The efficient framework for estimation of MFO includes three steps: 1) image enhancement, 2) extraction of object regions, and 3) estimation of MFO.
Image enhancement using MVEF algorithm
Sonograms are usually affected by speckle noises, which hinder the analysis of musculoskeletal geometry. Taking into account the fact that the fiber in sonograms are tubular and include coherent orientation tendencies, we apply MVEF to enhance the sonograms before the line angle detection. The MVEF method is based on the second order local structure, with excellent noise and background suppression performance [31]. The method includes three steps: the Hessian matrix estimation (including the choice of Gaussian kernels), computation of eigenvector for each scale and processing for the maximum vesselness response. More details can be found in [31].
Extraction of object regions
In this study, objects to be extracted from sonograms are regions which may represent long and thin muscle fibers. Since fibers, both aponeuroses and other fascia structures, have higher intensity than the background in a sonogram, a straightforward approach to find the potential regions of fibers is to apply a threshold on the enhanced image, so that the pixels whose value is greater than the threshold are regarded as the pixels located in the potential object regions. In this paper, the Otsu’s method is employed to get the optimal threshold [32]. This will result in a binary map, I
map
, where the white components represent the candidate regions for muscle fibers.
Estimation of MFO
According to our observations, shapes of the interested object regions could vary and be divided into three different patterns. RA: regions which are long and thin, where each of them represents one major muscle fiber. RB: regions which are long but have branches because of adherence of two or several muscle fibers. RC: regions which are short and possibly from a 'broken’ line caused by partial imaging one single muscle fiber.
Orientations of RA and RC would be calculated as the angle between the y-axis and major axis of ellipse that has the same normalized second central moments as the region. Angles of RB would be calculated using Hough transform (HT).
Specifically speaking, 3 shape measures for the region, aspect ratio Ar, width ω and length L, will be used for classification of RA, RB and RC. In this study, L and ω are calculated respectively as the length of the major and minor axis of the ellipse that has the same normalized second central moments as the region, and the aspect ratio is defined as L/ω.
The procedures of the proposed framework for MFO estimation on the binary map are shown in Figure 1.
The proposed framework detects lines one by one in I
map
, starting with the longest region and detailed steps are:
Step 1. Setting parameters T
1
, T
2
, T
3
and N; n = 1; . (T
1
, T
2
are thresholds for shape measurements Ar and ω respectively. T
3
is the ratio of the length of the last and first detected lines. N is the upper limit of line number for each image).
Step 2. Extracting the longest region and calculating its aspect ratio Arn, width ωn and length Ln.
Step 3. If Arn > T
1
and ωn < T
2
, the orientation of the region is estimated as the angle between the y-axis and major axis of ellipse that has the same normalized second central moments as the region; Otherwise, applying HT on the region, and the line that with global maximum in the accumulator array is detected.
Step 4. Removing pixels close to the line detected in step #3 and getting the updated map . (This step can remove noises near the line and avoid the duplication in the angle measurement of RC).
Step 5. Check whether n = N or Ln < L1× T
3
. If not, n = n + 1 and repeat from step #2.
Experiments
Experiment setup for normal subjects
The proposed framework is first evaluated using the dataset from previous reports [3, 5], which were acquired on biceps and forearm muscles during various typical exercise tasks, from three healthy adult male volunteers, and the detailed experiment setup can be found in [3, 5].
Then to further evaluate the proposed framework on same muscle but from different subjects, we designed an experiment example on the sonograms of gastrocnemius. Eleven healthy male subjects (mean ± SD, age = 29.4 ± 1.8 years; body weight 65.9 ± 9.3 kg; height = 170.3 ± 5.1 cm) volunteered to participate in this experiment. No participant had a history of neuromuscular disorders, and all were aware of experimental purposes and procedures. The human subject ethical approval was obtained from the relevant committee in the Hong Kong Polytechnic University, Hung Hum, Hong Kong and informed consents were obtained from subjects prior to the experiment.
The testing position of the subject was in accordance with the Users Guide of a Norm dynamometer (Humac/Norm Testing and Rehabilitation System, Computer Sports Medicine, Inc., Massachusetts, USA). Each subject was required to put forth his maximal effort of isometric plantar flexion for a period of 3 seconds with verbal encouragement provided. The maximal voluntary contraction (MVC) was defined as the highest value of torque recorded during the entire isometric contraction. A rest of 5 min was allowed before the subject performing another MVC test. The MVC torque was then calculated by averaging the two recorded highest torque values from the two tests. The subject was instructed to generate a torque waveform in rough sinusoid shape, up to 90% of his MVC, using ankle plantar flexion movements in prone position. The torque was measured by the aforementioned dynamometer and the reason for choosing 90% MVC as the highest value was to avoid muscle fatigue.
A real-time B-mode ultrasonic scanner (EUB-8500, Hitachi Medical Corporation, Tokyo, Japan) with a 10 MHz electronic linear array probe (L53L, Hitachi Medical Corporation, Tokyo, Japan) was used to obtain ultrasound images of muscles. The long axis of the ultrasound probe was arranged parallel to the long axis of the gastrocnemius and on its muscle belly. The ultrasound probe was fixed by a custom-designed foam container with fixing straps, and a very generous amount of ultrasound gel was applied to secure acoustic coupling between the probe and skin during muscle contractions, as shown in Figure 2. The probe was adjusted to optimize the contrast of muscle fascicles in ultrasound images. Then the B-mode ultrasound images were digitized by a video card (NI PCI-1411, National Instruments, Austin, USA) at a rate of 25 frame/s for later analysis.
Surface electromyography (EMG) signals were collected from the gastrocnemius muscle using bipolar Ag-AgCl electrodes (Axon System, Inc., NY, USA), amplified by a multiple channel amplifier (RM6280 Multi-Channel Biosignal Collection and Processing System, Chengdu Instrument Company, Chengdu, China), with a gain of 2000, filtered separately by 10–400 Hz, 5–100 Hz band-pass analog filters within the amplifier, and then digitized by a 12-bit data acquisition card (NI-DAQ 6024E, National Instruments Corporation, Austin, TX, USA) with a sampling rate of 1 kHz. Ultrasound image sequences, surface EMG and torque signals were simultaneously collected and stored by a custom-made program for ultrasonic measurement of motion and elasticity (UMME, http://www.tups.org).
Totally eleven sequences of gastrocnemius ultrasound images were acquired and each includes 200 images. For each sequence, 3 frames corresponding to the torque at 0%, 45% and 90% MVC were used to evaluate the proposed framework.
Furthermore, one sequence including all 200 frames from one representative subject is used to evaluate the performance of MFO tracking, following the practice of [4, 7].
Experiment setup for an aged subject with cerebral infarction
We also tried to preliminarily evaluate the performance of the proposed framework on sonograms from other than healthy or young subjects. One male subject with unilateral limb dysfunction caused by cerebral infarction (age = 68 years; body weight = 71 kg; height = 1.72 m; right leg dysfunctional) volunteered to participate in this study. The human subject ethical approval was obtained from the relevant committee in Zhujiang Hospital, Guangzhou, China before carrying out the experiment. The subject was briefed about the procedure of experiment and written consents were collected prior to the experiment. The subject was seated with both right hip and knee angles of 90. During measurement, the subject was asked to perform plantar flextion both in left leg (normal) and in right leg (dysfunctional) with his best efforts, and the rough contraction time is about .4 seconds in one exercise. A laptop ultrasound system (SS-10, Sonostar Technologies Co., Limited, Guangzhou, China), with a 7.5 MHz electronic linear array probe, was used to obtain the ultrasonic image sequences. The long axis of the probe was arranged parallel to the long axis of the gastrocnemius muscle (as shown in Figure 3) and during the muscle contraction the probe was managed to keep on well coupled to the muscle belly with a luxury usage of gel by an experienced operator. 128 consecutive frames from each leg were captured. The caregiver of the patient had the SonoStar scanner available only, and they allowed no third-party scanner used inside their medical facilities.
Data processing
All images were cropped to remove the imaging tags and retained only the image content, and then processed using the procedures described above. All codes were written in Matlab R2010a.
Five parameters could be controlled by the users. In our experiment, T
1
, T
2
were set to 5, 30 pixels respectively. The removal width, which indicated the number of neighboring pixels to be removed along the line after a line was detected, was set to 17 pixels.
Last-to-first ratio T
3
was set to 10%. This ratio together with N (the number of lines to be detected for each image) was used as iteration termination conditions. For evaluation purpose in this article, we supposed there would be at most seven representative and interesting linear patterns visible per sonogram from the normal subjects, i.e., patterns corresponding to the skin, bone, superficial aponeurosis, deep aponeurosis and fascicles in between them. Therefore, for normal subjects in this study, we set N, the maximum line per sonogram, to 7.