# CMU 42731 Bioimage Informatics - Project2_handout (3 pages)

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## Project2_handout

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## Project2_handout

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Problems/Exams

Pages:
3
School:
Carnegie Mellon University
Course:
42731 Bioimage Informatics -
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BME 42 731 ECE 18 795 Project Assignment 2 Statistical and sub pixel particle feature detection Assigned on Feb 15 2010 Due on Mar 15 2010 in class A Overview This project is divided into two parts In the first part you are asked to implement the statistical particle feature detection algorithm described in Lecture 8 In the second part you are asked to implement one of the sub pixel feature detection algorithms described in 1 The total score is 80 points B Instructions B 1 Image data We will use the same Drosophila vesicle transport image sequence that we used in project assignment 1 For the initial implementation and testing you only need to use one frame from the sequence However you final implementation must be able to detect particles from all 150 frames in a batch B 2 Part I Particle feature detection B 2 1 Calibration of dark noise 10 points Manually crop a rectangular region in the image background area which we assume contains essentially noise Calculate the mean and standard deviation of background noise These parameters will be used later in the statistical selection of point features in B 2 5 B 2 2 Low pass filtering using a Gaussian kernel 10 points Implement a 2D Gaussian filter Be sure to normalize the coefficients so that they add up to 1 Apply the Gaussian filter with standard deviation of 1 2 and 5 to one selected frame from the image sequence Identify cases when point features in unfiltered image become merged or shifted B 2 3 Detection of local maxima and local minima 10 points Apply a Gaussian kernel with standard deviation equal the Rayleigh radius The image sequence was collected using an objective lens with 100 and a NA of 1 4 The 1 fluorophore used is YFP Yellow Fluorescent Protein Its emission wavelength is at 530 nm Use a 3 3 mask to detect local maxima and local minima Select one frame compare detection results using a 3 3 mask versus a 5 5 mask B 2 4 Constructing local association of maxima and minima 10 points You can use either a

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