Sugar-free aerated chocolate: Production, investigation of bubble features using X-ray computed tomography and image processing.

In contrast to other imaging techniques, X-ray imaging does not destruct the internal structure of the sample being imaged. Furthermore, this technique is able to capture numerous images of the sample at a low slice thickness, which is almost impossible in other imaging techniques. In this study, sugar was replaced with inulin:maltodextrin mixtures at ratios of 25:75 (i25), 50:50 (i50), and 75:25 (i75). Then, nitrogen (N2 ) and carbon dioxide (CO2 ) were injected into the three mixtures as well as the sugar-containing sample (control) at pressures of 3, 4.5, and 6 bar to produce aerated chocolate. The images of the samples were captured using X-ray computed tomography (XCT). After processing, they were segmented using the Chan-Vese model. Image segmentation showed that the Chan-Vese method, compared with adaptive thresholding, was more able to segment the images and remove the noise. The bubble total volume (10440 ± 9206 mm3 ) and average diameter (1.30 ± 0.10 mm) of the control were larger than those of the other samples. The results also demonstrated that the sugar-free aerated samples had lower hardness than the corresponding unaerated ones. However, it was reversed in the case of the control. This research sheds light on the industrialization of the production of aerated chocolate and the application of XCT and image processing in the analysis of the microstructure of aerated products.

Development of shelf life kinetic model for fresh rainbow trout (Oncorhynchus mykiss) fillets stored under modified atmosphere packaging.

The sensory, chemical (based on the thiobarbituric acid, total volatile basic nitrogen and trimethylamine), and microbial quality (based on the total viable count and lactic acid bacteria count) of the rainbow trout stored under modified atmosphere packaging (MAP) conditions was evaluated. Four different gas combinations, including P1 (80% CO2, 10% N2, 10% O2), P2 (60% CO2, 20% N2, 20% O2), P3 (60% CO2, 40% N2, 0% O2), and P4 (40% CO2, 30% N2, 30% O2), were used. Also, the fish packages were stored at four constant temperatures (including 0, 5, 10, and 15 °C) for 12 days. The absence of oxygen in P3 and high concentration of carbon dioxide in P1 extended the shelf life by delaying the chemical, microbial, and sensory spoilage. Over the storage time of trout fillets in MAP, the rate of chemical reactions significantly increased while the sensory scores decreased. Based on the Arrhenius kinetic modeling for the spoilage reactions of the sensory (total acceptance) and chemical (total volatile basic nitrogen) indices, the shelf life was extended for P3 and succeedingly, for P1 packaging.

A new dataset of computed-tomography angiography images for computer-aided detection of pulmonary embolism.

The lack of publicly available datasets of computed-tomography angiography (CTA) images for pulmonary embolism (PE) is a problem felt by physicians and researchers. Although a number of computer-aided detection (CAD) systems have been developed for PE diagnosis, their performance is often evaluated using private datasets. In this paper, we introduce a new public dataset called FUMPE (standing for Ferdowsi University of Mashhad's PE dataset) which consists of three-dimensional PE-CTA images of 35 different subjects with 8792 slices in total. For each benchmark image, two expert radiologists provided the ground-truth with the assistance of a semi-automated image processing software tool. FUMPE is a challenging benchmark for CAD methods because of the large number (i.e., 3438) of PE regions and, more especially, because of the location of most of them (i.e., 67%) in lung peripheral arteries. Moreover, due to the reporting of the Qanadli score for each PE-CTA image, FUMPE is the first public dataset which can be used for the analysis of mortality and morbidity risks associated with PE. We also report some complementary prognosis information for each subject.

FoCA: A new framework of coupled geometric active contours for segmentation of 3D cardiac magnetic resonance images.

In this paper, a new framework of coupled active contours (FoCA) is proposed for segmentation of the left ventricle myocardium, in cardiac magnetic resonance (CMR) images, without primary learning and user-driven segmentation. Primarily, we suggest a pair of coupled geometric active contours (GACs) for segmentation of the endo- and epicardial boundaries of the left ventricle in every CMR slice. The energy functional of each active contour includes the edge and shape terms of the STACS energy functional, regulator term of the local binary fitting (LBF), and new region and coupling terms. Two new patch-based region terms, inspired by LBF and piecewise model, are proposed to effectively handle intensity inhomogeneity of CMR images. Furthermore, a coupling energy term is added to the epicardial energy functional to avoid intersection with the endocardial curve. For 3D implementation, every 2D active contour in each slice is effectively jointed to the corresponding curves in the previous and next slices (of the same volume) by using a new coupling energy term, obtained by extending the 2D length-shortening regulator. Also, the initial contour and algorithm parameters are automatically regulated. Finally, 3D+t implementation is performed by using the sequential initialization method. Experimental results demonstrated that the proposed method provided superior solution quality compared to a large number of counterpart algorithms by using two well-known frequently-used databases.

A novel constructive-optimizer neural network for the traveling salesman problem.

In this paper, a novel constructive-optimizer neural network (CONN) is proposed for the traveling salesman problem (TSP). CONN uses a feedback structure similar to Hopfield-type neural networks and a competitive training algorithm similar to the Kohonen-type self-organizing maps (K-SOMs). Consequently, CONN is composed of a constructive part, which grows the tour and an optimizer part to optimize it. In the training algorithm, an initial tour is created first and introduced to CONN. Then, it is trained in the constructive phase for adding a number of cities to the tour. Next, the training algorithm switches to the optimizer phase for optimizing the current tour by displacing the tour cities. After convergence in this phase, the training algorithm switches to the constructive phase anew and is continued until all cities are added to the tour. Furthermore, we investigate a relationship between the number of TSP cities and the number of cities to be added in each constructive phase. CONN was tested on nine sets of benchmark TSPs from TSPLIB to demonstrate its performance and efficiency. It performed better than several typical Neural networks (NNs), including KNIES_TSP_Local, KNIES_TSP_Global, Budinich's SOM, Co-Adaptive Net, and multivalued Hopfield network as wall as computationally comparable variants of the simulated annealing algorithm, in terms of both CPU time and accuracy. Furthermore, CONN converged considerably faster than expanding SOM and evolved integrated SOM and generated shorter tours compared to KNIES_DECOMPOSE. Although CONN is not yet comparable in terms of accuracy with some sophisticated computationally intensive algorithms, it converges significantly faster than they do. Generally speaking, CONN provides the best compromise between CPU time and accuracy among currently reported NNs for TSP.

A novel evolutionary approach for optimizing content-based image indexing algorithms.

Optimization of content-based image indexing and retrieval (CBIR) algorithms is a complicated and time-consuming task since each time a parameter of the indexing algorithm is changed, all images in the database should be indexed again. In this paper, a novel evolutionary method called evolutionary group algorithm (EGA) is proposed for complicated time-consuming optimization problems such as finding optimal parameters of content-based image indexing algorithms. In the new evolutionary algorithm, the image database is partitioned into several smaller subsets, and each subset is used by an updating process as training patterns for each chromosome during evolution. This is in contrast to genetic algorithms that use the whole database as training patterns for evolution. Additionally, for each chromosome, a parameter called age is defined that implies the progress of the updating process. Similarly, the genes of the proposed chromosomes are divided into two categories: evolutionary genes that participate to evolution and history genes that save previous states of the updating process. Furthermore, a new fitness function is defined which evaluates the fitness of the chromosomes of the current population with different ages in each generation. We used EGA to optimize the quantization thresholds of the wavelet-correlogram algorithm for CBIR. The optimal quantization thresholds computed by EGA improved significantly all the evaluation measures including average precision, average weighted precision, average recall, and average rank for the wavelet-correlogram method.