0000000000385802

AUTHOR

Usama Pervaiz

showing 3 related works from this author

Fast PET Scan Tumor Segmentation Using Superpixels, Principal Component Analysis and K-Means Clustering

2018

Positron Emission Tomography scan images are extensively used in radiotherapy planning, clinical diagnosis, assessment of growth and treatment of a tumor. These all rely on fidelity and speed of detection and delineation algorithm. Despite intensive research, segmentation remained a challenging problem due to the diverse image content, resolution, shape, and noise. This paper presents a fast positron emission tomography tumor segmentation method in which superpixels are extracted first from the input image. Principal component analysis is then applied on the superpixels and also on their average. Distance vector of each superpixel from the average is computed in principal components coordin…

FOS: Computer and information sciencespositron emission tomographyprincipal component analysisComputer scienceComputer Vision and Pattern Recognition (cs.CV)k-meansCoordinate systemComputer Science - Computer Vision and Pattern RecognitionFOS: Physical sciences02 engineering and technologyBenchmarkQuantitative Biology - Quantitative MethodsBiochemistry Genetics and Molecular Biology (miscellaneous)030218 nuclear medicine & medical imagingsuperpixels03 medical and health sciences0302 clinical medicineStructural Biology0202 electrical engineering electronic engineering information engineeringmedicineSegmentationComputer visionTissues and Organs (q-bio.TO)Cluster analysisQuantitative Methods (q-bio.QM)Pixelmedicine.diagnostic_testbusiness.industrysegmentationk-means clusteringQuantitative Biology - Tissues and OrgansPattern recognitionPhysics - Medical PhysicsPositron emission tomographyFOS: Biological sciencesPhysics - Data Analysis Statistics and ProbabilityPrincipal component analysis020201 artificial intelligence & image processingMedical Physics (physics.med-ph)Artificial intelligenceNoise (video)businessData Analysis Statistics and Probability (physics.data-an)BiotechnologyMethods and Protocols
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Complete End-To-End Low Cost Solution To a 3D Scanning System with Integrated Turntable

2017

3D reconstruction is a technique used in computer vision which has a wide range of applications in areas like object recognition, city modelling, virtual reality, physical simulations, video games and special effects. Previously, to perform a 3D reconstruction, specialized hardwares were required. Such systems were often very expensive and was only available for industrial or research purpose. With the rise of the availability of high-quality low cost 3D sensors, it is now possible to design inexpensive complete 3D scanning systems. The objective of this work was to design an acquisition and processing system that can perform 3D scanning and reconstruction of objects seamlessly. In addition…

FOS: Computer and information sciencesbusiness.industryComputer scienceComputer Vision and Pattern Recognition (cs.CV)3D reconstructionComputer Science - Computer Vision and Pattern Recognition0211 other engineering and technologiesProcess (computing)Point cloud020207 software engineering02 engineering and technologyProcessingVirtual realitySoftware0202 electrical engineering electronic engineering information engineeringTable (database)businesscomputerComputer hardware021106 design practice & managementcomputer.programming_languageGraphical user interface
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Taxonomic Classification for Living Organisms Using Convolutional Neural Networks

2017

Taxonomic classification has a wide-range of applications such as finding out more about evolutionary history. Compared to the estimated number of organisms that nature harbors, humanity does not have a thorough comprehension of to which specific classes they belong. The classification of living organisms can be done in many machine learning techniques. However, in this study, this is performed using convolutional neural networks. Moreover, a DNA encoding technique is incorporated in the algorithm to increase performance and avoid misclassifications. The algorithm proposed outperformed the state of the art algorithms in terms of accuracy and sensitivity, which illustrates a high potential f…

0301 basic medicinelcsh:QH426-470Taxonomic classificationADNCodificació Teoria de laBiologyBioinformaticsMachine learningcomputer.software_genreDNA; genes; taxonomic classification; convolutional neural networks; encodingConvolutional neural networkArticle03 medical and health sciences0302 clinical medicineBiologia -- ClassificacióEncoding (memory)convolutional neural networksGeneticstaxonomic classificationSensitivity (control systems)genesGenetics (clinical)ta113Biology -- Classificationbusiness.industryBiological classificationCoding theoryDNAencodinglcsh:Genetics030104 developmental biologyGenes030220 oncology & carcinogenesisEncodingConvolutional neural networksArtificial intelligenceCoding theorybusinesscomputerGens
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