Mr. Karthik Krishnan Profile

Karthik Krishnan

Principal Engineer at Philips Healthcare

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Publications (9)

Proceedings Article | 2 March 2018 Presentation + Paper

Automatic and fast CT liver segmentation using sparse ensemble with machine learned contexts

Bhavya Ajani, Aditya Bharadwaj, Karthik Krishnan

Proceedings Volume 10574, 105740L (2018) https://doi.org/10.1117/12.2292660

KEYWORDS: Liver, Image segmentation, Machine learning, Databases, Data modeling, Pathology, Statistical modeling, Feature extraction, 3D modeling, Tumors

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Proceedings Article | 24 February 2017 Presentation + Paper

Rapid perfusion quantification using Welch-Satterthwaite approximation and analytical spectral filtering

Karthik Krishnan, Kasireddy Reddy, Bhavya Ajani, Phaneendra Yalavarthy

Proceedings Volume 10133, 101330Q (2017) https://doi.org/10.1117/12.2249975

KEYWORDS: Deconvolution, Optical filters, Image analysis, Cerebral blood flow, Tissues, Blood circulation, Blood, Fourier transforms, Linear filtering, Convolution, Signal to noise ratio, Computed tomography, Imaging systems, Brain

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SPIE Journal Paper | 20 September 2016 Open Access

Tumor volume measurement error using computed tomography imaging in a phase II clinical trial in lung cancer

Claudia Henschke, David Yankelevitz, Rowena Yip, Venice Archer, Gudrun Zahlmann, Karthik Krishnan, Brian Helba, Ricardo Avila

JMI, Vol. 3, Issue 03, 035505, (September 2016) https://doi.org/10.1117/12.10.1117/1.JMI.3.3.035505

KEYWORDS: Computed tomography, Scanners, Tumors, Optical spheres, Clinical trials, Manufacturing, Sensors, Lung cancer, Calibration, Solids

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Proceedings Article | 24 February 2010 Paper

Evaluation of nonholonomic needle steering using a robotic needle driver

Emmanuel Wilson, Jeinan Ding, Craig Carignan, Karthik Krishnan, Rick Avila, Wes Turner, Dan Stoianovici, David Yankelevitz, Filip Banovac, Kevin Cleary

Proceedings Volume 7625, 762523 (2010) https://doi.org/10.1117/12.844479

KEYWORDS: Biopsy, Tissues, Lung, Visualization, Robotics, Optical inspection, Inspection, Computing systems, Instrument modeling, Visual process modeling

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Proceedings Article | 27 March 2009 Paper

Evaluation of the accuracy of deformable registration of prostate MRI for targeted prostate cancer radiotherapy

Karthik Krishnan, Rex Cheung

Proceedings Volume 7259, 725935 (2009) https://doi.org/10.1117/12.811697

KEYWORDS: Prostate, Magnetic resonance imaging, Tumors, Image registration, Radiotherapy, Image segmentation, Gold, Visualization, Distance measurement, Prostate cancer

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Proceedings Article | 28 February 2009 Paper

Automated coronary CT angiography plaque-lumen segmentation

Harvey Cline, Karthik Krishnan, Sandy Napel, Geoffrey Rubin, Wesley Turner, Ricardo Avila

Proceedings Volume 7260, 726003 (2009) https://doi.org/10.1117/12.811328

KEYWORDS: Angiography, Arteries, Point spread functions, Blood, Image segmentation, Computed tomography, Blood vessels, 3D modeling, Algorithm development, Blood circulation

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Proceedings Article | 29 April 2005 Paper

The effects of 3D region-based compression on the performance of an automatic lung nodule detection system

Benjamin Odry, Karthik Krishnan, Mariappan Nadar, David Naidich, Carol Novak, Michael Marcellin

Proceedings Volume 5747, (2005) https://doi.org/10.1117/12.595475

KEYWORDS: Lung, Image compression, Computer aided design, Image segmentation, Detection and tracking algorithms, JPEG2000, Computer aided diagnosis and therapy, Computed tomography, Medical imaging, Image processing algorithms and systems

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There is growing interest in computer aided diagnosis applications including automatic detection of lung nodules from multislice computed tomography (CT). However the increase in the number and size of CT datasets introduces high costs for data storage and transmission, and becomes an obstacle to routine clinical exam as well as hindering widespread utilization of computerized applications. We investigated the effects of 3D lossy region-based JPEG2000 standard compression on the results of an automatic lung nodule detection system. As the algorithm detects the lungs within the datasets, we used this lung segmentation to define a region of interest (ROI) where the compression should be of higher fidelity. We tested 4 methods of 3D compression: 1) default compression of the whole image, 2) default compression of segmented lungs with masking out all non-lung regions, 3) ROI-based compression as specified in the JPEG2000 standard and 4) compression where voxels in the ROI are weighted to be given emphasis in the encoding. We tested 7 compression ratios per method: 1, 4, 6, 8, 10, 20, and 30 to 1. We then evaluated our experimental CAD algorithm on 10 patients with 67 documented nodules initially identified on the decompressed data. Sensitivities and false positive rates were compared for the various compression methods and ratios. We found that region-based compression generally performs better than default compression. The sensitivity with default compression decreased from 85% at no compression to 61% at 30:1 compression, a decrease of 25%, whereas the masked compression method saw a decreased in sensitivity on only 13.5% at maximum compression. At compression levels up to 10:1, all 3 region-based compression methods had decreases in sensitivity of 7.5% or less. Detection of small nodules (< 4mm in diameter) was more affected by compression than detection of large nodules; sensitivity to calcified nodules was less affected by compression than to non-calcified nodules.

Proceedings Article | 15 April 2005 Paper

Prioritization of compressed data by tissue type using JPEG2000

Karthik Krishnan, Michael Marcellin, Ali Bilgin, Mariappan Nadar

Proceedings Volume 5748, (2005) https://doi.org/10.1117/12.594914

KEYWORDS: JPEG2000, Wavelets, Computer programming, Image compression, Tissues, Discrete wavelet transforms, Bone, Visualization, Medical imaging, Distortion

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Proceedings Article | 19 April 2004 Paper

Compression/decompression strategies for large-volume medical imagery

Karthik Krishnan, Michael Marcellin, Ali Bilgin, Mariappan Nadar

Proceedings Volume 5371, (2004) https://doi.org/10.1117/12.535650

KEYWORDS: Discrete wavelet transforms, JPEG2000, Visualization, Medical imaging, Wavelets, Distortion, Visual compression, Image compression, Data modeling, Data compression

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