A comparative study on manual and automatic slice-to-volume registration of CT images

In order to assess the clinical relevance of a slice-to-volume registration algorithm, this technique was compared to manual registration. Reformatted images obtained from a diagnostic CT examination of the lower abdomen were reviewed and manually registered by 41 individuals. The results were refined by the algorithm. Furthermore, a fully automatic registration of the single slices to the whole CT examination, without manual initialization, was also performed. The manual registration error for rotation and translation was found to be 2.7+/-2.8 degrees and 4.0+/-2.5 mm. The automated registration algorithm significantly reduced the registration error to 1.6+/-2.6 degrees and 1.3+/-1.6 mm (p = 0.01). In 3 of 41 (7.3%) registration cases, the automated registration algorithm failed completely. On average, the time required for manual registration was 213+/-197 s; automatic registration took 82+/-15 s. Registration was also performed without any human interaction. The resulting registration error of the algorithm without manual pre-registration was found to be 2.9+/-2.9 degrees and 1.1+/-0.2 mm. Here, a registration took 91+/-6 s, on average. Overall, the automated registration algorithm improved the accuracy of manual registration by 59% in rotation and 325% in translation. The absolute values are well within a clinically relevant range.

Automatic quantitation of multiple sclerosis lesions on MR images

A two-pronged approach for the automatic quantitation of multiple sclerosis (MS) lesions on magnetic resonance (MR) images has been developed. This method includes the design and use of a pulse sequence for improved lesion-to-tissue contrast (LTC) and seeks to identify and minimize the sources of false lesion classifications in segmented images. The new pulse sequence, referred to as AFFIRMATIVE (Attenuation of Fluid by Fast Inversion Recovery with MAgnetization Transfer Imaging with Variable Echoes), improves the LTC, relative to spin-echo images, by combining Fluid-Attenuated Inversion Recovery (FLAIR) and Magnetization Transfer Contrast (MTC). In addition to acquiring fast FLAIR/MTC images, the AFFIRMATIVE sequence simultaneously acquires fast spin-echo (FSE) images for spatial registration of images, which is necessary for accurate lesion quantitation. Flow has been found to be a primary source of false lesion classifications. Therefore, an imaging protocol and reconstruction methods are developed to generate "flow images" which depict both coherent (vascular) and incoherent (CSF) flow. An automatic technique is designed for the removal of extra-meningeal tissues, since these are known to be sources of false lesion classifications. A retrospective, three-dimensional (3D) registration algorithm is implemented to correct for patient movement which may have occurred between AFFIRMATIVE and flow imaging scans. Following application of these pre-processing steps, images are segmented into white matter, gray matter, cerebrospinal fluid, and MS lesions based on AFFIRMATIVE and flow images using an automatic algorithm. All algorithms are seamlessly integrated into a single MR image analysis software package. Lesion quantitation has been performed on images from 15 patient volunteers. The total processing time is less than two hours per patient on a SPARCstation 20. The automated nature of this approach should provide an objective means of monitoring the progression, stabilization, and/or regression of MS lesions in large-scale, multi-center clinical trials. ^

Automatic estimation of noise parameters in Fourier-domain optical coherence tomography cross sectional images using statistical information

We present an application and sample independent method for the automatic discrimination of noise and signal in optical coherence tomography Bscans. The proposed algorithm models the observed noise probabilistically and allows for a dynamic determination of image noise parameters and the choice of appropriate image rendering parameters. This overcomes the observer variability and the need for a priori information about the content of sample images, both of which are challenging to estimate systematically with current systems. As such, our approach has the advantage of automatically determining crucial parameters for evaluating rendered image quality in a systematic and task independent way. We tested our algorithm on data from four different biological and nonbiological samples (index finger, lemon slices, sticky tape, and detector cards) acquired with three different experimental spectral domain optical coherence tomography (OCT) measurement systems including a swept source OCT. The results are compared to parameters determined manually by four experienced OCT users. Overall, our algorithm works reliably regardless of which system and sample are used and estimates noise parameters in all cases within the confidence interval of those found by observers.

Fully Automatic Segmentation of Hip CT Images

Automatic segmentation of the hip joint with pelvis and proximal femur surfaces from CT images is essential for orthopedic diagnosis and surgery. It remains challenging due to the narrowness of hip joint space, where the adjacent surfaces of acetabulum and femoral head are hardly distinguished from each other. This chapter presents a fully automatic method to segment pelvic and proximal femoral surfaces from hip CT images. A coarse-to-fine strategy was proposed to combine multi-atlas segmentation with graph-based surface detection. The multi-atlas segmentation step seeks to coarsely extract the entire hip joint region. It uses automatically detected anatomical landmarks to initialize and select the atlas and accelerate the segmentation. The graph based surface detection is to refine the coarsely segmented hip joint region. It aims at completely and efficiently separate the adjacent surfaces of the acetabulum and the femoral head while preserving the hip joint structure. The proposed strategy was evaluated on 30 hip CT images and provided an average accuracy of 0.55, 0.54, and 0.50 mm for segmenting the pelvis, the left and right proximal femurs, respectively.

Automatic assessment of time-resolved OCT images for selective retina therapy

Purpose In recent years, selective retina laser treatment (SRT), a sub-threshold therapy method, avoids widespread damage to all retinal layers by targeting only a few. While these methods facilitate faster healing, their lack of visual feedback during treatment represents a considerable shortcoming as induced lesions remain invisible with conventional imaging and make clinical use challenging. To overcome this, we present a new strategy to provide location-specific and contact-free automatic feedback of SRT laser applications. Methods We leverage time-resolved optical coherence tomography (OCT) to provide informative feedback to clinicians on outcomes of location-specific treatment. By coupling an OCT system to SRT treatment laser, we visualize structural changes in the retinal layers as they occur via time-resolved depth images. We then propose a novel strategy for automatic assessment of such time-resolved OCT images. To achieve this, we introduce novel image features for this task that when combined with standard machine learning classifiers yield excellent treatment outcome classification capabilities. Results Our approach was evaluated on both ex vivo porcine eyes and human patients in a clinical setting, yielding performances above 95 % accuracy for predicting patient treatment outcomes. In addition, we show that accurate outcomes for human patients can be estimated even when our method is trained using only ex vivo porcine data. Conclusion The proposed technique presents a much needed strategy toward noninvasive, safe, reliable, and repeatable SRT applications. These results are encouraging for the broader use of new treatment options for neovascularization-based retinal pathologies.

Automatic segmentation of relevant textures in agricultural images

One important issue emerging strongly in agriculture is related with the automatization of tasks, where the optical sensors play an important role. They provide images that must be conveniently processed. The most relevantimage processing procedures require the identification of green plants, in our experiments they come from barley and corn crops including weeds, so that some types of action can be carried out, including site-specific treatments with chemical products or mechanical manipulations. Also the identification of textures belonging to the soil could be useful to know some variables, such as humidity, smoothness or any others. Finally, from the point of view of the autonomous robot navigation, where the robot is equipped with the imaging system, some times it is convenient to know not only the soil information and the plants growing in the soil but also additional information supplied by global references based on specific areas. This implies that the images to be processed contain textures of three main types to be identified: green plants, soil and sky if any. This paper proposes a new automatic approach for segmenting these main textures and also to refine the identification of sub-textures inside the main ones. Concerning the green identification, we propose a new approach that exploits the performance of existing strategies by combining them. The combination takes into account the relevance of the information provided by each strategy based on the intensity variability. This makes an important contribution. The combination of thresholding approaches, for segmenting the soil and the sky, makes the second contribution; finally the adjusting of the supervised fuzzy clustering approach for identifying sub-textures automatically, makes the third finding. The performance of the method allows to verify its viability for automatic tasks in agriculture based on image processing

Automatic segmentation of abdominal aortic aneurysm from medical images based on active shape models and texture models

A semi-automatic segmentation algorithm for abdominal aortic aneurysms (AAA), and based on Active Shape Models (ASM) and texture models, is presented in this work. The texture information is provided by a set of four 3D magnetic resonance (MR) images, composed of axial slices of the abdomen, where lumen, wall and intraluminal thrombus (ILT) are visible. Due to the reduced number of images in the MRI training set, an ASM and a custom texture model based on border intensity statistics are constructed. For the same reason the shape is characterized from 35-computed tomography angiography (CTA) images set so the shape variations are better represented. For the evaluation, leave-one-out experiments have been held over the four MRI set.

An automatic method of above-water iceberg 3-D profiling based on autonomous surface craft (ASC) using surface images and LIDAR

This thesis reports on a novel method to build a 3-D model of the above-water portion of icebergs using surface imaging. The goal is to work towards the automation of iceberg surveys, allowing an Autonomous Surface Craft (ASC) to acquire shape and size information. After collecting data and images, the core software algorithm is made up of three parts: occluding contour finding, volume intersection, and parameter estimation. A software module is designed that could be used on the ASC to perform automatic and fast processing of above-water surface image data to determine iceberg shape and size measurement and determination. The resolution of the method is calculated using data from the iceberg database of the Program of Energy Research and Development (PERD). The method was investigated using data from field trials conducted through the summer of 2014 by surveying 8 icebergs during 3 expeditions. The results were analyzed to determine iceberg characteristics. Limitations of this method are addressed including its accuracy. Surface imaging system and LIDAR system are developed to profile the above-water iceberg in 2015.

Automatic segmentation of choroidal thickness in optical coherence tomography

The assessment of choroidal thickness from optical coherence tomography (OCT) images of the human choroid is an important clinical and research task, since it provides valuable information regarding the eye’s normal anatomy and physiology, and changes associated with various eye diseases and the development of refractive error. Due to the time consuming and subjective nature of manual image analysis, there is a need for the development of reliable objective automated methods of image segmentation to derive choroidal thickness measures. However, the detection of the two boundaries which delineate the choroid is a complicated and challenging task, in particular the detection of the outer choroidal boundary, due to a number of issues including: (i) the vascular ocular tissue is non-uniform and rich in non-homogeneous features, and (ii) the boundary can have a low contrast. In this paper, an automatic segmentation technique based on graph-search theory is presented to segment the inner choroidal boundary (ICB) and the outer choroidal boundary (OCB) to obtain the choroid thickness profile from OCT images. Before the segmentation, the B-scan is pre-processed to enhance the two boundaries of interest and to minimize the artifacts produced by surrounding features. The algorithm to detect the ICB is based on a simple edge filter and a directional weighted map penalty, while the algorithm to detect the OCB is based on OCT image enhancement and a dual brightness probability gradient. The method was tested on a large data set of images from a pediatric (1083 B-scans) and an adult (90 B-scans) population, which were previously manually segmented by an experienced observer. The results demonstrate the proposed method provides robust detection of the boundaries of interest and is a useful tool to extract clinical data.

Mawson the astrobiologist rover : towards automatic recognition of stromatolites

This work aims at developing a planetary rover capable of acting as an assistant astrobiologist: making a preliminary analysis of the collected visual images that will help to make better use of the scientists time by pointing out the most interesting pieces of data. This paper focuses on the problem of detecting and recognising particular types of stromatolites. Inspired by the processes actual astrobiologists go through in the field when identifying stromatolites, the processes we investigate focus on recognising characteristics associated with biogenicity. The extraction of these characteristics is based on the analysis of geometrical structure enhanced by passing the images of stromatolites into an edge-detection filter and its Fourier Transform, revealing typical spatial frequency patterns. The proposed analysis is performed on both simulated images of stromatolite structures and images of real stromatolites taken in the field by astrobiologists.

A convolutional neural network for automatic analysis of aerial imagery

This paper introduces a new method to automate the detection of marine species in aerial imagery using a Machine Learning approach. Our proposed system has at its core, a convolutional neural network. We compare this trainable classifier to a handcrafted classifier based on color features, entropy and shape analysis. Experiments demonstrate that the convolutional neural network outperforms the handcrafted solution. We also introduce a negative training example-selection method for situations where the original training set consists of a collection of labeled images in which the objects of interest (positive examples) have been marked by a bounding box. We show that picking random rectangles from the background is not necessarily the best way to generate useful negative examples with respect to learning.

Automatic clustering of white matter fibers in brain diffusion mri with an application to genetics

To understand factors that affect brain connectivity and integrity, it is beneficial to automatically cluster white matter (WM) fibers into anatomically recognizable tracts. Whole brain tractography, based on diffusion-weighted MRI, generates vast sets of fibers throughout the brain; clustering them into consistent and recognizable bundles can be difficult as there are wide individual variations in the trajectory and shape of WM pathways. Here we introduce a novel automated tract clustering algorithm based on label fusion - a concept from traditional intensity-based segmentation. Streamline tractography generates many incorrect fibers, so our top-down approach extracts tracts consistent with known anatomy, by mapping multiple hand-labeled atlases into a new dataset. We fuse clustering results from different atlases, using a mean distance fusion scheme. We reliably extracted the major tracts from 105-gradient high angular resolution diffusion images (HARDI) of 198 young normal twins. To compute population statistics, we use a pointwise correspondence method to match, compare, and average WM tracts across subjects. We illustrate our method in a genetic study of white matter tract heritability in twins.

Automatically Recovering Geometry and Texture from Large Sets of Calibrated Images

Three-dimensional models which contain both geometry and texture have numerous applications such as urban planning, physical simulation, and virtual environments. A major focus of computer vision (and recently graphics) research is the automatic recovery of three-dimensional models from two-dimensional images. After many years of research this goal is yet to be achieved. Most practical modeling systems require substantial human input and unlike automatic systems are not scalable. This thesis presents a novel method for automatically recovering dense surface patches using large sets (1000's) of calibrated images taken from arbitrary positions within the scene. Physical instruments, such as Global Positioning System (GPS), inertial sensors, and inclinometers, are used to estimate the position and orientation of each image. Essentially, the problem is to find corresponding points in each of the images. Once a correspondence has been established, calculating its three-dimensional position is simply a matter of geometry. Long baseline images improve the accuracy. Short baseline images and the large number of images greatly simplifies the correspondence problem. The initial stage of the algorithm is completely local and scales linearly with the number of images. Subsequent stages are global in nature, exploit geometric constraints, and scale quadratically with the complexity of the underlying scene. We describe techniques for: 1) detecting and localizing surface patches; 2) refining camera calibration estimates and rejecting false positive surfels; and 3) grouping surface patches into surfaces and growing the surface along a two-dimensional manifold. We also discuss a method for producing high quality, textured three-dimensional models from these surfaces. Some of the most important characteristics of this approach are that it: 1) uses and refines noisy calibration estimates; 2) compensates for large variations in illumination; 3) tolerates significant soft occlusion (e.g. tree branches); and 4) associates, at a fundamental level, an estimated normal (i.e. no frontal-planar assumption) and texture with each surface patch.

Automatic classification of breast density

A recent trend in digital mammography is computer-aided diagnosis systems, which are computerised tools designed to assist radiologists. Most of these systems are used for the automatic detection of abnormalities. However, recent studies have shown that their sensitivity is significantly decreased as the density of the breast increases. This dependence is method specific. In this paper we propose a new approach to the classification of mammographic images according to their breast parenchymal density. Our classification uses information extracted from segmentation results and is based on the underlying breast tissue texture. Classification performance was based on a large set of digitised mammograms. Evaluation involves different classifiers and uses a leave-one-out methodology. Results demonstrate the feasibility of estimating breast density using image processing and analysis techniques