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.
- MeSH
- Echocardiography MeSH
- Cardiomegaly diagnosis MeSH
- Humans MeSH
- Check Tag
- Humans 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
We developed a swing arm device for acoustic measurement of food texture, which resolved difficulties of food texture evaluation. The device has a structure of balance-style and a probe in the device is moved downward along with motion of swing arm according to the balance of weights at both ends of the swing arm. The probe was inserted into a food sample. The device measured displacement and acceleration of the probe on food fracture by probe insertion with high precision until the probe stops inserting into a food sample. Using the displacement and acceleration of the probe on fracture, we calculated three parameters to determine food texture. Energy texture index (ETI) which is probe kinetic energy of acoustic vibration was evaluated by the vibration on food fracture. Audible energy texture index (aETI) could be introduced as food texture perceived by human sense of hearing, which was obtained by multiplying ETI by human hearing sensitivity. It was found that the ETI and aETI can be used for measurement of characteristic food texture detected at a tooth and perceived in brain, respectively. Food friction index (FFI) to explain the friction strength of a probe against a food sample was theoretically formulated under the condition of probe motion in the device. FFI was found to be useful not only for crispy food like biscuit but for soft food. The measured FFI indicated characteristic of smoothness of probe insertion into food sample. PRACTICAL APPLICATIONS: The swing arm device can be used to estimate food texture by measuring probe vibration energy on food fracture. Energy texture index (ETI) and Audible energy texture index (aETI) are introduced into measurements of food with crispness which emits sounds on fracture by probe insertion. ETI is an index to estimate probe vibration energy, while aETI is one to estimate food texture by human sense of hearing that was corrected by human hearing sensitivity as human hearing sensitivity is highest around sound frequency ranging from 1,000 to 4,000 Hz. Food friction index (FFI) was also obtained by the device to evaluate smoothness of probe insertion into a food sample. ETI, aETI, and FFI can be useful parameters for food texture of water melon, pear, persimmon, apple, cookie, potato chips, and biscuit. FFI is especially suitable for evaluation of soft food such as banana, avocado, mango, melon, peach, pudding, or bread.
- MeSH
- Acoustics * MeSH
- Food Analysis instrumentation methods MeSH
- Equipment Design MeSH
- Kinetics MeSH
- Humans MeSH
- Food Handling instrumentation methods MeSH
- Hearing Tests MeSH
- Models, Theoretical MeSH
- Vibration MeSH
- Acceleration MeSH
- Sound MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
