First of all, here is link to part 1 and here is link to the video of my talk about applications of correlation.
This part is mostly technical, about correlation calculation approaches. In the end of the last post a “cross-correlation measure” was defined (not real cross-correlation) as the summary of Euclidean distances between two functions in 1D. For 2D (images) there are 2 independent space coordinates, so “cross-correlation distance” is also 2D function. (continue reading…)
Last couple of weeks I was reading, coding and playing with spatial/temporal cross-correlation for microscopy image/movie analysis. I’m going to summarize my findings in a series of posts, before I forget everything. It is going to be a long story, so buckle up.
About a year ago me and Jalmar were working on the automated methods of line/curve extraction from images (mostly microtubules in microscopy pictures, but not limited to). There are a lot of different methods, but after some research we concluded that one of the basic and “essential baseline control” was Carsten Steger’s “An Unbiased Detector of Curvilinear Structures” published in 1996 (~830 citations since then). The paper describes method in all details and what is even more interesting, there was a link to ftp with open source code. Unfortunately almost 20 years later this ftp was down and so we tried to reconstruct code according to description in the paper with some success. (continue reading…)
It is not very clear, how biological systems (cells in particular) can bridge amazing ranges of scales in time and space. To study these processes one would need a corresponding multi-scale measurement device (in theory). But microscope, for example, by default is one-scale instrument. Meaning that in 99% of movie acquisitions there is only one time scale. I mean frame rate, it is one of the major parameters (exposure in stream or the interval between frames in timelapse recording). So biological process that one can observe and record will highly depend on the chosen microscopy time-scale. If studied process happens on different time intervals, chances are one would miss it. (continue reading…)
In many areas of science (sociology, intracellular transport, stock exchange, climatology, etc) there is a need to decompose observed noisy time series into a set of piecewise linear trends. Let me show a typical example data that I’ve generated myself:
Some bits of my current work. Relatively recent development of superresolution technique provided tools to see the shape and sub-structure of cells in much more details. Here is example: live image of HeLa cells labelled with membrane fluorescent marker (mEos) in regular widefield (top-left half) and in superresolution (SR) reconstruction:
Recently I’ve been working on projects involving heavy usage of particle tracking. There are many available solutions to do that, but since I’m a big fan of ImageJ/FIJI, I’m sticking to it. Among multiple plugins for tracking there are two best options (in my opinion): TrackMate for the automatic tracking and MTrackJ for the manual and track editing/visualization.
Kymograph is a useful tool to transform a movie containing particles movement into a single picture/figure. It is convenient when analyzing vesicles movement in cells, axons/dendrites of neurons, cars movement on roads. It shows particles’ speed, direction, intensity, etc. What it does is just plotting how intensity is changing over time along some line or curve. (continue reading…)
A lot of different things happened since my last post.
After some time I’ve redesigned my website (finally) and also recovered my blog, where I mostly plan to share different notes/images/movies, i.e. interesting leftovers from scientific research.