Fine Grained Classification of Mammographic Lesions using Pixel N-grams
Breast cancer is the most common type of cancer
worldwide. Early diagnosis of breast cancer can result in
better treatment options increasing the survival chances of a
patient. Automated or computer aided detection of breast
cancer is applied in order to improve the accuracy and
turnover time. However, the accuracy of automated detection
systems can still be improved. Most of the efforts in the
computer aided detection systems classify the images into
cancerous and non-cancerous categories. The aim of this paper
is to classify the mammographic lesions into three categories
namely circumscribed, speculation and normal. The novel
Pixel N-gram features have been used for classification of
these lesions. Pixel N-grams are originated from character Ngram
concept of text categorization. Classification
performance is noted in order to analyse the effect of
increasing N and effect of using different classifiers (MLP,
SVM and KNN). It was observed that the classification
performance increases with increase in N and then starts
decreasing again. Moreover, classification performance
achieved using MLP classifier was better than the performance
using SVM or KNN classifiers.
mortality," Salud publica de Mexico, vol. 51, pp. s141-s146, 2009.
 S. Malvia, S. A. Bagadi, U. S. Dubey, and S. Saxena,
"Epidemiology of breast cancer in Indian women," Asia Pacific
Journal of Clinical Oncology, 2017.
 P. Náther, "N-gram based Text Categorization," Lomonosov
Moscow State Univ, 2005.
 I. Kanaris, K. Kanaris, I. Houvardas, and E. Stamatatos, "Words
versus character n-grams for anti-spam filtering," International
Journal on Artificial Intelligence Tools, vol. 16, no. 06, pp. 1047-
 P. Kulkarni, A. Stranieri, S. Kulkarni, J. Ugon, and M. Mittal,
"Visual character n-grams for classification and retrieval of
radiological images," The International Journal of Multimedia &
Its Applications, vol. 6, no. 2, p. 35, 2014.
 P. Kulkarni, A. Stranieri, S. Kulkarni, J. Ugon, and M. Mittal,
"Hybrid Technique Based On Ngram And Neural Networks For
Classification Of Mammographic Images," in Second International
Conference on Signal, Image Processing and Pattern Recognition,
2014, pp. 297-306.
 K. Bovis and S. Singh, "Detection of masses in mammograms
using texture features," in Proceedings of 15th International
Conference on Pattern Recognition, 2000, vol. 2, pp. 267-270:
 A. M. Khuzi, R. Besar, W. W. Zaki, and N. Ahmad, "Identification
of masses in digital mammogram using gray level co-occurrence
matrices," Biomedical imaging and intervention journal, vol. 5, no.
 S. Beura, B. Majhi, and R. Dash, "Mammogram classification
using two dimensional discrete wavelet transform and gray-level
co-occurrence matrix for detection of breast cancer,"
Neurocomputing, vol. 154, pp. 1-14, 2015.
 S. V. da Rocha, G. B. Junior, A. C. Silva, A. C. de Paiva, and M.
Gattass, "Texture analysis of masses malignant in mammograms
images using a combined approach of diversity index and local
binary patterns distribution," Expert Systems with Applications,
vol. 66, pp. 7-19, 2016.
 M. Hussain, S. Khan, G. Muhammad, M. Berbar, and G. Bebis,
"Mass detection in digital mammograms using gabor filter bank,"
 T. Mu, A. K. Nandi, and R. M. Rangayyan, "Classification of
breast masses using selected shape, edge-sharpness, and texture
features with linear and kernel-based classifiers," Journal of Digital
Imaging, vol. 21, no. 2, pp. 153-169, 2008.
 Y. Zhang, N. Tomuro, J. Furst, and D. S. Raicu, "Building an
ensemble system for diagnosing masses in mammograms,"
International Journal of Computer Assisted Radiology and Surgery,
vol. 7, no. 2, pp. 323-329, 2012.
 Y. Li, H. Chen, G. K. Rohde, C. Yao, and L. Cheng, "Texton
analysis for mass classification in mammograms," Pattern
Recognition Letters, vol. 52, pp. 87-93, 2015.
 P. Kulkami, A. Stranieri, and J. Ugon, "Texture image
classification using pixel N-grams," in IEEE International
Conference on Signal and Image Processing (ICSIP), 2016, pp.
 P. Kulkarni, A. Stranieri, and J. Ugon, Pixel N-grams: Size,
Location and Resolution Invariance for Shape Classification
International Journal of Science Engineering and Management
 P. Kulkarni, Analysis and Comparison of Co-occurrence Matrix
and Pixel N-gram Features for Mammographic Images.
International Conference on Communication and
Computing(2015), Banglore, India, 7-14
 J. Suckling et al., "The mammographic image analysis society
digital mammogram database," in Exerpta Medica. International
Congress Series, 1994, vol. 1069, pp. 375-378.
 I. Bankman, Handbook of medical image processing and analysis.
academic press, 2008.
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