Current Issue : January - March Volume : 2019 Issue Number : 1 Articles : 5 Articles
This paper provides an explicit representation of malignant cell division. To\nput this in perspective, the paper also provides a summary review of cellular\ndivision and duplication. Finally, a theory of tumor/cancer initiation following\ncarcinogenic exposure is presented....
Due to the close resemblance between overlapping and cancerous nuclei, the\nmisinterpretation of overlapping nuclei can affect the final decision of cancer cell detection. Thus,\nit is essential to detect overlapping nuclei and distinguish them from single ones for subsequent\nquantitative analyses. This paper presents a method for the automated detection and classification\nof overlapping nuclei from single nuclei appearing in cytology pleural effusion (CPE) images. The\nproposed system is comprised of three steps: nuclei candidate extraction, dominant feature extraction,\nand classification of single and overlapping nuclei. A maximum entropy thresholding method\ncomplemented by image enhancement and post-processing was employed for nuclei candidate\nextraction. For feature extraction, a new combination of 16 geometrical and 10 textural features was\nextracted from each nucleus region. A double-strategy random forest was performed as an ensemble\nfeature selector to select the most relevant features, and an ensemble classifier to differentiate between\noverlapping nuclei and single ones using selected features. The proposed method was evaluated on\n4000 nuclei from CPE images using various performance metrics. The results were 96.6% sensitivity,\n98.7% specificity, 92.7% precision, 94.6% F1 score, 98.4% accuracy, 97.6% G-mean, and 99% area under\ncurve. The computation time required to run the entire algorithm was just 5.17 s. The experiment\nresults demonstrate that the proposed algorithm yields a superior performance to previous studies\nand other classifiers. The proposed algorithm can serve as a new supportive tool in the automated\ndiagnosis of cancer cells from cytology images....
a...
not available...
Dual energy X-ray absorptiometry (DXA) is a dominant technique for the\nmeasurement of bone mineral density (BMD). Quality control (QC) of DXA\nis very important for the accuracy of results and correct interpretation made\nby the physician. We have performed the quality control procedures of Lunar\nDPX Pro bone densitometer according to the manufacturerâ??s recommendations\nand current clinical guidelines at the Institute of Nuclear Medicine &\nAllied Sciences (INMAS), Khulna. The objective of this study is to maximize\nthe performance of the technologist as well as the reliability of the equipment\n(Linearity, X-ray tube output, Half value layer, Kerma-area product, Radiation\nfield size, Fan angle, Spatial resolution, Room safety). The study result\nshows that the mean BMD reading is 1.004 g/cm2 with a standard deviation of\n0.0035 and co-efficient of variation 0.34%. It also shows that the precision of\nthe technologist is good and there is no malfunctioning in the DXA bone\ndensitometer....
Loading....