alexa Nuclear Segmentation of Glioblastoma Multiforme Cells by Multireference Level Set | Open Access Journals
ISSN: 2155-9538
Journal of Bioengineering & Biomedical Science
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Nuclear Segmentation of Glioblastoma Multiforme Cells by Multireference Level Set

Rujuta O* and Vyavahare AJ

Department of Electronics & Communication, M.E. E &TC Modern College of Engineering, Pune, India

*Corresponding Author:
Rujuta O
Department of Electronics & Communication
M.E. E &TC, Modern College of Engineering, Pune, India
Tel: 020 2569 6064
E-mail: [email protected]

Received Date: April 26, 2017; Accepted Date: May 08, 2017; Published Date: May 15, 2017

Citation: Rujuta O, Vyavahare AJ (2017) Nuclear Segmentation of Glioblastoma Multiforme Cells by Multireference Level Set. J Bioengineer & Biomedical Sci 7: 226. doi: 10.4172/2155-9538.1000226

Copyright: © 2017 Rujuta O, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

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Abstract

Histological tissue section consists of rich information about cell type, cellular morphology, cell state and health etc. which is very important for clinical diagnosis and therapy. Automated analysis provides insights of tumor subtypes. Since tumor sections are collected from different laboratory, some issues arises called technical and biological variations. In this paper we developed an approach for nuclear segmentation on tumours histological section, which addresses problems of processing tissues at different laboratory under microscope. Eventually, the resolution is formulated in multi reference level set frame. Experimental results show performance of proposed method.

Keywords

Tumor histology sections; Laplacian of gaussian; Gaussian mixture model; Multi reference level set; Nuclear segmentation

Introduction

The histological tumor tissue section provides morphometric composition to gain insight into cellular morphology, organization, and sample tumor heterogeneity in large cohort. Tumor section represents, strong incorporation and representation to detect mitotic cell, cellular abnormality, and autoimmune responses. If tumor tissue morphology and infrastructure can be marked on a very large scale record set, then it will shows the path for composing prognostic database. The same manner genome analysis techniques identified molecular subtype. Genome-wide molecular analysis (e.g. transcriptome) has the benefits of supervised methods for data examination and pathway quality improvement, which can allow assumption generation for the prevailing techniques. Also histological stains structure are compicated to evaluate due to the biological and technical variations. They provides details understanding into tissue composition and heterogeneity in case of unusual events. The histological tissue section visualized with hematoxylin and eosin stains (H&E), called as DNA content (e.g. nuclei) and protein respectively in different variation of color. A trained pathologist can detect outline of the rich content in cellular morphology, such as cell shape, cell organization, cell state and health, and cellular production. The main issue to process with the large collection of dataset is color composition concerned to (e.g. damage) and biological variation (e.g. cell type, cell state) across tissue section while scanning under microscope. Regarding to heterogeneity in tumor architecture, nuclear color in the area identified in one tissue area may be same as to cytoplasmic color in another tissue. At the same time, nuclear color density or chromatin content may vary within slide image. Therefore, image research should be supportable and strong, with respect to change in sample arrangements and tumor base within the entire slide image and across the tumor dataset. The tissue parts are scanned at either at 20X or 40X. Each image is segmented into 1k × 1k pixels for evolving. This paper includes 1) pre-processing for edge detection.

Related Work

The main complications in nuclear segmentation are cellular structure (technical, biological) variation or heterogeneity. The techniques for automatic detection and segmentation are based on adaptive thresholding by morphological operator [1,2], fuzzy clustering [3], level set based on gradient information and energy minimization function [4,5]. Color deconvolution after optimum thresholding [6], color and texture based hybrid analysis followed by clustering [7]. There is some application combined by above mentioned techniques. i.e., iterative radial voting [8] was used to estimate seeds for the location of nuclei and the model interaction between neighboring nuclei with multiphase level set [9,10], and in [1] an initial segmentation of nucleus with graph cut is obtained by multi scale detection and result further refined with next iteration of same method. Nuclear segmentation through color decomposition, using the same techniques developed for fluorescence microscopy [11]. Still it is a challenging to effectively address the systematical requirements of tumor histological characterization. Thresholding and clustering re-applicable for only constant chromatin content for nuclei in the image. But due to wide variation in chromatin contents problems occurs with overlapping and clumping of the nuclei, also due to tissue thickness, they cannot be segmented properly. The method proposed in [12] focus iterative radial voting on delineation of overlapping nuclei [6], but seed detection can have failed in the presence of wide variation in the nuclear size, lead to fragmentation.

Pre-Processing

Our method expressed a preprocessing construction representation of nuclear and background of an image based on nuclear response and image denoising using LoG operator nuclear channel.

These representations expressed in terms of GMM and we will then utilize a level set framework to segment foreground and background content. Finally delineated blobs are subjected to convexity constraints for partitioning clumps of nuclei.

Nuclear response LoG filter

As discussed previous, while processing tumor tissues under different laboratories under microscope, Hence local variations in an image are captured by LoG filter. LoG is used for edge detection of nuclei and background samples. We calculated MSE, SNR and PSNR for noise level. Normally these parameters are used for image compression but here we used to compare edge detection quality. If the PSNR value is getting less then it shows high edge detection capability. Given dataset scanned at 20× or 40× objective during processing each image is manually segmented and processed with LOG filter, and MSE, SNR, PSNR are collected. These tissue sections processed and scanned under different laboratories, some biological variations may occur due to addition of noise. For pre-processing of image denoising is necessary. Here MSE (mean squared error) is used to represent cumulative squared error between detected and original image and PSNR (peak signal to noise ratio) represent a measure of the peak error [13] (Table 1).

IMAGE ID SNR PSNR MSE
01 12.78 19.22 58.50
02 14.41 18.27 48.54
03 13.12 18.95 56.25
04 14.37 18.30 48.75
05 15.24 17.75 44,07
06 15.01 18.77 42.78
07 15.91 17.99 40.82
08 13.91 18.83 51.40
09 15.19 18.60 44.35
10 14.34 18.76 48.89
11 13.51 18.84 53.79
12 16.38 18.79 38.64
13 12.24 18.23 62.30

Table 1: Calculation of SNR, PSNR and MSE after processing by LoG filter.

Proposed Method

Gaussian mixture model

GMM [14] is used to approximate complicated distribution of output coming from object and background of an image, which provide general frame work to characterize heterogeneity. Statistically, a mixture model is usually defined as probability distribution of convex combination of several independent components with different probability distribution. The aim is to estimate from which source output is generated as well as parameter describing source components of GMM. With a set of N samples (image points) from n dimensional space, X={x1,…xj,…xN} in which each sample drawn from M Gaussian components. A GMM can be denoted as,

equation

where the parameters are equation such that, equation and αi is refers to probability of each component; equation μi is mean and Σi is covariance matrix i=1,..M Gaussian xj is drawn from the probability of xj coming from i-th Gaussian is:

equation

is used to estimate hidden distribution given data.

The main parameter scale for LoG filter σ determined based on dimensions of malignant and normal nuclear size at 20×. Minimum scale value for LoG filter is σmin and maximum scale value is σmax which is defined as expected range of sizes of nuclei (Table 2).

IMAGE ID PERFORMANCE MEASURE FOR σ
2.0 4.0 6.0
1 0.50 0.69 0.76
2 0.44 0.59 0.67
3 0.63 0.74 0.78
4 0.69 0.73 0.76
5 0.60 0.75 0.77
6 0.72 0.76 0.76
7 0.77 0.78 0.78
8 0.72 0.78 0.78
9 0.75 0.76 0.76
10 0.71 0.76 0.76
11 0.55 0,65 0.76
12 0.78 0.78 0.78
13 0.18 0.30 0.59

Table 2: Performance measurement after reducing cross validation error for the range of σ (min and max scale of LoG based on dimensions of malignant and normal of size of nuclei).

Multireference level set mmodel for nuclei segmentation

Energy function can be minimized as follows [15,16]:

Let

1) Fk(p) is kth feature of pixel p;

2) Pk F and Pk B are probability of fk obtained by nuclei and background, then

PK F(p)= GMMk F(p)/GMMk F(p)+ GMMk B(p) and,

PK B(p)= GMMk B(p)/GMMk F(p)+ GMMk B(p)

3)λk is weight for Riλk = <hist(Rk),

4) DI is nuclear channel

5) C denotes curve.

E=μ . Length (C) + υ Area(inside(C))

equation

equation (1)

Where, μ, υ, λF, λB, α are fixed coefficients. CF, CB are mean intensities of nuclear region i.e., nuclear foreground and background respectively, measured in (DI). The first two terms used for smoothness of nuclear boundary and size respectively. Second two terms for variation in groups of nuclear staining space for nuclear and background respectively. And last four terms related to prior knowledge [17,18].

The separation of nuclei foreground and background can be achieved by minimizing energy function via level set evolution. Regularized Heaviside function as follows:

equation (2)

where ε is regulation parameter of Heaviside function. And delta function as follows:

equation (3)

The impartial energy function can be rewritten as,

equation (4)

Energy function minimization achieved by gradient decent method, and Eular Lagrange equation for ∅ is:

equation

Basically MRL is region based active contour model and it is not sensitive to initialization. In our approach we initialize zero level set contour at the center of the image having constant radius, which evolved until the differences in spatial location between two zero level set from consecutive iterations are below the threshold (Figure 1).

bioengineering-biomedical-science-original

Figure 1: Segmentation result based on MRL approach (a) original images (b) nuclear classification via MRL.

Nuclear partition via geometric reasoning

Final part is nuclei partitioning. However after level set evolution, we got binarized image of clumps of nuclei , next step is partition them into single nucleus. Generally nulear shape is convex in shape therefore overlapping nuclei detected by concavities has to be separate out through geometric reasoning, which is explained by following steps [18]:

1) Detection of maximum point curvature: As the contour of nuclear mask extracted, derivative are computed by derivative of Gaussian.

2) Delauney triangulation : DT is applied to all points of maximum curvature for making possible grouping. The conclusion of grouping then refined by removing edges based on triangulation rules.

3) Geometric reasoning: Properties of both obtained graph and shape of object combined for edge inference.

Discussion

Our objective dataset consists of hematoxylin and eosin stained GBM tumor section samples collected from different laboratories. Those samples have some technical variation, as they are collected from different laboratories. We manually selected 13 samples and segmentation was carried out on decomposed tissue block.

Gmm component considered are 20. Other parameter setting were α=1.5, μ=0.2/timestep, λ=0.05, and σ{2.0,4.0,6.0}, in which determination of σ based on dimensions of malignant and normal nuclear size at 20×, and all other parameter selected to minimize the cross validation error. The algorithm implemented in Matlab 2014.

Conclusion and Future Work

System can better characterize with variation in data, thus much robust and effective. The LoG filter response gives edge detection information hence background contents are excluded which leads to increase in precision but the drawback of it is the tiny fragments are also indicated. Segmentation performance is indicated by Table 3. Our future work will focus on improving nuclear segmentation by withdrawing the drawbacks and evaluating the method on other remaining tumor types.

IMAGE ID No. of cells detected Correctly Segmented Precision
1 713 165 0.92
2 1104 265 0.94
3 1463 341 0.93
4 1124 238 0.85
5 1528 337 0.88
6 748 170 0.90
7 1583 367 0.92
8 2143 398 0.74
9 1546 283 0.73
10 1728 367 0.84
11 602 113 0.75
12 1694 401 0.94
13 744 168 0.90

Table 3: Summary of segmentation performance data for sample images.

Acknowledgement

The author wish to thank Prof. A.J.Vyavahare for her valuable guidance.

References

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