Radim Burget

Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image

Glaucoma is a disease of the retina which is one of the most common causes of permanent blindness worldwide. This paper presents an automatic image processing based method for glaucoma diagnosis from the digital fundus image. In this paper wavelet feature extraction has been followed by optimized genetic feature selection combined with several learning algorithms and various parameter settings. Unlike the existing research works where the features are considered from the complete fundus or a sub image of the fundus, this work is based on feature extraction from the segmented and blood vessel removed optic disc to improve the accuracy of identification. The experimental results presented in this paper indicate that the wavelet features of the segmented optic disc image are clinically more significant in comparison to features of the whole or sub fundus image in the detection of glaucoma from fundus image. Accuracy of glaucoma identification achieved in this work is 94.7% and a comparison with existing methods of glaucoma detection from fundus image indicates that the proposed approach has improved accuracy of classification.

Novel Method for Localization of Common Carotid Artery Transverse Section in Ultrasound Images Using Modified Viola-Jones Detector

This article describes a novel method for highly accurate and effective localization of the transverse section of the carotis comunis artery in ultrasound images. The method has a high success rate, approximately 97%. Unlike analytical methods based on geometric descriptions of the object sought, the method proposed here can cover a large area of shape variation of the artery under study, which normally occurs during examinations as a result of the pressure on the examined tissue, tilt of the probe, setup of the sonographic device, and other factors. This method shows great promise in automating the process of determining circulatory system parameters in the non-invasive clinical diagnostics of cardiovascular diseases. The method employs a Viola-Jones detector that has been specially adapted for efficient detection of transverse sections of the carotid artery. This algorithm is trained on a set of labeled images using the AdaBoost algorithm, Haar-like features and the Matthews coefficient. The training algorithm of the artery detector was modified using evolutionary algorithms. The method for training a cascade of classifiers achieves on a small number of positive and negative training data samples (about 500 images) a high success rate in a computational time that allows implementation of the detector in real time. Testing was performed on images of different patients for whom different ultrasonic instruments were used under different conditions (settings) so that the algorithm developed is applicable in general radiologic practice.

Towards an automatic design of non-cryptographic hash function

This paper presents an automatic approach to a non-cryptographic hash function design based on grammar guided genetic programming. The paper describes how it is possible to design a non-cryptographic hash function, implementation issues such as terminal and nonterminal symbols, fitness measure, and used context-free grammar. The main aim of this paper is to link the expert knowledge in the design of non-cryptographic hash function and the process of automatic design which can try many more combinations then an expert can. The hash function automatically designed in the paper is competitive with human design and it is compared with the most used non-cryptographic hashes in the field of speed of processing and in the field of collision resistance. The results are discussed in the last section and further improvement is mentioned.

Evolutionary improved object detector for ultrasound images

Object detection in ultrasound images is difficult problem mainly because of relatively low signal-to-noise ratio. This paper deals with object detection in the noisy ultrasound images using modified version of Viola-Jones object detector. The method describes detection of carotid artery longitudinal section in ultrasound B-mode images. The detector is primarily trained by AdaBoost algorithm and uses a cascade of Haar-like features as a classifier. The main contribution of this paper is a method for detection of carotid artery longitudinal section. This method creates cascade of classifiers automatically using genetic algorithms. We also created post-processing method that marks position of artery in the image. The proposed method was released as open-source software. Resulting detector achieved accuracy 96.29%. When compared to SVM classification enlarged with RANSAC (RANdom SAmple Consensus) method that was used for detection of carotid artery longitudinal section, works our method real-time.

Automatic 3D segmentation of human brain images using data-mining techniques

This paper proposes a method for automatic 3D segmentation of human brain CT scans using data mining techniques. The brain scans are processed in 2D and 3D. The proposed method has several steps - image pre-processing, segmentation, feature extraction from segments, data mining, and post-processing. The method introduced is implemented in 3D image processing extension for the RapidMiner platform, and both are provided as open source. With testing data the resultant performance selection of tissue slices from the brain image was 98.08% when compared to human expert results.

Topology aware feedback transmission for real-time control protocol

Real-time Transport Protocol (RTP) is a protocol for delivering time sensitive data such as audio and video over the Internet. RTP is usually used in conjunction with Real-time Control Protocol (RTCP), which provides statistics about flow of data using RTP. Certain problems related to RTCP scalability have arisen, mostly due to growth in the IPTV market and higher demands on bigger broadcasting sessions. Several solutions based on tree structures such as S-RTCP, MS-RTCP, TTP and others have been introduced. This paper proposes a method for constructing an optimal and well organized hierarchical tree structure with respect to standard RTCP. The proposed method can be integrated with most of the current solutions, including S-RTCP, MS-RTCP and TTP. Experience gained by us deploying our method on a global research network called PlanetLab is also discussed at the end of this paper.

Study and performance of localization methods in IP based networks: Vivaldi algorithm

This paper deals with station localization in distributed applications in the Internet. Several localization techniques are described with their advantages and disadvantages. This paper is focused on the sophisticated Vivaldi algorithm and its variations. A special simulation tool was developed in order to simulate the influence of configuration parameters and setting to algorithm performance. Several tests were performed to examine how both convergence and accuracy of localization process are affected by different settings of algorithm constants and by the number of reference points.

Digital identification tags for medical fundus images for tele-ophthalmology applications

This paper proposes a method of inserting a digital pattern having patient identity in the medical image without tampering the medical information of the image. To attain imperceptible insertion of the digital pattern a frequency domain approach is used in the mid frequency band of the discrete cosine transform. The original medical image and the stego-image is compared and analyzed for all features and also tested for retaining of all features and medical information. Blood vessels have been extracted from the original and stego image and it has been established from experimental results that the features remains unaltered. Texture features also has been analyzed and experimental results indicates that the variation in the texture features is minimal and do not affect the medical information. The correlation of the features extracted is above 0.99 indicating the insertion of the digital pattern did not cause any loss of medical information in the image.

Generation of biometric based unique digital watermark from iris image

This paper proposes a proficient digital watermark generation technique from biometric data which will be unique and can be logically owned to prove ownership. The biometric pattern of iris is used to generate the digital watermark that has a clear stamp of ownership. The generated watermark has been studied for uniqueness and identification and has been used to watermark audio signals. Dither modulation quantization is applied on the singular values of Singular Value Decomposition domain for embedding the watermark. Experimental results indicates that the watermark can survive the signal processing attacks such as Gaussian noise corruption, re-sampling, re-quantization, cropping, and compression and maintain the perceptual properties of the host signal and hence satisfies the design requirements of digital watermarking. The extracted biometric based watermark was uniquely identified under signal processing attacks.

An efficient automatic method of Optic disc segmentation using region growing technique in retinal images

Segmentation of Optic disc (OD) from a retinal image is a essential step while developing automated screening systems for eye disease like diabetic retinopathy, Glaucoma etc. This paper proposes a method of automatic optic disk segmentation based on region growing technique with automatic seed selection. In this method centre of optic disk is considered as a seed to apply region growing technique to segment the optic disk from the preprocessed retinal image. Automatic detection of centre of optic disk is done by double windowing method. The algorithm uses image processing techniques like contrast adjustment, morphological operations & filtering to process the retinal image and to remove the blood vessels from the retinal image. The performance of optic disk segmentation by proposed method is compared with Optic disk segmentation by ophthalmologists and results are found convincing and efficient. The experimental results indicate this method of segmentation of the OD has good accuracy and also is computationally cheap.