Background: Low Back Pain (LBP) is a common disorder involving the muscles and
bones and about half of the people experience LBP at some point of their lives. Since the social
economic cost and the recurrence rate over the life time is very high, the treatment/rehabilitation of
chronic LBP is important to physiotherapists, both for clinical and research purposes. Trunk muscles
such as the lumbar multifidi is important in spinal functions and intramuscular fat is also important
in understanding pain control and rehabilitations. However, the analysis of such muscles
and related fat require many human interventions and thus suffers from the operator subjectivity
especially when the ultrasonography is used due to its cost-effectiveness and no radioactive risk.
Aims: In this paper, we propose a fully automatic computer vision based software to compute the
thickness of the lumbar multifidi muscles and to analyse intramuscular fat distribution in that area.
Methods: The proposed system applies various image processing algorithms to enhance the intensity
contrast of the image and measure the thickness of the target muscle. Intermuscular fat analysis
is done by Fuzzy C-Means (FCM) clustering based quantization.
Results: In experiment using 50 DICOM format ultrasound images from 50 subjects, the proposed
system shows very promising result in computing the thickness of lumbar multifidi.
Conclusion: The proposed system have minimal discrepancy(less than 0.2 cm) from human expert
for 72% (36 out of 50 cases) of the given data. Also, FCM based intramuscular fat analysis looks
better than conventional histogram analysis.