The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering have made medical image analysis one of the top research and development area. One of the reasons for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This includes application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.

Medical Image Analysis using Convolutional Neural Networks: A Review

As the next generation standard of video coding, the High Efficiency Video Coding (HEVC) is intended to provide significantly better coding efficiency than all existing video coding standards. To improve the coding efficiency of intra frame coding, up to 34 intra prediction modes are defined in HEVC. The best mode among these pre-defined intra prediction modes is selected by rate-distortion optimization (RDO) for each block. If all directions are tested in the RDO process, it will be very time-consuming. To alleviate the encoder computation load, this paper proposes a new method to reduce the candidates in RDO process. In addition, the direction information of the neighboring blocks is made full use of to speed up intra mode decision. Experimental results show that the proposed scheme provides 20% and 28% time savings in intra high efficiency and low complexity cases on average compared to the default encoding scheme in HM 1.0 with almost the same coding efficiency. This algorithm has been proposed to HEVC standard and partially adopted into the HEVC test model.

Fast mode decision algorithm for intra prediction in HEVC

Proceedings of world academy of science, engineering and technology, vol 8

The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an effective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering has made medical image analysis one of the top research and development area. One of the reason for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This include application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.

The goal of this paper is to offer a structured synopsis of the problems in image motion computation and analysis, and of the methods proposed, exposing the underlying models and supporting assumptions. A sufficient number of pointers to the literature will be given, concentrating mostly on recent contributions. Emphasis will be on the detection, measurement and segmentation of image motion. Tracking, and deformable motion issues will be also addressed. Finally, a number of related questions which could require more investigations will be presented.

Computation and analysis of image motion: a synopsis of current problems and methods

Summary The new video coding standard, H.264/MPEG-4 AV C, uses an intra prediction mode with 4x4 blocks and 16x16 blocks sizes for luma component and 8x8 blocks size for chroma component. This new feature of H.264/AVC offers a considerably higher improvement in coding efficiency compared to other compression standards. In order to achieve this, a robust Rate-distortion optimization (RDO) technique is employed to select the best coding mode for each block sizes. However, the computational complexity of H.264 encoder is drastically increased due to the various intra prediction modes. In this paper, we propose a fast mode decision algorithm for intra prediction to reduce the complexity of H.264 video coding. The proposed algorithm based the fact that the dominating direction of a smaller block is similar to that of bigger block, the directional correlation of each block is consistent with directions of the edges, and the prediction modes of each block are also correlated with those of neighboring modes. The experimental results show that the fast intra mode decision algorithm is able to reduce on the average 84.68% encoding time, with a negligible peak signal-to noise ratio loss of 0.19dB or, equivalently, a bit rate increment of 1.88%.

/pdf/fast-mode-decision-algorithm-for-intra-prediction-in-h-264-4ob3fi4n83.pdf

Fast Mode Decision Algorithm for Intra prediction in H.264/AVC Video Coding

A performance comparison of measurement matrices in compressive sensing

This paper presents two-dimensional motion estimation methods which take advantage of the intrinsic redundancies inside 3DTV stereoscopic image sequences. Most of the previous studies extract, either disparity vector fields if they are involved in stereovision, or apparent motion vector fields to be applied to motion compensation coding schemes. For 3DTV image sequence analysis and transmission, we can jointly estimate these two feature fields. Locally, initial image data are grouped within two views (the left and right ones) at two successive time samples and spatio-temporal coherence has to be used to enhance motion vector field estimation. Three different levels of ‘coherence’ have been experimented subject to the crucial assumption that an initial calibration of the stereoscopic sensors provides us with geometric change of coordinates for two matched features.

Constrained disparity and motion estimators for 3DTV image sequence coding

Spectrum sensing is an important process in cognitive radio. Spectrum sensing techniques suffer from high processing time, hardware cost, and computational complexity. To address these problems, compressive sensing has been proposed to decrease the processing time and expedite the scanning process of the radio spectrum. Selection of a suitable sparse recovery algorithm is necessary to achieve this goal. This paper provides a deep survey on these sparse recovery algorithms, classify them into categories, and compares their performances. Six algorithms from different categories were implemented and their performances compared. As comparison metrics, we used recovery error, recovery time, covariance, and phase transition diagram. The results show that techniques under Greedy category are faster, techniques of Convex and Relaxation category perform better in term of recovery error, and Bayesian based techniques are observed to have an advantageous balance of small recovery error and a short recovery time.

Compressive sensing: Performance comparison of sparse recovery algorithms

Spectrum sensing enables cognitive radio systems to detect unused portions of the radio spectrum and then use them while avoiding interferences to the primary users. Energy detection is one of the most used techniques for spectrum sensing because it does not require any prior information about the characteristics of the primary user signal. However, this technique does not distinguish between the signal and the noise. It has a low performance at low SNR, and the selection of the threshold becomes an issue because the noise is uncertain. The detection performance of this technique can be further improvedusing a dynamic selection of the sensing threshold. In this work, we investigate a dynamic selection of this threshold by measuring the power of noise present in the received signal using a blind technique. The proposed model was implemented and tested using GNU Radio software and USRP units. Our results show that the dynamic selection of the threshold based on measuring the noise level present in the received signal during the detection process increases the probability of detection and decreases the probability of false alarm compared to the ones of energy detection with a static threshold.

Ahmed Tamtaoui

Papers

Fast Mode Decision Algorithm for Intra prediction in H.264/AVC Video Coding

A performance comparison of measurement matrices in compressive sensing

Constrained disparity and motion estimators for 3DTV image sequence coding

Compressive sensing: Performance comparison of sparse recovery algorithms

Spectrum sensing: Enhanced energy detection technique based on noise measurement