Computer-aided Diagnosis Technologies in Medicine

In this thesis, I focused on the research stream of computer-aided diagnosis technologies in medicine, and together with my collaborators I proposed several approaches for certain applications. Chapter 2 was motivated by the need of the exclusive detection of vascular bifurcations in retinal images.I demonstrated the effectiveness of the proposed model in two applications. One application concerns the detection of architectural and electrical symbols and the other one is the exclusive detection of vascular bifurcations without crossovers in retinal fundus images. In Chapter 3, Chapter 4 and Chapter 5, I proposed methods that can be used to assist medical experts in the diagnosis of epidermolysis bullosa acquisita (EBA). In Chapter 3, I reported a modified inhibition-augmented model for the ridge-ending detection, which is used for localizing u-serrated patterns for the diagnosis of EBA. In Chapter 4, I gave an account of another novel approach of automatic differentiation of u- and n-serrated patterns by normalized histogram of orientations in DIF images. In Chapter 5, I investigated the feasibility of using CNNs for the recognition of u-serrated patterns that can assist in the diagnosis of EBA

Network Intrinsic QoE Metrics for Video Transmission

Nowadays, networking is more and more important to people’s lives. Especially video streaming is playing a significant role in study and entertainment life. Many new applications appear to give people better videos. Because of high definition video, video compression and evaluation techniques become very useful to not only video web sites but also network operation and providers. Talking about video evaluation, Quality of experience (QoE) is an important indicator indicating the user experience of a video. There are a number of factors affecting performance of video delivery in the Internet with queue management discipline being one of the most important. From the networking perspective, there are two main router queue management, drop tail and Active Queue Management (AQM). Drop tail is widely used and it is simple to configure and maintain. Even though it may cause continuous packet loss when congestion happens over the network which may have a great impact on video streaming, it, nowadays, is still widely used. AQM could be a better way to manage the router buffer. Random early detection (RED) is one of AQM and it can avoid congestion because it drops packets randomly. However, it is more difficult to configure. The experiment in this paper is a statistical experiment to get the relationship between packet loss probability and correlation and QoE to provide a new network-intrinsic QoE metric..The process of video transmission over the network is simulated. The new metric is obtained by analyzing the obtained results. Even though it is a reference model, it is still very important. First, it gives a better way to estimate video quality using the network parameters. Second, analyzing the obtained results we see that , in order to get a better quality of video, RED is a better choice. In the future, the more accurate metrics can be obtained by more times of experiment. Such values would provide more detailed quantitative relationship between packet loss probability and correlation and QoE

Use of convolutional neural networks for the detection of u-serrated patterns in direct immunofluorescence images to facilitate the diagnosis of epidermolysis bullosa acquisita

The u-serrated immunodeposition pattern in direct immunofluorescence (DIF) microscopy is a recognizable feature and confirmative for the diagnosis of epidermolysis bullosa acquisita (EBA). Due to unfamiliarity with serrated patterns, serration pattern recognition is still of limited use in routine DIF microscopy. The objective of this study was to investigate the feasibility of using convolutional neural networks (CNNs) for the recognition of u-serrated patterns that can assist in the diagnosis of EBA. The nine most commonly used CNNs were trained and validated by using 220,800 manually delineated DIF image patches from 106 images of 46 different patients. The data set was split into 10 subsets: nine training subsets from 42 patients to train CNNs and the last subset from the remaining four patients for a validation data set of diagnostic accuracy. This process was repeated 10 times with a different subset used for validation. The best-performing CNN achieved a specificity of 89.3% and a corresponding sensitivity of 89.3% in the classification of u-serrated DIF image patches, an expert level of diagnostic accuracy. Experiments and results show the effectiveness of CNN approaches for u-serrated pattern recognition with a high accuracy. The proposed approach can assist clinicians and pathologists in recognition of u-serrated patterns in DIF images and facilitate the diagnosis of EBA

Automatic classification of serrated patterns in direct immunouorescence images

Direct immunofluorescence (DIF) images are used by clinical experts for the diagnosis of autoimmune blistering diseases. The analysis of serration patterns in DIF images concerns two types of patterns, namely n- and u-serrated. Manual analysis is time-consuming and challenging due to noise. We propose an algorithm for the automatic classification of serrated patterns in DIF images. We first segment the epidermal basement membrane zone (BMZ) where n- and u-serrated patterns are typically found. Then, we apply a bank of B-COSFIRE filters to detect ridges and determine their orientations with respect to the BMZ. Finally, we classify an image by comparing its normalized histogram of relative orientations with those of the training images using a nearest neighbor approach. We achieve a recognition rate of 84.4% on a UMCG data set of 416 DIF images, which is comparable to 83.4% by clinical experts.peer-reviewe