| dc.description.abstract | Abstract:Prostate cancer is one of the most
common cancers in males and one of the
significant causes of cancer mortality. Most
prostate malignancies are presently diagnosed
based on an increased PSA level, despite this
biomarker having only limited accuracy.
Prostate cancer differs from most other cancers
because it is frequently multifocal and does not
appear as a single spherical mass. The illness
progresses at different rates, and it is frequently
asymptomatic until it has gone to late stages
Multi-parametric MRI (mpMRI) has advanced
dramatically in the last 20 years, as has the
treatment of localised prostate cancer. As a
result, this research aims to develop an
algorithm to identify features based on the
Local Binary Pattern (LBP) based histogram
and Grey Level Run Length Matrix (GLRLM)
characteristics of mpMRI images, to improve
detection rate and accuracy of prostate cancer
diagnosis. Local binary patterns are texture
descriptors that have been effectively employed
as image descriptors in various applications.
Images were gathered from a public image
database to complete this work. The operator is
applied to the selected region of interest (ROI)
to generate the LBP image. Texture pattern
probability was summarised into a histogram,
and second-order statistics were obtained using
the GLRLM operator. The statistical significance
of the eleven characteristics was determined
using an independent two-sample t-test using
four features from the histogram and seven
features from the GLRLM operator. The
suggested approach yielded three favourable
outcomes in the research, which can be utilised
to identify malignant tumours from benign
tumours. The positive results include the firstorder statistics standard deviation and kurtosis
and the second-order statistic Run Length Nonuniformity (RLN). | en_US |
