Detection of Tumour Based on Breast Tissue Categorization | Chapter 04 | Advances in Applied Science and Technology Vol. 4
Background:
Breast cancer originates in breast tissue, which is made up of glands for milk
production (lobules), and the
ducts that connect
lobules to the
nipple. Breasts contain
both dense tissue (glandular tissue
and connective tissue,
together known as
fibro-glandular tissue) and
fatty tissue. Fatty tissue appears
dark on a mammogram, whereas fibro-glandular tissue appears as white. Despite
the benefits of Computer Aided Detection (CAD), false detection of breast
tumour is still a challenging issue with oncologist. A mammography is a
non-invasive screening tool that uses low energy X-rays to show the pathology
structure of breast tissue. Interpreting mammogram visually is a time consuming
process and requires
a great deal
of skill and
experience. Earlier Computer Aided
Techniques emphasis detection of tumour in breast tissues rather than
categorization of breast into Breast Imaging Report and Data System (BI-RADS)
which is the medically understandable method of reporting.
Aim:
The work centred on developing a CAD system which is capable of not only
detecting but also categorizing breast tissue in line with BI-RADS scale.
Methodology:
The acquired images were pre-processed to remove unwanted contents. Two stage
medical procedural approach was designed to categorize thetissue in breast
images into low dense (fatty) and high dense. Tumours in the low dense breasts
were segmented, and then classified as normal,
benign and malignant.
The developed system
was evaluated using
sensitivity, specificity, false
positive reduction, false negative reduction and overall performance.
Results:
The developed CAD
achieved 90.65% sensitivity,
73.59% specificity, 0.02
positive reduction, 0.04 false negative reduction and 85.71% overall performance.
Conclusion:
The false positive
reduction result obtained
shows that false detection has
been minimized as a result of categorization procedure of the breast
tissue in mammograms. This article has
reported breast tumour
detection from breast
tissue categorisation using
Medical procedural approach. The
developed system assisted
in identification of
suspicious mammograms and identification of
dense and fatty
breasts. The classification of
the segmented mammogram
into normal, benign and
malignant achieved a
better false positive
reduction (0.02) andfalse
negative reduction (0.04) and
thus provided an
improved method for
detection and classification of
breast tumour in terms of overall performance.
Author(s) Details
Temilola Morufat Adepoju
Department of Computer
Science and Engineering, Faculty of Engineering & Technology, LAUTECH,
Nigeria.
John Adedapo Ojo
Department of Electronic and
Electrical Engineering, Faculty of Engineering & Technology, LAUTECH,
Nigeria.
Elijah Olusayo Omidiora
Department of Computer
Science and Engineering, Faculty of Engineering & Technology, LAUTECH,
Nigeria.
Bello Temitope Olugbenga
Department of Radiology,
Faculty of Health Sciences, LAUTECH, Nigeria.
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