A chest x-ray screening system for pulmonary pathologies such as tuberculosis (TB) is of paramount
importance due to the increasing mortality rate of patients with undiagnosed TB, especially in densely-populated
developing countries. As a first step toward developing such screening systems, this paper presents a novel computer
vision module that automatically segments the lungs from posteroanterior digital chest x-ray images. The segmentation
task is non-trivial, due to poor image contrast and occlusion of the lung region by ribs, clavicle, heart, and by non-TB
abnormalities associated with pulmonary diseases. In the proposed procedure, we first compute a lung shape model by
employing a level set based technique for registration up to a homography. Next, we use this computed mean lung shape
to initialize the level set that is based on a best fit measure obtained in a heuristically estimated search space for the
projective transform parameters. Once the level set is initialized, a suite of customized lower level image features and
higher level shape features up to a homography evolve the level set function at a lower resolution in order to achieve a
coarse segmentation of the lungs. Finally, a fine segmentation step is performed by adding additional shape variation
constraints and evolving the level set in a higher resolution. We processed the standard Japanese Society of Radiological
Technology (JSRT) dataset, comprised of 247 images, using this scheme. The promising results (92% accuracy)
demonstrate the viability and efficacy of the proposed approach.
KEYWORDS: Image segmentation, Lung, Chest imaging, Algorithm development, Detection and tracking algorithms, Stars, Medical imaging, Digital imaging, Heart, RGB color model
This paper presents a novel interactive annotation toolbox which extends a well-known user-steered segmentation
framework, namely Intelligent Scissors (IS). IS, posed as a shortest path problem, is essentially driven by lower level
image based features. All the higher level knowledge about the problem domain is obtained from the user through mouse clicks. The proposed work integrates one higher level feature, namely shape up to a rigid transform, into the IS
framework, thus reducing the burden on the user and the subjectivity involved in the annotation procedure, especially
during instances of occlusions, broken edges, noise and spurious boundaries. The above mentioned scenarios are
commonplace in medical image annotation applications and, hence, such a tool will be of immense help to the medical
community. As a first step, an offline training procedure is performed in which a mean shape and the corresponding
shape variance is computed by registering training shapes up to a rigid transform in a level-set framework. The user
starts the interactive segmentation procedure by providing a training segment, which is a part of the target boundary. A partial shape matching scheme based on a scale-invariant curvature signature is employed in order to extract shape
correspondences and subsequently predict the shape of the unsegmented target boundary. A ‘zone of confidence’ is
generated for the predicted boundary to accommodate shape variations. The method is evaluated on segmentation of
digital chest x-ray images for lung annotation which is a crucial step in developing algorithms for screening Tuberculosis.
We present a machine vision system for automatic identification of the class of firearms by extracting and analyzing two
significant properties from spent cartridge cases, namely the Firing Pin Impression (FPI) and the Firing Pin Aperture
Outline (FPAO). Within the framework of the proposed machine vision system, a white light interferometer is employed
to image the head of the spent cartridge cases. As a first step of the algorithmic procedure, the Primer Surface Area
(PSA) is detected using a circular Hough transform. Once the PSA is detected, a customized statistical region-based
parametric active contour model is initialized around the center of the PSA and evolved to segment the FPI.
Subsequently, the scaled version of the segmented FPI is used to initialize a customized Mumford-Shah based level set
model in order to segment the FPAO. Once the shapes of FPI and FPAO are extracted, a shape-based level set method is
used in order to compare these extracted shapes to an annotated dataset of FPIs and FPAOs from varied firearm types. A
total of 74 cartridge case images non-uniformly distributed over five different firearms are processed using the
aforementioned scheme and the promising nature of the results (95% classification accuracy) demonstrate the efficacy of
the proposed approach.
We present a machine vision system for simultaneous and objective evaluation of two important functional attributes of a fabric, namely, soil release and shrinkage. Soil release corresponds to the efficacy of the fabric in releasing stains after laundering and shrinkage essentially quantifies the dimensional changes in the fabric postlaundering. Within the framework of the proposed machine vision scheme, the samples are prepared using a prescribed procedure and subsequently digitized using a commercially available off-the-shelf scanner. Shrinkage measurements in the lengthwise and widthwise directions are obtained by detecting and measuring the distance between two pairs of appropriately placed markers. In addition, these shrinkage markers help in producing estimates of the location of the center of the stain on the fabric image. Using this information, a customized adaptive statistical snake is initialized, which evolves based on region statistics to segment the stain. Once the stain is localized, appropriate measurements can be extracted from the stain and the background image that can help in objectively quantifying stain release. In addition, the statistical snakes algorithm has been parallelized on a graphical processing unit, which allows for rapid evolution of multiple snakes. This, in turn, translates to the fact that multiple stains can be detected and segmented in a computationally efficient fashion. Finally, the aforementioned scheme is validated on a sizeable set of fabric images and the promising nature of the results help in establishing the efficacy of the proposed approach.
Stain release is the degree to which a stained substrate approaches its original unsoiled appearance as a result of care
procedure. Stain release has a significant impact on the pricing of the fabric and, hence, needs to be quantified in an
objective manner. In this paper, an automatic approach for the objective assessment of fabric stain release that utilizes
region-based statistical snakes, is presented. This deformable contour approach employs a pressure energy term in the
parametric snake model in conjunction with statistical information (hence, statistical snakes) extracted from the image to
segment the stain and subsequently assign a stain release grade. This algorithm has been parallelized on a General
Purpose Graphical Processing Unit (GPGPU) for accelerated and simultaneous segmentation of multiple stains on a
fabric. The computational power of the GPGPU is attributed to its hardware and software architecture, which enables
multiple and identical snake kernels to be processed in parallel on several streaming processors. The detection and
segmentation results of this machine vision scheme are illustrated as part of the validation study. These results establish
the efficacy of the proposed approach in producing accurate results in a repeatable manner. In addition, this paper
presents a comparison between the benchmarking results for the algorithm on the CPU and the GPGPU.
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