• Journal of Biomedical Engineering Research
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• v.38 no.1
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• pp.25-31
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• 2017

Testing TB in chest X-ray images is a typical method to diagnose presence and magnitude of PTB lesion. However, the method has limitation due to inter-reader variability. Therefore, it is essential to overcome this drawback with automatic interpretation. In this study, we propose a novel method for detection of PTB using SegNet, which is a deep learning architecture for semantic pixel wise image labelling. SegNet is composed of a stack of encoders followed by a corresponding decoder stack which feeds into a soft-max classification layer. We modified parameters of SegNet to change the number of classes from 12 to 2 (TB or none-TB) and applied the architecture to automatically interpret chest radiographs. 552 chest X-ray images, provided by The Korean Institute of Tuberculosis, used for training and test and we constructed a receiver operating characteristic (ROC) curve. As a consequence, the area under the curve (AUC) was 90.4% (95% CI:[85.1, 95.7]) with a classification accuracy of 84.3%. A sensitivity was 85.7% and specificity was 82.8% on 431 training images (TB 172, none-TB 259) and 121 test images (TB 63, none-TB 58). This results show that detecting PTB using SegNet is comparable to other PTB detection methods.

• Journal of Yeungnam Medical Science
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• v.41 no.2
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• pp.113-119
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• 2024

Background: Missing isoniazid (INH) resistance during tuberculosis (TB) diagnosis can worsen the outcomes of INH-resistant TB. The BD MAX MDR-TB assay (BD MAX) facilitates the rapid detection of TB and INH and rifampin (RIF) resistance; however, data related to its performance in clinical setting remain limited. Moreover, its effect on treatment outcomes has not yet been studied. Methods: We compared the performance of BD MAX for the detection of INH/RIF resistances to that of the line probe assay (LPA) in patients with pulmonary TB (PTB), using the results of a phenotypic drug sensitivity test as a reference standard. The treatment outcomes of patients who used BD MAX were compared with those of patients who did not. Results: Of the 83 patients included in the study, the BD MAX was used for an initial PTB diagnosis in 39 patients. The sensitivity of BD MAX for detecting PTB was 79.5%. The sensitivity and specificity of BD MAX for INH resistance were both 100%, whereas these were 50.0% and 95.8%, respectively, for RIF resistance. The sensitivity and specificity of BD MAX were comparable to those of LPA. The BD MAX group had a shorter time interval from specimen request to the initiation of anti-TB drugs (2.0 days vs. 5.5 days, p=0.001). Conclusion: BD MAX showed comparable performance to conventional tests for detecting PTB and INH/RIF resistances. The implementation of BD MAX as a diagnostic tool for PTB resulted in a shorter turnaround time for the initiation of PTB treatment.

• Journal of Animal Environmental Science
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• v.9 no.1
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• pp.45-56
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• 2003

$TAO^{TM}$ System is an auto-heated thermophilic aerated digestion process using a proprietary microbe called as a Phototropic Bacteria (PTB). High metabolic activity results in heat generation, which enables to produce a pathogen-free and digested liquid fertilizer at short retention times. TAO$^{TM}$ system has been developed to reduce a manure volume and convert into the liquid fertilizer using swine manure since 1992. About 100 units have been installed and operated in Korean swine farms so far. TAO$^{TM}$ system consists of a reactor vessel and ejector-type aeration pumps and foam removers. The swine slurry manure enters into vessel with PTB and is mixed and aerated. The process is operated at detention times from 2 to 4 days and temperature of 55 to $65^{\circ}C$. Foams are occurred and broken down by foam removers to evaporate water contents. Generally, at least 30% of water content is evaporated, 99% of volatile fatty acids caused an odor are removed and pathogen destruction is excellent with fecal coliform, rotavirus and salmonella below detection limits. The effluent from TAO$^{TM}$ system, called as the "TAO EFFLUX", is screened and has superb properties as a fertilizer. Normally N-P-K contents of screened TAO Efflux are 4.7 g/L, 0.375 g/L and 2.8 g/L respectively. The fertilizer effect of TAO EFFLUX compared to chemical fertilizer has been demonstrated and studied with various crops such as rice, potato, cabbage, pumpkin, green pepper, parsley, cucumber and apple. Generally it has better fertilizer effects and excellent soil fertility improvement effects. Moreover, the TAO EFFLUX is concentrated through membrane technology without fouling problems for a cost saving of long distance transportation and a commercialization (crop nutrient commodity) to a gardening market, for example.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.270-278
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• 2000

A machine vision system using charge coupled device(CCD) camera for the weed detection in a radish farm was developed. Shape features were analyzed with the binary images obtained from color images of radish and weeds. Aspect, Elongation and PTB were selected as significant variables for discriminant models using the STEPDISC option. The selected variables were used in the DISCRIM procedure to compute a discriminant function for classifying images into one of the two classes. Using discriminant analysis, the successful recognition rate was 92% for radish and 98% for weeds. To recognize radish and weeds more effectively than the discriminant analysis, an artificial neural network(ANN) was used. The developed ANN model distinguished the radish from the weeds with 100%. The performance of ANNs was improved to prevent overfitting and to generalize well using a regularization method. The successful recognition rate in the farms was 93.3% for radish and 93.8% for weeds. As a whole, the machine vision system using CCD camera with the artificial neural network was useful to detect weeds in the radish farms.