INFORMATION CHANGE THE WORLD

International Journal of Information Engineering and Electronic Business(IJIEEB)

ISSN: 2074-9023 (Print), ISSN: 2074-9031 (Online)

Published By: MECS Press

IJIEEB Vol.8, No.3, May. 2016

Preparing Mammograms for Classification Task: Processing and Analysis of Mammograms

Full Text (PDF, 797KB), PP.57-66


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Author(s)

Aderonke A. Kayode, Babajide S.Afolabi, Bolanle O. Ibitoye

Index Terms

Mammograms;Classification;Abnormalities;Enhancement;Segmentation;Feature extraction

Abstract

Breast cancer is the most common cancer found in women in the world. Mammography has become indispensable for early detection of breast cancer. Radiologists interpret patients' mammograms by looking for some significant visual features for decision making. These features could have different interpretations based on expert's opinion and experience. Therefore, to solve the problem of different interpretations among experts, the use of computer in facilitating the processing and analysis of mammograms has become necessary.
This study enhanced and segmented suspicious areas on mammograms obtained from Radiology Department, Obafemi Awolowo University Teaching Hospital, Ile-Ife, Nigeria. Also, Features were extracted from the segmented region of interests in order to prepare them for classification task.
The result of implementation of enhancement algorithm used on mammograms shows all the subtle and obscure regions thereby making suspicious regions well visible which in turn helps in isolating the regions for extraction of textural features from them. Also, the result of the feature extraction shows pattern that will enable a classifier to classify these mammograms to one of normal, benign and malignant classes.

Cite This Paper

Aderonke A. Kayode, Babajide S.Afolabi, Bolanle O. Ibitoye,"Preparing Mammograms for Classification Task: Processing and Analysis of Mammograms", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.8, No.3, pp.57-66, 2016. DOI: 10.5815/ijieeb.2016.03.07

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