Automatic ROI detection and classification of the Achilles tendon ultrasound images

Ultrasound (US) imaging plays an important role in medical imaging technologies. It is widely used because of its ease of use and low cost compared to other imaging techniques. Specifically, ultrasound imaging is used in the detection of the Achilles Tendon (AT) pathologies as it detects important details. For example, US imaging is used for AT rupture that affects about 1 in 5,000 people worldwide. Decision support systems are important in medical imaging, as they assist radiologist in detecting probable diagnoses and lesions. The work presented in this paper concerns the development of a software application to detect changes in the AT ultrasound images and subsequently classify them into normal or abnormal. We propose an approach that fully automates the detection for the Region of Interest (ROI) in ultrasound AT images. The original image is divided into six blocks with 1 cm size in each direction. The blocks lie inside the vulnerable area considered as our ROI. The proposed system achieved an accuracy of 97.21%

In vivo measurement of skin surface strain and sub-surface layer deformation induced by natural tissue stretching.

Stratum corneum and epidermal layers change in terms of thickness and roughness with gender, age and anatomical site. Knowledge of the mechanical and tribological properties of skin associated with these structural changes are needed to aid in the design of exoskeletons, prostheses, orthotics, body mounted sensors used for kinematics measurements and in optimum use of wearable on-body devices. In this case study, optical coherence tomography (OCT) and digital image correlation (DIC) were combined to determine skin surface strain and sub-surface deformation behaviour of the volar forearm due to natural tissue stretching. The thickness of the epidermis together with geometry changes of the dermal-epidermal junction boundary were calculated during change in the arm angle, from flexion (90°) to full extension (180°). This posture change caused an increase in skin surface Lagrange strain, typically by 25% which induced considerable morphological changes in the upper skin layers evidenced by reduction of epidermal layer thickness (20%), flattening of the dermal-epidermal junction undulation (45-50% reduction of flatness being expressed as Ra and Rz roughness profile height change) and reduction of skin surface roughness Ra and Rz (40-50%). The newly developed method, DIC combined with OCT imaging, is a powerful, fast and non-invasive methodology to study structural skin changes in real time and the tissue response provoked by mechanical loading or stretching

An automated image analysis system for the detection of microcalcifications.

The interpretation of medical images is one of the most difficult tasks in computer vision, largely because of the high degree of variability associated with normal and abnormal appearances. This thesis introduces a systematic method for the detection of microcalcifications as one of the most important signs of early breast cancer. It involves a four step procedure. The first step is blob detection to detect regions of microcalcification size range. The second step involves a specially designed directional region growing method to find the best fitting boundaries for each blob region. A newly developed combination of classifiers is then applied to label each region as a microcalcification or background. The final processing step involves a search for the existence of clusters of microcalcifications using a hierarchical nearest mean clustering method. The contributions of the work to the field of image processing are; a new blob detection system; a novel region growing method and a theoretical framework for combining classifiers which use a combination of shared and distinct representations. Here specifically, we present a blob detection method with the capability of detecting any suspected blob of specific size range. Then a new region growing method is developed based on a unique directional growing process providing predictable behaviour for the method. The application of two discontinuity measures is considered for the extraction of two fitting boundaries representing information about the region and its local background. The information conveyed by the boundaries and their associated regions is used to compute reliable representations for labelling each blob region. The robustness of the region growing method to the choice of a starting point and to Gaussian noise is examined on real images. We demonstrate that commonly used classifiers provide reliable results in labelling the suspected regions. In spite of achieving an acceptable performance using different individual classifiers, a decision fusion rule involving a weighted combination of classifiers is developed and its performance on the problem is investigated. The combination rule is applicable when mixed mode representations (some shared and some individual features) are used. A comparative study of the individtial classifiers and also of conventional classifier combination techniques with the weighted combiner is performed on independent test sets. The results achieved with the presented algorithm are very promising and approaching a level where a clinical pilot evaluation for screening purposes would be warranted

An automated image analysis system for the detection of microcalcifications.

The interpretation of medical images is one of the most difficult tasks in computer vision, largely because of the high degree of variability associated with normal and abnormal appearances. This thesis introduces a systematic method for the detection of microcalcifications as one of the most important signs of early breast cancer. It involves a four step procedure. The first step is blob detection to detect regions of microcalcification size range. The second step involves a specially designed directional region growing method to find the best fitting boundaries for each blob region. A newly developed combination of classifiers is then applied to label each region as a microcalcification or background. The final processing step involves a search for the existence of clusters of microcalcifications using a hierarchical nearest mean clustering method. The contributions of the work to the field of image processing are; a new blob detection system; a novel region growing method and a theoretical framework for combining classifiers which use a combination of shared and distinct representations. Here specifically, we present a blob detection method with the capability of detecting any suspected blob of specific size range. Then a new region growing method is developed based on a unique directional growing process providing predictable behaviour for the method. The application of two discontinuity measures is considered for the extraction of two fitting boundaries representing information about the region and its local background. The information conveyed by the boundaries and their associated regions is used to compute reliable representations for labelling each blob region. The robustness of the region growing method to the choice of a starting point and to Gaussian noise is examined on real images. We demonstrate that commonly used classifiers provide reliable results in labelling the suspected regions. In spite of achieving an acceptable performance using different individual classifiers, a decision fusion rule involving a weighted combination of classifiers is developed and its performance on the problem is investigated. The combination rule is applicable when mixed mode representations (some shared and some individual features) are used. A comparative study of the individtial classifiers and also of conventional classifier combination techniques with the weighted combiner is performed on independent test sets. The results achieved with the presented algorithm are very promising and approaching a level where a clinical pilot evaluation for screening purposes would be warranted

Accuracy of the skin model in quantifying blood and epidermal melanin

SIGLEAvailable from British Library Document Supply Centre-DSC:8092.7029(00-4) / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Fast algorithm for blind optimization of optical systems. Statistics and methodology

Adaptive Optics (AO) improves the efficiency of the optical devices in confocal imaging systems by reducing wavefront aberrations. Aberration is caused by imperfections within the system and reduces the optical signal to noise ratio of the resultant images besides deteriorating the quality of the images. An adaptive optics system comprises a wavefront sensor and a deformable mirror (DM), is a straightforward solution to compensate for this distortion. In confocal microscopy, the wavefront sensor can be supplanted with an optimization algorithm. We have previously implemented the general simulated annealing (SA) algorithm for optimizing confocal microscopes. In this paper the modified version of the simulated annealing algorithm, fast simulated annealing (FSA) is investigated which takes less than one hundredth of the optimization time required by the general version

Spatial compounding algorithm for speckle reduction of dynamic focus OCT images

Optical coherence tomography is capable of imaging the microstructures within tissues. To preserve the transverse resolution at all imaging depths, we implement a dynamic focusing scheme. To improve the quality of images further, a simple speckle reduction scheme is employed which uses the vibration introduced by the translation stage used for axial scanning. A spatial compounding technique is developed based on co-registration followed by an averaging algorithm. We conclude that the degree of speckle reduction achieved is worth the expense of more complicated processing required. © 1989-2012 IEEE