137 research outputs found
Local Appearance Knowledge and Shape Variation Models for Muscle Segmentation
In this report, we present a novel prior knowledge representation of shape variation using diffusion wavelets and applied for medical image segmentation. One of the major advantage of our approach is that it can reflect arbitrary and continuous interdependencies in the training data. In contrast to state-of-the-art methods our framework during the learning stage optimizes the coefficients as well as the number and the position of landmarks using geometric (reconstructed surface) constraints. Saliency is encoded in the model and segmentation is expressed through the extraction of the corresponding features in a new data-set. The resulting paradigm supports hierarchies both in the model and the search space, can encode complex geometric and photometric dependencies of the structure of interest, and can deal with arbitrary topologies. In another hand, our report deals with a different model search methodology where we apply an approach related to active feature models; the location of landmarks is updated iteratively, using local features, and the canonical correlation analysis. We report promising results on two challenging medical data sets, that illustrate the potential of our method
Atlas-based segmentation of neck muscles from MRI for the characterisation of Whiplash Associated Disorder
Whiplash-associated disorder (WAD) is a commonly occurring injury that often results from neck trauma suffered in car accidents. However the cause of the condition is still unknown and there is no definitive clinical test for the presence of the condition. Researchers have begun to analyze the size of neck muscles and the presence of fatty infiltrates to help understand WAD. However this analysis requires a high precision delineation of neck muscles which is very challenging due to a lack of distinctive features in neck magnetic resonance imaging (MRI). This paper presents a novel atlas-based neck muscle segmentation method which employs discrete cosine-based elastic registration with affine initialization. Our algorithm shows promising results based on clinical data with an average Dice similarity coefficient (DSC) of 0.84±0.0004
Biomedical Sensing and Imaging
This book mainly deals with recent advances in biomedical sensing and imaging. More recently, wearable/smart biosensors and devices, which facilitate diagnostics in a non-clinical setting, have become a hot topic. Combined with machine learning and artificial intelligence, they could revolutionize the biomedical diagnostic field. The aim of this book is to provide a research forum in biomedical sensing and imaging and extend the scientific frontier of this very important and significant biomedical endeavor
Comparison of peripheral quantitative computed tomography and magnetic resonance imaging for tissue characterisation in the gastrocnemius muscle
of the calf muscles. Magnetic resonance imaging (MRI) and ultrasound (US) are the
medical imaging modalities that are usually used to assess such injuries.
Texture analysis is a digital image processing technique that quantifies the
relationship between pixel intensities (grey levels) and pixel positions. Texture can
reveal valuable information that cannot be perceived by the naked eye. Dedicated
image processing software is required to extract texture parameters. Texture analysis
has been implemented for medical imaging modalities such as MRI, US and
computed tomography (CT) for the evaluation sports muscle injury.
Peripheral quantitative tomography (pQCT) is an adaptation of conventional CT. In
this project, texture analysis was implemented on MRI and pQCT images of the
gastrocnemius muscle (GM). MRI is an expensive technique that requires specialised
facilities. Conversely, pQCT utilises a small-bore, low-dose X-ray scanner, which is
portable and less costly than MRI. It has traditionally been used mainly for bone
analysis. The aim of this study was to assess the suitability of pQCT for GM tissue
characterisation using texture analysis compared with MRI. The study is novel in that
it is the first to apply texture analysis to GM images using pQCT
Texture analysis was done on image data acquired from MRI (GE, 1.5T) and pQCT
(Stratec XCT 2000) in a group of healthy human subjects and an injured subject. A
water phantom was also scanned with pQCT. An existing standard imaging protocol
was observed for MRI acquisition, while pQCT image acquisition parameters were
explored and optimised to yield a standard protocol.
The pQCT scanner was shown to be capable of acquiring calf muscle images and
distinguishing calf muscle boundaries. Texture parameters (grey level, variance,
skewness, kurtosis, co-occurrence matrix, run length matrix, gradient, autoregressive
(AR) model and wavelet transform) were extracted from the acquired images. The
repeatability of these quantities for pQCT in a healthy human subject and a water
phantom was assessed by calculating the coefficient of variation (%CV). The effect
of pQCT parameters (scan speed and pixel size) was tested using multiple variate
II
analysis of variance (MANOVA). The effect of region of interest (ROI) area and
anatomical position were evaluated using simple linear regression.
The t-test was used to compare the mean values of the texture features in the right
and left leg for both MRI and pQCT in a group of healthy human subjects. Neither
MRI nor pQCT showed significant differences between the two legs for any of the
texture features. In addition, there was no significant difference between the two
modalities for the AR model and wavelet transform texture parameters. Reference
ranges for the medial head of the GM were defined for both modalities. A study of a
single injured subject revealed that the values of the AR model texture parameter fell
outside the reference ranges for both MRI and pQCT, and so the AR model was
identified as the most sensitive texture parameter for distinguishing injured from
uninjured GM.
The principal conclusion from this work is that pQCT has the potential to be used for
imaging the gastrocnemius muscle and that GM images from both MRI and pQCT
scanners can be objectively characterised by texture analysis. In addition, the autoregressive
model texture parameter may be the most appropriate for muscle
characterisation
An image processing decisional system for the Achilles tendon using ultrasound images
The Achilles Tendon (AT) is described as the largest and strongest tendon in the human body. As for any other organs in the human body, the AT is associated with some medical problems that include Achilles rupture and Achilles tendonitis. AT rupture affects about 1 in 5,000 people worldwide. Additionally, AT is seen in about 10 percent of the patients involved in sports activities. Today, ultrasound imaging plays a crucial role in medical imaging technologies. It is portable, non-invasive, free of radiation risks, relatively inexpensive and capable of taking real-time images. There is a lack of research that looks into the early detection and diagnosis of AT abnormalities from ultrasound images. This motivated the researcher to build a complete system which enables one to crop, denoise, enhance, extract the important features and classify AT ultrasound images. The proposed application focuses on developing an automated system platform. Generally, systems for analysing ultrasound images involve four stages, pre-processing, segmentation, feature extraction and classification. To produce the best results for classifying the AT, SRAD, CLAHE, GLCM, GLRLM, KPCA algorithms have been used. This was followed by the use of different standard and ensemble classifiers trained and tested using the dataset samples and reduced features to categorize the AT images into normal or abnormal. Various classifiers have been adopted in this research to improve the classification accuracy. To build an image decisional system, a 57 AT ultrasound images has been collected. These images were used in three different approaches where the Region of Interest (ROI) position and size are located differently. To avoid the imbalanced misleading metrics, different evaluation metrics have been adapted to compare different classifiers and evaluate the whole classification accuracy. The classification outcomes are evaluated using different metrics in order to estimate the decisional system performance. A high accuracy of 83% was achieved during the classification process. Most of the ensemble classifies worked better than the standard classifiers in all the three ROI approaches. The research aim was achieved and accomplished by building an image processing decisional system for the AT ultrasound images. This system can distinguish between normal and abnormal AT ultrasound images. In this decisional system, AT images were improved and enhanced to achieve a high accuracy of classification without any user intervention
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