Automated analysis
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Experimental pathology, ISSN 0232-2862 suppl. 9, 1984
123 s. : il., tab., grafy
230 s. : il.
Clinical and biochemical analysis ; Vol. 7
333 s. : il.
Clinical and biochemical analysis ; Vol. 7
620 s. : il.
Background: Tuberculosis (TB) is a major cause of illness and death in many countries, especially in Asia and Africa. Repeated tests of microscopic examination are needed to be performed for early detection of the disease. Hence there is a need to automate the diagnostic process for improvement in the sensitivity and accuracy of the test. Objective: To automate the decision support system for tuberculosis digital images using histogram based statistical features and evolutionary based extreme learning machines. Materials and methods: The sputum smear positive and negative images recorded under standard image acquisition protocol are subjected to histogram based feature extraction technique. Most significant features are selected using student ‘t’ test. These significant features are further used as input to the differential evolutionary extreme learning machine classifier. Results: Results demonstrate that the histogram based significant features are able to differentiate TB positive and negative images with a higher specificity and accuracy. Conclusion: The methodology used in this work seems to be useful for the automated analysis of TB sputum smear images in mass screening disorders such as pulmonary tuberculosis.
In the last few years, classification of cells by machine learning has become frequently used in biology. However, most of the approaches are based on morphometric (MO) features, which are not quantitative in terms of cell mass. This may result in poor classification accuracy. Here, we study the potential contribution of coherence-controlled holographic microscopy enabling quantitative phase imaging for the classification of cell morphologies. We compare our approach with the commonly used method based on MO features. We tested both classification approaches in an experiment with nutritionally deprived cancer tissue cells, while employing several supervised machine learning algorithms. Most of the classifiers provided higher performance when quantitative phase features were employed. Based on the results, it can be concluded that the quantitative phase features played an important role in improving the performance of the classification. The methodology could be valuable help in refining the monitoring of live cells in an automated fashion. We believe that coherence-controlled holographic microscopy, as a tool for quantitative phase imaging, offers all preconditions for the accurate automated analysis of live cell behavior while enabling noninvasive label-free imaging with sufficient contrast and high-spatiotemporal phase sensitivity.
- MeSH
- algoritmy MeSH
- buňky klasifikace cytologie MeSH
- holografie metody MeSH
- lidé MeSH
- mikroskopie metody MeSH
- rozpoznávání automatizované MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Clinical islet transplantation programs rely on the capacities of individual centers to quantify isolated islets. Current computer-assisted methods require input from human operators. Here we describe two machine learning algorithms for islet quantification: the trainable islet algorithm (TIA) and the nontrainable purity algorithm (NPA). These algorithms automatically segment pancreatic islets and exocrine tissue on microscopic images in order to count individual islets and calculate islet volume and purity. References for islet counts and volumes were generated by the fully manual segmentation (FMS) method, which was validated against the internal DNA standard. References for islet purity were generated via the expert visual assessment (EVA) method, which was validated against the FMS method. The TIA is intended to automatically evaluate micrographs of isolated islets from future donors after being trained on micrographs from a limited number of past donors. Its training ability was first evaluated on 46 images from four donors. The pixel-to-pixel comparison, binary statistics, and islet DNA concentration indicated that the TIA was successfully trained, regardless of the color differences of the original images. Next, the TIA trained on the four donors was validated on an additional 36 images from nine independent donors. The TIA was fast (67 s/image), correlated very well with the FMS method (R2=1.00 and 0.92 for islet volume and islet count, respectively), and had small REs (0.06 and 0.07 for islet volume and islet count, respectively). Validation of the NPA against the EVA method using 70 images from 12 donors revealed that the NPA had a reasonable speed (69 s/image), had an acceptable RE (0.14), and correlated well with the EVA method (R2=0.88). Our results demonstrate that a fully automated analysis of clinical-grade micrographs of isolated pancreatic islets is feasible. The algorithms described herein will be freely available as a Fiji platform plugin.
- MeSH
- algoritmy MeSH
- automatizace MeSH
- krysa rodu rattus MeSH
- Langerhansovy ostrůvky * cytologie MeSH
- lidé MeSH
- počítačové zpracování obrazu * MeSH
- potkani Wistar MeSH
- strojové učení MeSH
- transplantace Langerhansových ostrůvků * MeSH
- zvířata MeSH
- Check Tag
- krysa rodu rattus MeSH
- lidé MeSH
- zvířata MeSH
