Arunkumar Gururajan

Hierarchical segmentation of cervical and lumbar vertebrae using a customized generalized Hough transform and extensions to active appearance models

The paper describes a semi-automatic segmentation method for application to cervical and lumbar X-ray images. The method consists of a three stage, coarse to fine, segmentation process utilizing the generalised Hough transform for one stage, and active appearance models for two stages. Customizations to these algorithms are introduced, and segmentation results for 273 cervical X-ray images and 262 lumbar X-ray images are presented.

Customized Hough transform for robust segmentation of cervical vertebrae from X-ray images

This paper addresses the issues involved in developing a robust segmentation technique capable of finding the location and orientation of the cervical vertebrae in X-ray images. This technique should be invariant to rotation, scale, noise, occlusions and shape variability. A customized approach, based on the generalized Hough transform (GHT), that captures shape variability and exploits shape information embedded in the accumulator structure to overcome noise and occlusions is proposed. This approach effectively finds estimates of the location and orientation of the cervical vertebrae boundaries in digitized X-ray images.

Statistical approach to unsupervised defect detection and multiscale localization in two-texture images

We present a novel statistical approach to unsupervised de- tection and localization of a chromatic defect in a uniformly textured background. The test images are probabilistically modeled using Gauss- ian mixture models, and consequently defect detection is posed as a model-order selection problem. The statistical model is estimated using a modified Expectation-Maximization algorithm that aids in faster conver- gence of the scheme. A test image is segmented only if a defective region/blob has been declared to be present, and this improves the effi- ciency of the entire scheme. This work places equal emphasis on defect localization; hence, an elaborate statistical multiscale analysis is per- formed to accurately localize the defect in the image. The underlying idea behind the multiscale approach is that segmented structures should be stable across a wide range of scales. The efficacy of the proposed approach is successfully demonstrated on a large dataset of stained fabric images. The overall detection rate of the system is found to be 92% with a specificity of 95%. All of these factors make the proposed approach attractive for implementation in online industrial applications.

Objective Evaluation of Soil Release in Fabrics

Soil release is an important attribute of fabrics that impacts their pricing on the marketplace. Currently, stain release is evaluated manually and hence its assessment tends to be subjective. This paper introduces a novel approach based on image analysis for objective evaluation of stain release in fabrics. This system has the capability to accurately detect, localize and grade stains with minimal human intervention. The grades assigned by the system are based on a digitized version of the AATCC soil release replica. The stain detection performance of the system was validated on a large dataset of 360 stain images. The system produced excellent results with a sensitivity (true positive rate) of around 93 percent and specificity (1 — false positive rate) of around 95 percent. Further, a correlation study and analysis of variance were performed using the system assigned grades and the technician assigned grades, and these initial results are very promising.

Double-Edge Detection of Radiographic Lumbar Vertebrae Images Using Pressurized Open DGVF Snakes

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.

Interactive texture segmentation via IT-SNAPS

This paper presents a new framework for an interactive image delineation technique, which we term as the Interactive Texture-Snapping System (IT-SNAPS). The uniqueness of IT-SNAPS stems from the fact that it can effectively assist the user in accurately segmenting images with complex texture, without placing undue burden on the operator. This is made possible by unobtrusive extraction of information from the user during the delineation process, which in turn, is utilized on-the-fly to adapt IT-SNAPS to the boundary being segmented. The current work details (i) the mathematical construction of IT-SNAPS, (ii) demonstration of IT-SNAPS on a heterogenous texture image, and (iii) a performance comparison with the popular existing technique of intelligent scissors.

Machine vision scheme for stain-release evaluation using Gabor filters with optimized coefficients

This paper presents an efficient and practical approach for automatic, unsupervised object detection and segmentation in two-texture images based on the concept of Gabor filter optimization. The entire process occurs within a hierarchical framework and consists of the steps of detection, coarse segmentation, and fine segmentation. In the object detection step, the image is first processed using a Gabor filter bank. Then, the histograms of the filtered responses are analyzed using the scale-space approach to predict the presence/absence of an object in the target image. If the presence of an object is reported, the proposed approach proceeds to the coarse segmentation stage, wherein the best Gabor filter (among the bank of filters) is automatically chosen, and used to segment the image into two distinct regions. Finally, in the fine segmentation step, the coefficients of the best Gabor filter (output from the previous stage) are iteratively refined in order to further fine-tune and improve the segmentation map produced by the coarse segmentation step. In the validation study, the proposed approach is applied as part of a machine vision scheme with the goal of quantifying the stain-release property of fabrics. To that end, the presented hierarchical scheme is used to detect and segment stains on a sizeable set of digitized fabric images, and the performance evaluation of the detection, coarse segmentation, and fine segmentation steps is conducted using appropriate metrics. The promising nature of these results bears testimony to the efficacy of the proposed approach.

On the creation of a segmentation library for digitized cervical and lumbar spine radiographs.

In this paper, we address the issue of computer-assisted indexing in one specific case, i.e., for the 17,000 digitized images of the spine acquired during the National Health and Nutrition Examination Survey (NHANES). The crucial step in this process is to accurately segment the cervical and lumbar spine in the radiographic images. To that end, we have implemented a unique segmentation system that consists of a suite of spine-customized automatic and semi-automatic statistical shape segmentation algorithms. Using the aforementioned system, we have developed experiments to optimally generate a library of spine segmentations, which currently include 2000 cervical and 2000 lumbar spines. This work is expected to contribute toward the creation of a biomedical Content-Based Image Retrieval system that will allow retrieval of vertebral shapes by using query by image example or query by shape example.

Graphical processing unit–based machine vision system for simultaneous measurement of shrinkage and soil release in fabrics

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.

Assessing fabric stain release with a GPU implementation of statistical snakes

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.