Nuclei segmentation on bright-field images

IT 10 027 Examensarbete 15 hp June 2010 Nuclei segmentation on bright-field images Francisco Cruz Fernández Institutionen för informationsteknologi Department of Information Technology Teknisknaturvetenskaplig fakultet UTH-enheten Besöksadress: Ångströmlaboratoriet Lägerhyddsvägen 1 Hus 4, Plan 0 Postadress: Box 536 751 21 Uppsala Telefon: 018 – 471 30 03 Telefax: 018 – 471 30 00 Hemsida: http://www.teknat.uu.se/student Abstract Nuclei segmentation on bright-field images Francisco Cruz Fernández Nuclei segmentation is a common and complicated task in image analysis. There is no general solution for the problem, and depending on the image characteristics the segmentation can be performed in different ways. Bright-field images add some complications to the problem; the color of some elements of the image is close to the color of the nuclei, making the segmentation difficult. In this thesis some methods are presented to complete this task, two classifiers, minimum distance classifier and multilayer perceptron are tested to enhance the nuclei. After the classification, threshold methods together with morphological operations are used to get the segmentation of the nuclei with an accuracy around 85%. Tryckt av: Reprocentralen ITC IT 10 027 Examinator: Anders Jansson Ämnesgranskare: Amin Allalou Handledare: Amin Allalou  3 Contents Abstract 1. Introduction…………………………………………………………… 4 1.1. Overview……………………………………………...……… 4 1.2. Project aim…………………………………………………… 4 2. Background Theory...….……………………………………………. 5 2.1. Digital Image analysis………………………………………. 5 2.2. Previous work………………………………………….......... 5 2.3. Used tools......................................................................... 6 2.4. Algorithms and operations................................................ 6 2.4.1. Segmentation...................................................... 7 2.4.2. Median Filter....................................................... 8 2.4.3. Minimum distance classifier................................ 8 2.4.4. Neural Networks: Multilayer Perceptron............. 9 2.4.5. Threshold: Otsuʼs Method................................... 11 2.4.6. Bilinear Interpolation........................................... 12 2.4.7. Morphological operations.................................... 13 3. Materials and Methods.................................................................14 3.1. Image acquisition.............................................................. 14 3.2. Image set.......................................................................... 14 3.3. Classifying nuclei by minimum distance........................... 15 3.4. Multilayer perceptron classification................................... 16 3.5. Threshold.......................................................................... 20 3.6. Morphological operations.................................................. 22 4. Results...........................................................................................24 5. Conclusions.................................................................................. 28 6. Future Work...................................................................................28 6. Acknowledgments........................................................................ 28 8. References.................................................................................... 29 !  4 !"#$%&'()*+&,(%# # # !"!"#-./'.,/0# # "#$!%&$%!'(!)*%+$!%,%-./)/!#%/!#%0!/)+,)()1%,2!3&'+&$//!),!&$1$,2!.$%&/4!5'3#)/2)1%2$0! %-+'&)2#*/! %,0! *'&$! 3'6$&(7-! #%&06%&$! #%8$! -$0! 2'! 2#$! 0$8$-'3*$,2! '(! ,$6! 2$1#,)97$/!2#%2!#%8$!&$8'-72)',):$0!%&$%/!/71#!%/!*$0)1%-!)*%+),+4!;2!)/!,'6!3'//)<-$! 2'! '<2%),! )*%+$/! 6)2#! %!+''0! 3&$1)/)',! (&'*! %,.! 3%&2! 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