three-Dimensional * methods utilization MeSH
- Check Tag
- Humans MeSH
- Publication type
- Review MeSH
BACKGROUND: Duchenne muscular dystrophy (DMD) patients are monitored periodically for cardiac involvement, including cardiac MRI with gadolinium-based contrast agents (GBCA). Texture analysis (TA) offers an alternative approach to assess late gadolinium enhancement (LGE) without relying on GBCA administration, impacting DMD patients' care. The study aimed to evaluate the prognostic value of selected TA features in the LGE assessment of DMD patients. RESULTS: We developed a pipeline to extract TA features of native T1 parametric mapping and evaluated their prognostic value in assessing LGE in DMD patients. For this evaluation, five independent TA features were selected using Boruta to identify relevant features based on their importance, least absolute shrinkage and selection operator (LASSO) to reduce the number of features, and hierarchical clustering to target multicollinearity and identify independent features. Afterward, logistic regression was used to determine the features with better discrimination ability. The independent feature inverse difference moment normalized (IDMN), which measures the pixel values homogeneity in the myocardium, achieved the highest accuracy in classifying LGE (0.857 (0.572-0.982)) and also was significantly associated with changes in the likelihood of LGE in a subgroup of patients with three yearly examinations (estimate: 23.35 (8.7), p-value = 0.008). Data are presented as mean (SD) or median (IQR) for normally and non-normally distributed continuous variables and numbers (percentages) for categorical ones. Variables were compared with the Welch t-test, Wilcoxon rank-sum, and Chi-square tests. A P-value < 0.05 was considered statistically significant. CONCLUSION: IDMN leverages the information native T1 parametric mapping provides, as it can detect changes in the pixel values of LGE images of DMD patients that may reflect myocardial alterations, serving as a supporting tool to reduce GBCA use in their cardiac MRI examinations.
- MeSH
- Child MeSH
- Muscular Dystrophy, Duchenne * diagnostic imaging pathology MeSH
- Gadolinium MeSH
- Contrast Media MeSH
- Humans MeSH
- Magnetic Resonance Imaging * methods MeSH
- Adolescent MeSH
- Check Tag
- Child MeSH
- Humans MeSH
- Adolescent MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
