MRI ‘texture’ analysis of MR images of apples during ripening and storage

Létal, Ji_i; Jirák, Daniel; Šuderlová, L.; Hájek, Milan

doi:10.1016/s0023-6438(03)00099-9

Cited by 56 publications

(26 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These include core breakdown in pear (Lammertyn et al, 2003) watercore disorder , internal browning (Gonzalez et al, 2001) and mealiness in apple (Barreiro et al, 1999;Letal et al, 2003). There are few reports where MRI was used to detect the early stages of CI in sensitive produce.…”

Section: Introductionmentioning

confidence: 99%

Magnetic resonance imaging provides spatial resolution of Chilling Injury in Micro-Tom tomato (Solanum lycopersicum L.) fruit

Tao

Zhang

McCarthy

et al. 2014

Postharvest Biology and Technology

View full text Add to dashboard Cite

a b s t r a c tMagnetic resonance imaging (MRI) was used to monitor internal changes in harvested tomato (Solanum lycopersicum L. cv. Micro-Tom) fruit. Measurements of ethylene evolution, respiration, and ion leakage indicated that the fruit developed chilling injury (CI) after storage at 0• C. Unlike these measurements, MRI provided spatially resolved data. The apparent diffusion coefficient (ADC), which is an indication of water mobility in tissues, was calculated from MRIs of the different parts of the fruit. Storage for 1 or 2 weeks at 0• C caused no difference in the ADCs (D-values) in the pericarp, but it did lead to higher values in the inner tissues i.e., the columella and locular region compared to non-chilled fruit (P < 0.05). Changes in inner fruit D-values after 1 and 2 weeks of chilling at 0• C were similar to changes in respiration, ethylene production and ion leakage which increased (P < 0.05) compared to the non-chilled controls. Most CI studies of tomato fruit used pericarp tissue. Our data indicate that columella tissue changes occur in response to chilling injury in tomato fruit and suggest that more caution is needed when interpreting data from experiments commonly used to study this phenomenon.

show abstract

Section: Introductionmentioning

confidence: 99%

Magnetic resonance imaging provides spatial resolution of Chilling Injury in Micro-Tom tomato (Solanum lycopersicum L.) fruit

Tao

Zhang

McCarthy

et al. 2014

Postharvest Biology and Technology

View full text Add to dashboard Cite

show abstract

“…For instance, optical coherence tomography and visible/near infrared spectroscopy have been recently suggested as useful tools to predict peel collapse in stored citrus fruit (Magwaza et al, 2013(Magwaza et al, , 2014a. Similarly, MRI has been used to analyze internal changes in fruit during postharvest storage (Defraeye et al, 2013;Galed et al, 2004;Gonzalez et al, 2001;Herremans et al, 2014;Lammertyn et al, 2003;Letal et al, 2003). Our sample scans show examples of internal navel tissues of various sizes (Fig.…”

Section: Mean Values Of Pulp Diameter Albedo Width and Navel Dimensmentioning

confidence: 81%

Use of Magnetic Resonance Imaging (MRI) to Study and Predict Fruit Splitting in Citrus

Zur¹,

Shlizerman²,

Ben-Ari³

et al. 2017

Hortic J

View full text Add to dashboard Cite

Fruit splitting is a preharvest physiological disorder that occurs in many commercially important fruit species, including some citrus cultivars such as Navel oranges, Valencia oranges, and some mandarins. The phenomenon is affected by both genetic background and environmental conditions, causing heavy fruit loss in splitting-prone cultivars in some years. For instance, high levels of irrigation usually enhance splitting incidence. The phenomenon initiates at the end of the summer, toward fruit maturation, and to date, there is no way to predict splitting incidence. Here, we tested the ability of nondestructive magnetic resonance imaging (MRI), which does not alter the dimensions of the internal fruit tissues, to predict splitting incidence in 'Nova' mandarin fruit populations of trees subjected to two levels of irrigation (low and high). Samples, collected about two months prior to splitting appearance, were sorted by their internal tissues dimensions. Among all measured tissues, sorted navel dimensions showed the best correlation with splitting incidence. This was determined by dividing the MRI-sampled fruits into two populations, according to the actual splitting incidence, as calculated at the end of the season. Prediction of the splitting percentage in the low irrigation fruit population was than possible in the high irrigation fruit population, and vice versa. These results demonstrated the power of MRI to predict splitting probability as early as 2 months before split fruit are found. In addition to its potential practical application, the ability to predict splitting probability in a given fruit population could help elucidate the mechanism underlying the disorder.

show abstract

“…With a specialized software called Mazda [28], 296 texture features are computed for each element in the training set. Various approaches have demonstrated the effectiveness of this software in extracting textural features from different types of medical images [29][30][31][32][33].…”

Section: Proposed Methodsmentioning

confidence: 99%

Texture Classification of Proteins Using Support Vector Machines and Bio-inspired Metaheuristics

Fernández-Lozano

Seoane

Mesejo

et al. 2014

Biomedical Engineering Systems and Technologies

View full text Add to dashboard Cite

International audienceIn this paper, a novel classification method of two-dimensional poly-acrylamide gel electrophoresis images is presented. Such a method uses textural features obtained by means of a feature selection process for whose implementation we compare Genetic Algorithms and Particle Swarm Optimization. Then, the selected features, among which the most decisive and representative ones appear to be those related to the second order co-occurrence matrix, are used as inputs for a Support Vector Machine. The accuracy of the proposed method is around 94%, a statistically better performance than the classification based on the entire feature set. This classification step can be very useful for discarding over-segmented areas after a protein segmentation or identification process

show abstract

MRI ‘texture’ analysis of MR images of apples during ripening and storage

Cited by 56 publications

References 8 publications

Magnetic resonance imaging provides spatial resolution of Chilling Injury in Micro-Tom tomato (Solanum lycopersicum L.) fruit

Magnetic resonance imaging provides spatial resolution of Chilling Injury in Micro-Tom tomato (Solanum lycopersicum L.) fruit

Use of Magnetic Resonance Imaging (MRI) to Study and Predict Fruit Splitting in Citrus

Texture Classification of Proteins Using Support Vector Machines and Bio-inspired Metaheuristics

Contact Info

Product

Resources

About