Image Analysis for Automated Susceptibility Test Platform

  • 曹 懷之

Student thesis: Master's Thesis


There are many methods for antibiotic susceptibility test (AST) in clinical applications In this thesis a fast examination platform is developed for this purpose Dielectrophoresis image is acquired for automatic discrimination A rapid evaluation platform based on DEP image analysis has been proposed for antimicrobial susceptibility testing in clinical applications First the characteristics of the wafer were observed The Sobel detection method was used to convert the image of electrode line into a binary image Then the projection analysis was performed in both the horizontal and vertical directions After finding the corresponding points and line segments the four major zones where the bacteria usually attached were defined as the region of interest ROI- Next the noise reduction and local binarization were performed on the ROI regions the regions of the bacteria were then obtained for the subsequent bacteria segmentation The bacterial length of each candidate bacterium was then calculated by using the skeletal pattern The Dynamic Programming (DP) was then employed to optimize the ACM (Active contour model) and obtained the best profile solution as the refinement Due to the elongation or lysis phenomenon of drug-sensitive bacteria the bacteria which was sensitive to the applied antibiotic could be discriminated Our system classified the interpretation into two categories- the resistance group and the sensitive group The given bacteria image could be analyzed rapidly and effectively The proposed system can successfully establish a connection between the antibiotics and bacteria; it also provides an important reference for the physician in clinical diagnosis and treatment of bacterial infection The accuracy and efficiency for antimicrobial susceptibility testing is improved It does not only shorten the long processing time required by the traditional testing but save the valuable labor cost in clinical applications
Date of Award2017 Feb 15
Original languageEnglish
SupervisorYung-Nien Sun (Supervisor)

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