Automated Pathology Detection in the Human Brain
Author: Daniel Micallef
Supervisor: Mr Kristian Guillaumier
June 2015
The brain is the most vital organ in the human body. It consciously or subconsciously controls
each organ, muscle, or nerve. Any kind of treatment must be correctly diagnosed and intricately
planned, making sure that no eloquent areas of the brain are affected. Thus, doctors require
non-invasive imaging techniques in order to view the interior of the human body. One of the
most popular of such techniques is the Computed
Tomography (CT) modality.
Identifying and classifying pathologies from brain
CT images is a critical, yet time consuming task,
performed manually by medical experts. Such a
repetitive task leads to tiredness, making the
physician prone to human error. Notwithstanding
all the efforts done by radiologists to identify a
possible pathology in the brain, the retrospective Brain CT scan. Left: Healthy brain; Right:
error rate among radiologic examinations is Unhealthy brain with cerebral infarction.
approximately 30%1.
This gives rise to interest in Computer Aided Diagnosis (CAD). Artificial Intelligence (AI)
systems have the potential to be less susceptible to various biases and, despite their limitations,
can serve in a complementary role to human decision makers. Automating the identification
and classification of pathological areas will enable radiologists to reduce the total time taken for
diagnosing a patient, whilst lowering the possibility of erroneous patient diagnosis.
In this project, machine learning algorithms and image processing techniques are used in order
to create a generic method for the automatic detection of pathological areas in CT images of the
brain. A collection of 80 CT brain scans were collected and manually segmented. Image
processing techniques are used upon a 3-dimensional CT scan, preparing it for eventual
extraction of simple, computationally non-intensive features from every hemisphere in each 2dimensional slice of the 3-D volume. Features extracted from the images provided give rise to a
data set, from which a classification algorithm will learn.
1
The system was implemented using three different types of pathologies, and a very good recall
was recorded; however an increase in recall generally gives a negative turn to precision and
accuracy in the system. We conclude that further and more accurate pre-processing is required
for the system to extract consistent features, which, coupled with an increase in training data
will boost results, aiding human experts in detection and diagnosis of pathologies.
1
Cindy S Lee, Paul G Nagy, Sallie J Weaver, and David E Newman Toker. Cognitive and system factors contributing to
diagnostic errors in radiology. American Journal of Roentgenology, 201(3):611–617, 2013;
Daniel Micallef (292094M) | Faculty of ICT, University of Malta