Novel methods for microscopic image processing, analysis, classification and compression

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buir.advisorÇetin, Ahmet Enis
dc.contributor.authorSuhre, Alexander
dc.date.accessioned2016-01-08T20:03:50Z
dc.date.available2016-01-08T20:03:50Z
dc.date.issued2013
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionAnkara : The Department of Electrical and Electronics Engineering and the Graduate School of Engineering and Science of Bilkent University, 2013.en_US
dc.descriptionThesis (Ph. D.) -- Bilkent University, 2013.en_US
dc.descriptionIncludes bibliographical references leaves 106-118.en_US
dc.description.abstractMicroscopic images are frequently used in medicine and molecular biology. Many interesting image processing problems arise after the initial data acquisition step, since image modalities are manifold. In this thesis, we developed several algorithms in order to handle the critical pipeline of microscopic image storage/ compression and analysis/classification more efficiently. The first step in our processing pipeline is image compression. Microscopic images are large in size (e.g. 100K-by-100K pixels), therefore finding efficient ways of compressing such data is necessary for efficient transmission, storage and evaluation. We propose an image compression scheme that uses the color content of a given image, by applying a block-adaptive color transform. Microscopic images of tissues have a very specific color palette due to the staining process they undergo before data acquisition. The proposed color transform takes advantage of this fact and can be incorporated into widely-used compression algorithms such as JPEG and JPEG 2000 without creating any overhead at the receiver due to its DPCM-like structure. We obtained peak signal-to-noise ratio gains up to 0.5 dB when comparing our method with standard JPEG. The next step in our processing pipeline is image analysis. Microscopic image processing techniques can assist in making grading and diagnosis of images reproducible and by providing useful quantitative measures for computer-aided diagnosis. To this end, we developed several novel techniques for efficient feature extraction and classification of microscopic images. We use region co-difference matrices as inputs for the classifier, which have the main advantage of yielding multiplication-free computationally efficient algorithms. The merit of the co-difference framework for performing some important tasks in signal processing is discussed. We also introduce several methods that estimate underlying probability density functions from data. We use sparsity criteria in the Fourier domain to arrive at efficient estimates. The proposed methods can be used for classification in Bayesian frameworks. We evaluated the performance of our algorithms for two image classification problems: Discriminating between different grades of follicular lymphoma, a medical condition of the lymph system, as well as differentiating several cancer cell lines from each another. Classification accuracies over two large data sets (270 images for follicular lymphoma and 280 images for cancer cell lines) were above 98%.en_US
dc.description.degreePh.D.en_US
dc.description.statementofresponsibilitySuhre, Alexanderen_US
dc.format.extentxviii, 118 leaves, graphsen_US
dc.identifier.urihttp://hdl.handle.net/11693/16946
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectImage compressionen_US
dc.subjectfeature extractionen_US
dc.subjectpattern recognitionen_US
dc.subjectkernel density estimationen_US
dc.subjectregion covariance matrixen_US
dc.subject.lccWN180 .S84 2013en_US
dc.subject.lcshDiagnostic imaging--Digital techniques.en_US
dc.subject.lcshImage processing.en_US
dc.subject.lcshImaging systems in medicine.en_US
dc.subject.lcshImage compression.en_US
dc.subject.lcshOptical pattern recognition.en_US
dc.titleNovel methods for microscopic image processing, analysis, classification and compressionen_US
dc.typeThesisen_US

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