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Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis

GeethaRamani R., Balasubramanian L.

Biocybernetics and Biomedical Engineering

2016

Vol. 36, no. 1

102--118

Most of the retinal diseases namely retinopathy, occlusion etc., can be identified through changes exhibited in retinal vasculature of fundus images. Thus, segmentation of retinal blood vessels aids in detecting the alterations and hence the disease. Manual segmentation of vessels requires expertise. It is a very tedious and time consuming task as vessels are only a few pixels wide and extend almost throughout entire span of the fundus image. Employing computational approaches for this purpose would help in efficient retinal analysis. The methodology proposed in this work involves sequential application of image pre-processing, supervised and unsupervised learning and image post-processing techniques. Image cropping, color transformation and color channel extraction, contrast enhancement, Gabor filtering and halfwave rectification are sequentially applied during pre-processing stage. A feature vector is formed from the pre-processed images. Principal component analysis is performed on the feature vector. K-means clustering is executed on this outcome to group pixels as either vessel or non-vessel cluster. Out of the two groups, the identified non-vessel group undergoes an ensemble classification process employing root guided decision tree with bagging, while vessel group is left unprocessed as further processing might increase misclassifications of vessels as non-vessels. The resultant segmented image is formed through combining the results of clustering and ensemble classification process. The vessel segmented output from previous phase is post-processed through morphological techniques. The proposed technique is validated on images from publicly available DRIVE database. The proposed methodology achieves an accuracy of 95.36%, which is comparable with the existing blood vessel segmentation techniques.

Invariant Gabor-Zernike descriptor for postal applications

Andrysiak T., Miciak M., Boniecki R.

Zeszyty Naukowe. Telekomunikacja i Elektronika / Uniwersytet Technologiczno-Przyrodniczy w Bydgoszczy

2011

z. 15

5-17

In this paper a new solution of handwritten digits recognition system for postal applications is presented. Moreover, in this paper, a new method of handwritten characters recognition is introduced. The proposed algorithm is applied to classification of post mails on the basis of zip code information. In connection with this work the research was conducted with numeric characters used in real post code of mail pieces. Moreover, the article contains basic image processing for instance filtration binarization and normalization of the character. The main objective of this article is to use the Gabor filtration and Zernike moments to obtain a set of invariant features, on basis of which postal code will be recognized. The reported experiments' results prove the effectiveness of the proposed method. Furthermore, sources of errors as well as possible improvement of classification results will be discussed.

W artykule przedstawiono nowe rozwiązanie zadania rozpoznawania znaków pisanych ręcznie dla zastosowań pocztowych. Zaproponowano algorytm klasyfikacji przesyłek pocztowych działający na podstawie informacji zawartej w zapisie kodu pocztowego. Ponadto w artykule opisano podstawowe operacje przetwarzania wstępnego tj. filtracje, binaryzacje oraz normalizacje obrazu znaku. Głównym nacisk położono na wykorzystanie filtracji Gabora i momentów Zernike do uzyskania zbioru cech na podstawie których rozpoznawano kod pocztowy. Otrzymane wyniki eksperymentów pozwoliły wykazać skuteczność proponowanej metody. Dodatkowo w pracy przedstawiono źródła potencjalnych błędów w procesie rozpoznawania, jak również zaproponowano możliwości poprawy wyników klasyfikacji.