Sridharan Kamalakannan

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.

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. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

This paper presents a novel interactive annotation tool built on 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 for Tuberculosis.

This thesis explains, in detail, the various kinds of active contour models that have attracted the attention of many in the computer vision community in the recent years. It gives a detailed description of the energy formulations and the derivation of force equations using a calculus of variations method. These snake models are combined and customized for two applications: (1) detection of double edges in x-ray images of lumbar vertebrae using pressurized open DGVF snakes, and (2) fabric stain detection using statistical balloons. The detection of double edges in x-ray images of lumbar vertebrae is of prime importance in the assessment of injury or vertebral collapse, possibly due to osteoporosis or other spine pathology. Manual segmentation is prone to errors due to subjective judgment and, hence, computer vision methods, such as snakes, are an attractive alternative to providing an automatic means of segmenting the double edges. The proposed algorithm uses a  pressurized open model of DGVF snakes, customized to this application. This algorithm is applied to a set of over 30 lumbar images thus far, and the double-edge detection results have been deemed promising enough to set up a quantitative measurement for the assessment of injury or vertebral collapse. The goal in the second application is the automatic quantification of stain release in fabrics, which is an important property, impacting the fabrics’ pricing in the marketplace. Of course, to quantify stain release, one must first detect and segment the stains. This thesis proposes a balloon model with embedded statistical information in order to detect and segment the stains. A set of 15 stain images are used thus far to test the algorithm with near perfect detection and segmentation results.

The detection of double edges in x-ray images of lumbar vertebrae is of prime importance in the assessment of vertebral injury or collapse that may be caused by osteoporosis and other spine pathology. In addition, if the above double-edge detection process is conducted within an automatic framework, it would not only facilitate inexpensive and fast means of obtaining objective morphometric measurements on the spine, but also remove the human subjectivity involved in the morphometric analysis. This paper proposes a novel force-formulation scheme, termed as Pressurized Open Directional Gradient Vector Flow snakes, to discriminate and detect the superior and inferior double edges present in the radiographic images of the lumbar vertebrae. As part of the validation process, this algorithm is applied to a set of 100 lumbar images and the detection results are quantified using analyst-generated ground truth. The promising nature of the detection results bears testimony to the efficacy of the proposed approach