DocumentCode :
562800
Title :
Automatic detection of malignant cells in soft tissues from microscopic images
Author :
Arunachalam, P. ; Sasikala, M. ; Jagannathan, S.Y.
Author_Institution :
Dept. of ECE, Anna Univ., Chennai, India
fYear :
2012
fDate :
30-31 March 2012
Firstpage :
376
Lastpage :
380
Abstract :
The automatic detection of malignant cells in soft tissues with the help of medical images assists the pathologists in histopathology diagnosis to confirm cancer. This system helps pathologists to improve the accuracy, efficiency to detect malignancy and to minimize the inter-observer variation. In addition, the method may help us to analyze the morphometric and staining characteristics of the cells. The most reliable screening method, histological microscopic images from a biopsy sample is stained, based on the immunohistologically stained slide are fed to the microscope then images is acquired. The statistical texture features are extracted from microscopic images. The extracted features are fed to the radial basis function (RBF) network for classification and particularly suitable for use in an expert system that aids in the diagnosis malignancy of microscopic images. The overall accuracy of classification of the proposed approach is 83.3%.In this work, effective method of automatic detection of malignant cells in soft tissues is developed with the help of microscopic images. The advantages of this work are the time taken for detection is minimized and an early treatment for the disease is achievable.
Keywords :
cancer; expert systems; feature extraction; image classification; image texture; medical image processing; object detection; patient diagnosis; radial basis function networks; statistical analysis; cancer; cell morphometric characteristics; cell staining characteristics; classification accuracy; expert system; feature classification; feature extraction; histopathology diagnosis; immunohistologically stained slide; inter-observer variation; malignant cell detection; medical image; microscopic image; pathologist; radial basis function network; soft tissues; statistical texture feature; Biological system modeling; Biomedical imaging; Cancer; Computational modeling; Correlation; Entropy; Microscopy; artificial; malignant and nonmalignant; microscopic image; neural network; radial basis function network; statistical texture features;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advances in Engineering, Science and Management (ICAESM), 2012 International Conference on
Conference_Location :
Nagapattinam, Tamil Nadu
Print_ISBN :
978-1-4673-0213-5
Type :
conf
Filename :
6216033
Link To Document :
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