Tomasz Markiewicz 1,2, Robert Koktysz 2, Stanisław Osowski 1, Michał Muszyński 1, Wojciech Kozłowski 2

Transcription

1 Tomasz Markiewicz 1,, Robert Koktysz, Stanisław Osowski 1, Michał Muszyński 1, Wojciech Kozłowski 1 Warsaw University of Technology, Dept. of Electrical Eng. and Military Institute of Medicine, Department of Pathomorphology

2 Introduction The complex structure recognition in the image required design of the specific methods and algorithms of analysis and segmentation. One of the apropriate to this task technique is the texture analysis gives the numerical factors of the structures. The tools for the local texture calculation, classification and correction method of a region estimation are needed.

3 Material and the aim of study The histological slides (0) of placenta specimens come from archives of the Department of Pathomorphology, Military Institute of Medicine, Warsaw, Poland, are investigated. They were stained with hematoxylin and eosin, and the images were acquired at 400x magnification. The specimens come form spontaneous miscarriage (the first trimesters: 6-10 weeks of pregnancy).

4 Placenta structures Manual of Benirschke and Kaufmann s Pathology of the Human Placenta, Rebecca N. Baergen, Springer 005

5 Histological images

6 Histological images

7 Aim: Segmentation of the different structures of placenta. Classification of histological structures. Graduation of mezyncheme sweeling

8 Methods The proposed image analysis scheme is organized in the following steps: description of local image properties using Unser textural features, classification of pixels applying Support Vector Machine classifier, grouping pixels into compact subregions and correction of borders between subregions using region growing method.

9 Texture descriptors Our approach to texture analysis is based on the normalized probability of i-th intensity on the basis of histograms of the sum and difference images They are calculated indepedently over any considered region associated with an each pixel of the image. In our notation N represents the total number of pixel in region, s(x) and d(x) represent the pixel values of the sum and difference images. = + = ,,,,,, d l d k l k l k d l d k l k l k y y d y y s = = N i h i P N i h i P d d s s )/ ( ) ( ˆ )/ ( ) ( ˆ

10 Local Unser texture features Name Modified computational formula Name Modified computational formula Mean s( x) f 1 = x N = µ Contrast f 5 = x d( N x) Variance ( s( x) ) + d ( x) f = 1 x N x µ Homogeneity f 6 = d( N x x ) Energy s( x) d( x) Cluster shade ( s( x) µ ) f 3 = x N x f 7 = x N 3 Correlation ( s( x) ) d ( x) f 4 = 1 x N x µ Cluster prominence f 8 = ( s( x) µ ) x N 4

11 Implementation The difficult computational complexity problems arise in calculation of the local texture features. They are associated with the traveling location of central pixel and its neighboring region, because of constant change of the mean value over the actual region. It was solved by us applying the array operations. The process of adding the pixel values in sum and difference images was realized quickly by applying the average filtering of the image (embedded imfilter function in Matlab).

12 Implementation By using filtering we can create the mean mask for the whole image in only one analysis. For example, the variance feature can be computed according to the following (modified) expression. ( ) ( ) ( ) s x 4µ s x + 4µ N + d x x x x f = N The first term of this relation is calculated applying the filtering of the array-squared sum image (the Hadamard product), the second by array-fashion multiplication of the mean of the image and filtered sum image.

13 Crucial parameters Pixel width: 0.46μm. Displacement d equal 3 pixels in horizontal direction. We examined the local region masks in a disc shape with the radius of 5, 8, 10, 1, 15 and 0 pixels. The SVM classifier with Gaussian kernel function, one-against-one strategy and different C and σ were examined. All features were calculated for R, G and B channel of the image.

14 No of classes and region size In the experiments we recognized: villous mesenchyme (class 1), trophoblast (class ), areas of fibrosis (class 3), hemorrhage (class 4), trophoblast proliferation or bearing partitions (class 5) and background (class 6). Size: 5,10,15 or 0 pixels of region radius?

15 Identification of swelling degree of mezyncheme in villi Three classes have been defined: no swollen (class 1), medium swollen (class ) and large swollen (class 3). These classes were represented by the expertselected villi with uniqually-recognized swollen degree. To assess the class discrimination quality of each feature the relieff algorithm were used.

16 Graphical results

17 Graphical results

18 Graphical results

19 Sweeling degree

20 Numerical results (preliminary) Interior regions of the villi with a number of loosely distributed mesenchymal stromal cells were correctly identified with 94.7% of accuracy. The background area has been recognized with 97.% of accuracy. The fibrosis has been recognized with 91.7% of accuracy.

21 Numerical results (preliminary) The trophoblast has been recognized with 9.9% of accuracy. The rest regions were too small or non-unique reprezentation - not for numerical evaluation. The classes of villis were assessed by experts and recognized by system using random forest classifier. The average recognition accuracy of 3 classes was equal 96.1%.

22 Numerical results The rank of feature based on relieff method 0.07 weight - Importance of attributes (ReliefF) No of feature

23 Numerical results PCA of texture features of swollen villi

24 Conclusion We developed the tool based on generation of textural features describing local properties of images and classification of pixels. The proposed method was used in recognition of internal structures of placenta tissues in spontaneous miscarriage. The preliminary results have shown satisfactory accuracy of structure recognition, but need the future investigations: additional classes of structure, influence of image acquisition parameters, feature analysis in different pairs of classes etc.

25 Acknowledgement: This work is supported by the National Science Centre (Poland) by the grant 01/07/B/ST7/0103 in the years