Classification of breast tissues using Moran's index and Geary's coefficient as texture signatures and SVM

Authors:
Geraldo Braz Junior;Anselmo Cardoso de Paiva;Aristófanes Corrêa Silva;Alexandre Cesar Muniz de Oliveira
Affiliations:
Federal University of Maranhão - UFMA, Applied Computing Group NCA/UFMA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65085-580 São Luís, MA, Brazil;Federal University of Maranhão - UFMA, Applied Computing Group NCA/UFMA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65085-580 São Luís, MA, Brazil;Federal University of Maranhão - UFMA, Applied Computing Group NCA/UFMA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65085-580 São Luís, MA, Brazil;Federal University of Maranhão - UFMA, Applied Computing Group NCA/UFMA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, 65085-580 São Luís, MA, Brazil
Venue:
Computers in Biology and Medicine
Year:
2009

Citing 10
Cited 8

Handbook of Image and Video Processing

Handbook of Image and Video Processing
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Digital Image Processing

Digital Image Processing
A new approach to the classification of mammographic masses and normal breast tissue

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Computerized detection of breast masses in digitized mammograms

Computers in Biology and Medicine
Diagnosis of lung nodule using Moran’s index and Geary’s coefficient in computerized tomography images

Pattern Analysis & Applications
Toward breast cancer diagnosis based on automated segmentation of masses in mammograms

Pattern Recognition
A novel soft cluster neural network for the classification of suspicious areas in digital mammograms

Pattern Recognition
Semivariogram applied for classification of benign and malignant tissues in mammography

ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Semivariogram and SGLDM methods comparison for the diagnosis of solitary lung nodule

IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II

A very high performing system to discriminate tissues in mammograms as benign and malignant

Expert Systems with Applications: An International Journal
Survey on LBP based texture descriptors for image classification

Expert Systems with Applications: An International Journal
Matrix representation in pattern classification

Expert Systems with Applications: An International Journal
A simple method for improving local binary patterns by considering non-uniform patterns

Pattern Recognition
Classification of Benign and Malignant Breast Masses Based on Shape and Texture Features in Sonography Images

Journal of Medical Systems
Classification of breast tissues in mammographic images in mass and non-mass using mcintosh's diversity index and SVM

MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Mammographical mass detection and classification using Local Seed Region Growing-Spherical Wavelet Transform (LSRG-SWT) hybrid scheme

Computers in Biology and Medicine
Ensemble Classifier for Benign-Malignant Mass Classification

International Journal of Computer Vision and Image Processing

Quantified Score

Hi-index	0.01

Visualization

Abstract

Female breast cancer is the major cause of cancer-related deaths in western countries. Efforts in computer vision have been made in order to help improving the diagnostic accuracy by radiologists. In this paper, we present a methodology that uses Moran's index and Geary's coefficient measures in breast tissues extracted from mammogram images. These measures are used as input features for a support vector machine classifier with the purpose of distinguishing tissues between normal and abnormal cases as well as classifying them into benign and malignant cancerous cases. The use of both proposed techniques showed to be very promising, since we obtained an accuracy of 96.04% and Az ROC of 0.946 with Geary's coefficient and an accuracy of 99.39% and Az ROC of 1 with Moran's index to discriminate tissues in mammograms as normal or abnormal. We also obtained accuracy of 88.31% and Az ROC of 0.804 with Geary's coefficient and accuracy of 87.80% and Az ROC of 0.89 with Moran's index to discriminate tissues in mammograms as benign and malignant.