Automatic Characterizations of Lumbar Multifidus Muscle and Intramuscular Fat with Fuzzy C-means based Quantization from Ultrasound Images.

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 lifetime 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 analyze 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.