for ALS disease recognition PROCESAMIENTO DE IMGENES DIGITALES - - PowerPoint PPT Presentation
Image analysis of mice muscle cells for ALS disease recognition PROCESAMIENTO DE IMGENES DIGITALES Paolo Serafini Introduction Healthy mouse Mouse with ALS (Amyotrophic lateral sclerosis) Mutation in the SOD1 genome and the G93A
Paolo Serafini PROCESAMIENTO DE IMÁGENES DIGITALES
Mutation in the SOD1 genome and the G93A mutation (glycine 93 changed to alanine)
Red Image Green Image Blue Image Fast fibers Lens fibers Edge Nuclei
Green Image
Red Image Blue Image
Segmentation Without borders Combination Fast cells Binarisation Combination Red Green Blue
Green channel
1) After median filtering 2) After static filtering The first approach after the greyscale transformation is the combination of two filters to smooth the image and incremented the cells division.
The binarization was made through two different threshold methods:
With Otsu’s method for the threshold With a cycle and a defined threshold
Better result
The result after binarisation shows a lot of noise inside and
NOISE inside the cells NOISE
the cells
Sequence of removals: ➢White connected components smaller than 500 ➢Black parts with disk structural element ➢All white noise inside the cells through filling ➢Black connected components smaller than 1000
Removal of white connected components smoller than 500
Morphological operation of closing with a disk structural element of radius 30.
Through the use of imfill function implemented on Matlab I was able to remove this white noise inside the cells. Previous image After the filling
Final segmented image Remove of all connected component of the inverse image smaller than 1000
Used of functions:
With this function is possible to compute Area, Convexity and Eccentricty
Green channel
Green binarized image Red channel
Fast fibers Slow fibers
establish the amount of fibers with a mean intensity smaller and greater than 50.
Fast cells
which mean intensity is smaller than 50
fibers
Green channel
Binarized image Image after the remove of all connected component smaller than 500
Result image to establish the number
connected component Green binarized image Blue segmented image
nuclei is computed from the binarized and segmented image
the amount of nuclei inside the cells divided to the total
Green image Binarized image As we can see in this example the binarisation does not allowed a clearly separation of the different cells, due to a problem related to the thresold.
To overcome the problem is possible to implemented a GUI with which the user could change the values of the different threshold and thanks to this he could improve the segmentation. Anyhow, many tissues studied resulted well segmented and the computation of their property was very precise. This work allows the scientist to obtain the different property of the cells (Area, eccentricity, convexity, number of fast and slow fibres and number of nuclei inside the cells) and to start a study of that to understand the main characteristics that could establish if a Mouse is affected or not by the ALS.
[1] Mauro Miazaki, Matheus P Viana, Zhong Yang, Cesar H Comin, YamingWang, Luciano da F Costa, Xiaoyin Xu .”Automated high-content morphological analysis of muscle fiber histology”. Computers in Biology and Medicine, Vol. 63, pp. 28–35, 2015. [2] MathWorks. (s.f.). bwconncomp. Link: https://es.mathworks.com/help/images/ref/bwconncomp.html [3] MathWorks. (s.f.). imhmin. Link: https://it.mathworks.com/help/images/ref/imhmin.html [4] MathWorks. (s.f.). regionprops. Link: https://it.mathworks.com/help/images/ref/regionprops.html [5] MathWorks. (s.f.). imfill. Link: https://es.mathworks.com/help/images/ref/imfill.html [6] MathWorks. (s.f.). medfilt2. Link: https://it.mathworks.com/help/images/ref/medfilt2.html [7] MathWorks. (s.f.). ordfilt2. Link: https://it.mathworks.com/help/images/ref/ordfilt2.html [8] MathWorks. (s.f.). imclearborder. Link: https://es.mathworks.com/help/images/ref/imclearborder.html