Research
I am pursuing a PhD degree in the Department of Electrical and Computer Engineering (ECE) at Texas Tech University (TTU) and working as a Research Assistant at Applied Vision Lab (AVL) supervised by Dr. Hamed Sari-Sarraf. My research experience spans a period of five years during which I have published a number of peer reviewed articles, contributed to grant proposals, and in addition, developed and taught a course in computer vision at the ECE department at TTU. A significant portion of my research is focused to efficiently address the central problem in computer vision, i.e., image segmentation. Apart from the domain of image segmentation, I also have expertise in the closely related fields of pattern recognition, machine learning and bioinformatics.
Double-edge Detection in Radiographic Lumbar Vertebrae using PODGVF 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.We propose 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.
This work is done in collaboration with CEB, National Library of Medicine(NLM) under the supervision of Dr. Hamed Sari-Sarraf, Dr. Rodney Long and Dr. Sameer Antani.
S.Kamalakannan, A. Gururajan, H. Sari-Sarraf, R. Long and S. Antani,
IEEE Transactions on Biomedical Engineering, Vol. 57 (6), June 2010
Graphical Processing Unit based Machine Vision System for Adaptive Stain Segmentation
We developed 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 post laundering. 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. The proposed scheme was also extended for stain segmentation on printed fabrics.
This work is done in collaboration with Fiber and Biopolymer Research Institute (FBRI) under the supervision of Dr. Hamed Sari-Sarraf and Dr. Eric Hequet.
S.Kamalakannan, A. Gururajan, M. Shahriar, M. Hill, J. Anderson, H. Sari-Sarraf and E. Hequet,
"Assessing Fabric Stain Release with a GPU Implementation of Statistical Snakes" Proc. SPIE (Electronic Imaging), 2009
S.Kamalakannan, A. Gururajan, H. Sari-Sarraf and E.Hequet,
"GPU-Based Machine Vision System for Simultaneous Measurement of Shrinkage and Soil Release in Fabrics", Journal of Electronic Imaging, Vol. 19 (2), May 2010
M. Hill, S.Kamalakannan, A. Gururajan, H. Sari-Sarraf and E. Hequet,
"Dimensional Change Measurement and Stain Segmentation in Printed Fabrics", Textile Research Journal, Vol. 81(16), May 2011
Hierarchical Spine Segmentation Tool Box
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.
This work is done in collaboration with CEB, National Library of Medicine(NLM) under the supervision of Dr. Hamed Sari-Sarraf, Dr. Rodney Long and Dr. Sameer Antani.
A. Gururajan, S. Kamalakannan, M. Shahriar, and H. Sari-Sarraf,
"Analysis Tool for Digitized Cervical and Lumbar Vertebrae Images", Proc. SSIAI, IEEE Computer Society, 2008
A. Gururajan, S. Kamalakannan, H. Sari-Sarraf, R. Long and S. Antani,
"On the Creation of a Segmentation Library for Digitized Cervical and Lumbar Spine Radiographs", Computerized Medical Imaging and Graphics, Vol. 35(4), June 2011.
Detecting Biliary Dyskenesia from 3D Ultrasound Gallbladder images
Current medical analysis of proper gallbladder function relies on the measurement of volume change over time. This project seeks to replace the current method of measurement, which requires the injection of radioactive markers for imaging, with a non-invasive 3D ultrasound approach. A 3D segmentation toolbox has been built in order to segment the gallbladder and subsequently detect biliary dyskinesia.
This project was done in collaboration with Texas Tech University Health Science Center (TTUHSC) under the supervision of Dr. Hamed Sari-Sarraf
Automatic Firearm Identification from Spent Cartridge Cases
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.
This project was done as part of an Internship in Imaging, Signals, and Machine Learning (ISML) group, Oak Ridge National Laboratory (ORNL) under the supervision of Dr. Christopher J. Mann, Dr. Philip R. Bingham, Thomas P. Karnowski and Dr. Shaun S. Gleason
S.Kamalakannan, C. Mann, P. Bingham, T. Karnowski and S. Gleason,
" Automatic Firearm Class Identification from Cartridge Cases", Proc. SPIE (Electronic Imaging), 2011
Integrating Internal properties into an Interactive Segmentation Framework
This work 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 project forms a major part of my dissertation and is conducted under the supervision of Dr. Hamed Sari-Sarraf.
S.Kamalakannan, B. Bryant, H. Sari-Sarraf, R. Long, S. Antani and G.Thoma,
" Integrating Shape information into an Interactive Segmentation Framework", Proc. SPIE (Medical Imaging), 2013 (Submitted)
Machine Vision System for Tuberculosis Screening
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 towards developing such a screening system, a novel computer vision module is devloped 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 other abnormalities that may be present due to pulmonary diseases. As a first step of the algorithmic procedure, we 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.
This work is done in as part of an internship at CEB, National Library of Medicine(NLM) under the supervision of Dr. Rodney Long and Dr. Sameer Antani.
S.Kamalakannan, S. Antani, R.Long and G. Thoma,
"Customized Hybrid Level-Sets for Automatic Lung Segmentation in Chest X-ray Images", Proc. SPIE (Medical Imaging), 2013 (Submitted)
Detecting nerve gas by tracking the smoke front
This project deals with detecting nerve gas by tracking the smoke front in a video.