Academic Journal
Bioinformatics-inspired binary image correlation: application to bio-/medical-images, microsarrays, finger-prints and signature classifications
| Τίτλος: | Bioinformatics-inspired binary image correlation: application to bio-/medical-images, microsarrays, finger-prints and signature classifications |
|---|---|
| Συνεισφορές: | Pappusetty, Deepti, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science |
| Στοιχεία εκδότη: | Florida Atlantic University |
| Συλλογή: | FAU Digital Collections (Florida Atlantic University Digital Library) |
| Θεματικοί όροι: | Bioinformatics--Statistical methods, Diagnostic imaging--Digital techniques, Image processing--Digital techniques, Pattern perception--Data processing, DNA microarrays |
| Περιγραφή: | The efforts addressed in this thesis refer to assaying the extent of local features in 2D-images for the purpose of recognition and classification. It is based on comparing a test-image against a template in binary format. It is a bioinformatics-inspired approach pursued and presented as deliverables of this thesis as summarized below: 1. By applying the so-called 'Smith-Waterman (SW) local alignment' and 'Needleman-Wunsch (NW) global alignment' approaches of bioinformatics, a test 2D-image in binary format is compared against a reference image so as to recognize the differential features that reside locally in the images being compared 2. SW and NW algorithms based binary comparison involves conversion of one-dimensional sequence alignment procedure (indicated traditionally for molecular sequence comparison adopted in bioinformatics) to 2D-image matrix 3. Relevant algorithms specific to computations are implemented as MatLabTM codes 4. Test-images considered are: Real-world bio-/medical-images, synthetic images, microarrays, biometric finger prints (thumb-impressions) and handwritten signatures. Based on the results, conclusions are enumerated and inferences are made with directions for future studies. ; by Deepti Pappusetty. ; Thesis (M.S.C.S.)--Florida Atlantic University, 2011. ; Includes bibliography. ; Electronic reproduction. Boca Raton, Fla., 2011. Mode of access: World Wide Web. |
| Τύπος εγγράφου: | text |
| Περιγραφή αρχείου: | Electronic Thesis or Dissertation; xii, 122 p. : ill. (some col.); electronic |
| Γλώσσα: | English |
| Relation: | http://purl.flvc.org/FAU/3333052; 777953747; 3333052; FADT3333052; fau:3795 |
| Διαθεσιμότητα: | http://purl.flvc.org/FAU/3333052 https://fau.digital.flvc.org/islandora/object/fau%3A3795/datastream/TN/view/Bioinformatics-inspired%20binary%20image%20correlation.jpg |
| Rights: | http://rightsstatements.org/vocab/InC/1.0/ |
| Αριθμός Καταχώρησης: | edsbas.A428A301 |
| Βάση Δεδομένων: | BASE |
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