Retinal blood vessels extraction using probabilistic modelling

© 2014 Kaba et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.This article has been made available through the Brunel Open Access Publishing Fund.The analysis of retinal blood vessels plays an important role in detecting and treating retinal diseases. In this review, we present an automated method to segment blood vessels of fundus retinal image. The proposed method could be used to support a non-intrusive diagnosis in modern ophthalmology for early detection of retinal diseases, treatment evaluation or clinical study. This study combines the bias correction and an adaptive histogram equalisation to enhance the appearance of the blood vessels. Then the blood vessels are extracted using probabilistic modelling that is optimised by the expectation maximisation algorithm. The method is evaluated on fundus retinal images of STARE and DRIVE datasets. The experimental results are compared with some recently published methods of retinal blood vessels segmentation. The experimental results show that our method achieved the best overall performance and it is comparable to the performance of human experts.The Department of Information Systems, Computing and Mathematics, Brunel University

Akıllı tarfik sistemleri için araç plakası tanıma modülü tasarımı ve geliştirlmesi

Ülkemizde trafik ihlallerinin tespit edilmesi, gün geçtikçe büyüyen bir gereksinim halini almıştır. Özellikle otoyolların ücretlendirme noktaları kontrol altında tutulması gereken bölgelerdir. Bu amaçla, araç plakalarının tespitinin otomatik olarak yapılması elzem olmaktadır. Bu tezde otomatik plaka tanıma problemi iki ana alt modülle çözülmüştür. Bu modüller, plaka segmentasyonu ve karakter algılamadır. Plaka segmentasyonunda, araç görüntüsünden plaka görüntüsü ayrılmakta, karakter algılamada ise plaka görüntüsü üzerinde yer alan karakter görüntüleri çıkarılıp, tanınmakta ve metinsel ifadeye çevrilmektedir. Tez kapsamında, segmentasyon ve algılama modülleri için alternatif tekniklerin özellikleri ve başarımları verilmektedir. Daha sonra bu tekniklerin ışığı altında, hem segmentasyon hem de algılama modülleri için bu çalışmada yeni geliştirilen metodlar verilmekte ve Boğaziçi Köprüsünden elde edilen gerçek resimler üzerinde yapılan test sonuçları rapor edilmektedir

License plate segmentation for intelligent transportation systems

18th International Symposium on Computer and Information Sciences (ISCIS 2003) -- NOV 03-05, 2003 -- ANTALYA, TURKEYWOS: 000188096800055A license plate segmentation system is designed and developed The system has preprocessing, approximate region finding, plate extraction and character segmentation modules. For each module, alternative available methods are examined and proper sequence of operations is developed In character segmentation module, a novel method is devised. Finally, overall performance of the system is reported.Middle E Tech Univ, Sci & Tech Res Council Turkey, IEEE, Turkey Sect, Int Federat Informat Pro

Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm

WOS: 000270212400001PubMed ID: 19419790Blood vessels in ophthalmoscope images play an important role in diagnosis of some serious pathologies on retinal images. Hence, accurate extraction of vessels is becoming a main topic of this research area. Matched filter (MF) implementation for blood vessel detection is one of the methods giving more accurate results. Using this filter alone might not recover all the vessels (especially the capillaries). In this paper, a novel approach (MF/ant algorithm) is proposed to overcome the deficiency of the MF. The proposed method is a hybrid model of matched filter and ant colony algorithm. In this work, the accuracy and parameters of the hybrid algorithm are also discussed. The proposed method shows its success using the well known reference ophthalmoscope images of DRIVE database. (C) 2009 Elsevier Ireland Ltd. All rights reserved

Traffic flow condition classification for short sections using single microwave sensor

Daily observed traffic flow can show different characteristics varying with the times of the day. They are caused by traffic incidents such as accidents, disabled cars, construction activities and other unusual events. Three different major traffic conditions can be occurred: "Flow", "Dense" and "Congested". Objective of this research is to identify the current traffic condition by examining the traffic measurement parameters. The earlier researches have dealt only with speed and volume by ignoring occupancy. In our study, the occupancy is another important parameter of classification. The previous works have used multiple sensors to classify traffic condition whereas our work uses only single microwave sensor. We have extended Multiple Linear Regression classification with our new approach of Estimating with Error Prediction. We present novel algorithms of Multiclassification with One-Against-All Method and Multiclassification with Binary Comparison for multiple SVM architecture. Finaly, a non-linear model of backpropagation neural network is introduced for classification. This combination has not been reported on previous studies. Training data are obtained from the Corsim based microscopic traffic simulator TSIS 5.1. All performances are compared using this data set. Our methods are currently installed and running at traffic management center of 2.Ring Road in Istanbul. Copyright © 2010 Muhammed G. Cinsdikici and Kemal Memi

Automatic registration of structural brain MR images to MNI image space [Yaplsal Beyin MR Görüntülerinin MNI Görüntü Uzayma Otomatik Çakiştirilmasi]

2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 -- 16 May 2015 through 19 May 2015 -- 113052Disease diagnosis has been made by experts examining the images obtained by magnetic resonance imaging (MRI) technique, the disease process is observed using images taken at different times. Brain MR images are registered to the standard brain atlases because the human brain has a complex structure and varies from person to person. Corpus Callosum (CC) has a big importance for medical image registration because it can be easily distinguished on T1-weighted structural brain MR images and does not vary prominently between individuals. In this study, from the midsagittal brain MR image that belongs to the patient CC is detected fully automatically via Valley Matching (VM) Algorithm. The contribution of this study is registration of patient's MR image onto the Montreal Neurological Institute (MNI) image space by using automatically detected reference points. © 2015 IEEE

A comparative study of feature metrics for classification of human passport photos [Vesikalik fotograflarin siniflandirilmasi i·çin özellik ölçütleri üzerine kiyaslamali bir çahşma]

2007 IEEE 15th Signal Processing and Communications Applications, SIU -- 11 June 2007 through 13 June 2007 -- Eskisehir -- 73089In this study, for a multimedia indexing system that uses a Hierarchical Cellular Tree (HCT), effects of metrics that are obtained from the features extracted by using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are studied for a set of passport size photographs and classification performance is reported. Results obtained reveal that LDA has better performance than that of PCA for HCT