Liver tissue segmentation in microscopic images using fusion color space and multiscale morphological reconstruction

SKU: PAN_IPM_084 Categories: ,
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Description

Abstract

In this project, we propose a new method for the segmentation of digital two-dimensional color liver tissue images acquired by an optical microscope from histological segments of the liver of a hamster. The sections are acquired by cutting real livers of amoebic liver abscesses. This work is part of a medical research project on studying the process of amoebiasis, which harms the human liver, being an important and dangerous disease. The new method is based on a fusion of various results of the application of color histogram and multi-scale morphological filter, which uses size and color characteristics. As a result, the images are segmented into four classes: the liver cell nuclei, the cytoplasm, stained cells, and the background. For the evaluation and for testing the reliability of the proposed segmentation algorithm, we use a set of real 2D color images of a hamster’s liver.


System Analysis

   Existing Systems

  • Principal Component Analysis
  • DCT  and shape features

Drawbacks of Exisitng System

  • High Computational error.
  •  slow training for a large feature set.
  • Less accuracy in classification

Proposed Method

  • GLCM Features
  • Fusion 
  • K-means Clustering

Advantages

  • The segmentation algorithm Proves to be simple and effective
  • The greyscale Co-occurrence matrix performed well 
  • Better texture and edge representation 
  • Segmentation provides better clustering efficiency

Block Diagram 

Liver tissue segmentation in microscopic images using fusion color space and multiscale morphological reconstruction 1
Liver tissue segmentation in microscopic images using fusion color space and multiscale morphological reconstruction 

Hardware Requirements

  • system
  • 4 GB of RAM
  • 500 GB of Hard disk

Software Requirement

  • MATLAB 2014a

REFERENCES

  • [1] Upadhyay, Y. and Wasson, V. 2014. “Analysis of Liver MR Images for Cancer Detection using Genetic Algorithm”. International Journal of Engineering Research and General Science. Vol.2, No.4, PP: 730-737.
  •  [2] Kumar, P. Bhalerao, S. 2014. “Detection of Tumor in Liver Using Image Segmentation and Registration Technique”. IOSR Journal of Electronics and Communication Engineering (IOSR-JECE). Vo.9, No.2, PP: 110-115.
  •  [3] Selle, D.; Spindler, W.; Preim, B. and Peitgen, H. O. 2000. “Mathematical Methods in Medical Imaging: Analysis of Vascular Structures for Liver Surgery Planning”. PP: 1-21. 
  • [4] Zimmer, C. and Olivo-Marin, J. C. 2005. “Coupled Parametric Active Contours”. Transactions on Pattern Analysis and Machine Intelligence. Vol.27, No.11, PP: 1838-1841.
  •  [5] Chitra, S. and Balakrishnan, G. 2012. “Comparative Study for Two-Color Spaces HSCbCr and YCbCr in Skin Color Detection”. Applied Mathematical Sciences. Vol.6, No.85, PP: 4229 – 4238.

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