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Wigdahl, Jeffrey (2017) Retinal Vascular Measurement Tools for Diagnostic Feature Extraction. [Tesi di dottorato]

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Abstract (inglese)

The contributions of this work are in the development of new and state of the art algorithms for retinal image analysis including optic disc detection, tortuosity estimation, and cross-over abnormality detection. The retina is one of the only areas of the human body that blood vessels can be visualized noninvasively. Retinal imaging has become a standard in the ophthalmologist’s office because it is an easy and inexpensive way to monitor not just eye health, but also systemic vascular diseases. Changes to the retinal vasculature can be the early signs of diseases such as diabetic and hypertensive retinopathy, of which early detection can save vision, money, and improve overall health for the patient. When looking at the retinal vasculature, ophthalmologists generally rely on a qualitative assessment which can make comparisons over time or between different ophthalmologists difficult. Computer aided systems are now able to quantify what the ophthalmologist is qualitatively measuring in what they consider to be the most important features of the vasculature. These include, but are not limited to, tortuosity, arteriolar narrowing, cross-over abnormalities, and artery-vein (AV) ratio. The University of Padova has created a semi-automatic system for detecting and quantifying retinal vessels starting from optic disc detection, vessel segmentation, width estimation, tortuosity calculation, AV classification, and AV ratio. We propose a new method for optic disc detection that converts the retinal image into a graph and exploits vessel enhancement methods to calculate edge weights in finding the shortest path between pairs of points on the periphery of the image. The line segment with the maximum number of shortest paths is considered the optic disc location. The method was tested on three publicly available datasets: DRIVE, DIARETDB1, and Messidor consisting of 40, 89, and 1200 images and achieved an accuracy of 100, 98.88, and 99.42% respectively. The second contribution is a new algorithm for calculating abnormalities at AV crossing points. In retinal images, Gunn’s sign appears as a tapering of the vein at a crossing point, while Salus’s sign presents as an S-shaped curving. This work presents a method for the automatic quantification of these two signs once a crossover has been detected; combining segmentation, artery vein classification, and morphological feature extraction techniques to calculate vein widths and angles entering and exiting the crossover. Results on two datasets show separation between the two classes and that we can reliably detect and quantify these signs under the right conditions. The last contribution in tortuosity consists of two parts. A comparative study was performed on several of the most popular methods for tortuosity estimation on a new vessel dataset. Results show that several methods have good Cohen’s kappa agreement with both graders, while the tortuosity density metric has the highest single metric average agreement across vessel type and grader. The second is a new way to enhance curvature in segmented vessels based on a difference of Gabor filters to create a curvature enhanced image. The proposed method was tested on the RET-TORT database using several methods to calculate tortuosity, and had best Pearson’s correlation of .94 for arteries and .882 for veins, outperforming single mathematical formulations on the data. This held true after testing the method on the propose dataset as well, having higher correlation values across grader and vessel type compared with other tortuosity metrics.
Summary of Results:
The optic disc detection method was tested on three publicly available datasets: DRIVE, DIARETDB1, and Messidor consisting of 40, 89, and 1200 images and achieved an accuracy of 100, 98.88, and 99.42% respectively.
The AV nicking quantification method was tested on a small dataset of 10 crossing provided by doctors at Papageorgiou Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece. Results showed separation between the normal and abnormal classes for both the Gunn and Salus sign. The method was then tested on a larger, publicly available dataset which showed good separation for the Gunn sign.
The proposed tortuosity method was tested on the RET-TORT database using several methods to calculate tortuosity, and had best Pearson’s correlation of .94 for arteries and .882 for veins, outperforming single mathematical formulations on the data. It was then tested on the dataset proposed in this thesis, further corroborating the effectiveness of the method.

Abstract (italiano)

I contributi di questo lavoro sono per lo sviluppo di nuovi e lo stato degli algoritmi d'arte per l'analisi di immagini tra cui il rilevamento della retina ottica del disco, la stima tortuosità, e anomalia rilevamento cross-over. La retina è una delle poche zone del corpo umano che vasi sanguigni possono essere visualizzate in maniera non invasiva. imaging della retina è diventato uno standard nell'ufficio del oculista Poiché si tratta di un modo semplice e poco costoso per monitorare non solo la salute degli occhi, ma anche le malattie vascolari sistemiche. Le modifiche al sistema vascolare della retina possono essere i primi segni di malattie come la retinopatia diabetica e ipertensiva, di cui la diagnosi precoce può salvare la visione, il denaro, e migliorare la salute generale del paziente. Se si guarda alla vascolarizzazione della retina, oftalmologi in genere si basano su una valutazione qualitativa che può fare comparazioni nel tempo o tra i diversi oculisti difficile. Computer Aided sistemi sono ora incendio quantificare ciò che l'oculista è qualitativamente misura in quello che considerano essere le caratteristiche più importanti del sistema vascolare. Questi includono, ma non sono limitati a, tortuosità, arteriolare restringimento, anomalie di crossover, e il rapporto arteria-vena (AV). L'Università di Padova ha creato un sistema semi-automatico per la rilevazione e quantificazione vasi retinici a partire dalla rilevazione ottica del disco, la segmentazione nave, la stima di larghezza, il calcolo tortuosità, la classificazione AV, e il rapporto di AV. Abbiamo proposto un nuovo metodo per il rilevamento ottico che converte l'immagine retinica in un grafico e sfrutta disco metodi di aumento del vaso per calcolare i pesi bordo nel trovare il percorso più breve tra coppie di punti sulla periferia dell'immagine. Il segmento linea con il numero massimo di percorsi più brevi è considerata la posizione del disco ottico. Il metodo è stato testato su tre insiemi di dati accessibili al pubblico: DRIVE, DIARETDB1, e Messidor Composto da 40, 89, e 1200 immagini e ha raggiunto una precisione di 100, 98.88, e 99.42% respectively. Il secondo contributo è un nuovo algoritmo di calcolo anomalie AV ai punti di attraversamento. Nelle immagini della retina, segno di Gunn appare come una rastremazione della vena in un punto di passaggio, mentre il segno di Salus presenta come una curva a forma di S. Questo lavoro presenta un metodo per la quantificazione automatica di questi due segni once a incrocio è stato rilevato; combinando la segmentazione, l'arteria di classificazione della vena, e le tecniche di estrazione delle caratteristiche morfologiche per calcolare le larghezze delle vene e gli angoli che entrano ed escono il crossover. Risultati su due serie di dati mostrano la separazione tra le due classi e che possiamo in modo affidabile rilevare e quantificare Questi segni sotto le giuste condizioni. L'ultimo contributo in tortuosità compone di due parti. Uno studio comparativo è stato condotto su alcuni dei metodi più diffusi per la stima su un nuovo insieme di dati nave tortuosità. Che i risultati mostrano diversi metodi avere buon accordo con Cohen kappa Entrambi i selezionatori, mentre la metrica densità di tortuosità ha la più alta accordo metrica singola media di tipo di nave e selezionatore. Il secondo è un nuovo modo per migliorare curvature nei vasi segmentati sulla base di una differenza di Gabor filtri per creare una curvatura immagine migliorata. Theproposed Il metodo è stato testato su database RET-TORT utilizzando diversi metodi per calcolare tortuosità, e aveva più di correlazione di Pearson di .94 per arterie e vene per 0,882, superando singole formulazioni matematiche alla data. Questo valeva dopo aver testato il metodo proposto sul set di dati e, avendo valori di correlazione più elevati in tutta grader e tipo di imbarcazione tortuosità Rispetto ad altre metriche.
Sintesi dei risultati:
Il metodo di rilevazione disco ottico è stato testato su tre insiemi di dati accessibili al pubblico: DRIVE, DIARETDB1, e Messidor Composto da 40, 89, e 1200 immagini e ha raggiunto una precisione di 100, 98.88, e 99.42% respectively.
Il metodo di quantificazione AV intaccare è stato testato su un piccolo insieme di dati di 10 attraversamento forniti dai medici Papageorgiou Hospital, Università Aristotele di Salonicco, Salonicco, Grecia. I risultati hanno mostrato separazione tra le normali e anormali Entrambe le classi per il segno Gunn e Salus. Il metodo è stato poi testato su un set di dati più grandi, a disposizione del pubblico che ha mostrato una buona separazione per il segno Gunn.
Il metodo tortuosità Theproposed è stato testato su database RET-TORT utilizzando diversi metodi per calcolare tortuosità, e aveva più di correlazione di Pearson di .94 per arterie e vene per 0,882, superando singole formulazioni matematiche alla data. E 'stato poi testato su set di dati Theproposed in questa tesi, Ulteriori confermano l'efficacia del metodo.

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Tipo di EPrint:Tesi di dottorato
Relatore:N/A, N/A
Correlatore:Ruggeri, Alfredo
Dottorato (corsi e scuole):Ciclo 29 > Corsi 29 > INGEGNERIA DELL'INFORMAZIONE
Data di deposito della tesi:30 Gennaio 2017
Anno di Pubblicazione:30 Gennaio 2017
Parole chiave (italiano / inglese):Retinal Image Processing, Optic Disc Detection, AV Nicking, Tortuosity
Settori scientifico-disciplinari MIUR:Area 09 - Ingegneria industriale e dell'informazione > ING-INF/06 Bioingegneria elettronica e informatica
Struttura di riferimento:Dipartimenti > Dipartimento di Ingegneria dell'Informazione
Codice ID:10129
Depositato il:02 Nov 2017 16:50
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