M.S. Sangriotis

Bio: M.S. Sangriotis is an academic researcher from National and Kapodistrian University of Athens. The author has contributed to research in topics: Integral imaging & Image processing. The author has an hindex of 11, co-authored 27 publications receiving 256 citations. Previous affiliations of M.S. Sangriotis include Athens State University.

Stereo image compression using wavelet coefficients morphology

Abstract: In this paper, we propose a new stereo image compression scheme that is based on the wavelet transform of both images and the disparity estimation between the stereo pair subbands. The two images are decomposed by using a Discrete Wavelet Transform (DWT) and coded by employing the morphological representation of the wavelet coefficients, which is a technique that exploits the intraband–interband statistical properties of them. The progressive pixel-to-pixel evaluation of the disparity has been incorporated to the morphological coder so that a dense disparity field to be formed for every subband. The proposed method demonstrates very good performance as far as PSNR measures and visual quality are concerned and low complexity.

A computer-aided system for malignancy risk assessment of nodules in thyroid US images based on boundary features

Abstract: In this paper, a novel computer-based approach is proposed for malignancy risk assessment of thyroid nodules in ultrasound images. The proposed approach is based on boundary features and is motivated by the correlation which has been addressed in medical literature between nodule boundary irregularity and malignancy risk. In addition, local echogenicity variance is utilized so as to incorporate information associated with local echogenicity distribution within nodule boundary neighborhood. Such information is valuable for the discrimination of high-risk nodules with blurred boundaries from medium-risk nodules with regular boundaries. Analysis of variance is performed, indicating that each boundary feature under study provides statistically significant information for the discrimination of thyroid nodules in ultrasound images, in terms of malignancy risk. k-nearest neighbor and support vector machine classifiers are employed for the classification tasks, utilizing feature vectors derived from all combinations of features under study. The classification results are evaluated with the use of the receiver operating characteristic. It is derived that the proposed approach is capable of discriminating between medium-risk and high-risk nodules, obtaining an area under curve, which reaches 0.95.

Stereo video coding based on quad-tree decomposition of B– P frames by motion and disparity interpolation

Abstract: A new optimised technique for coding stereoscopic image sequences is presented and compared with already known methods. The proposed technique, called enhanced interpolated motion and disparity estimation (EIMDE), is based on the joint method, which encodes the frames of the right image sequence by exploiting both the temporal redundancy of the same sequence and the disparity redundancy with the left image sequence. In the proposed method, a variable block size scheme has been employed for motion and disparity estimation. The block size is controlled by quad-tree decomposition of the processed frame based on a rate-distortion splitting criterion. For the prediction of a macroblock, optimised motion and disparity vectors are jointly estimated and the participating proportion of each similarity is suitably searched. In this way, the energy of the resulted residual frame is minimised and the whole framework is optimised. Finally, the residual frame is decomposed by a discrete wavelet transform and is further compressed by morphological encoding the resulting coefficients. The proposed coder has been experimentally evaluated on real image sequences, where it produced good performance over other known methods.

Effect of different traversal schemes in integral image coding

Abstract: Integral imaging (InIm) is a highly promising technique for the delivery of three-dimensional (3D) image content. During capturing, different views of an object are recorded as an array of elemental images (EIs), which form the integral image. High-resolution InIm requires sensors with increased resolution and produces huge amounts of highly correlated data. In an efficient encoding scheme for InIm compression both inter-EI and intra-EI correlations have to be properly exploited. We present an EI traversal scheme that maximizes the performance of InIm encoders by properly rearranging EIs to increase the intra-EI correlation of jointly coded EIs. This technique can be used to augment performance of both InIm specific and properly adapted general use encoder setups, used in InIm compression. An objective quality metric is also introduced for evaluating the effects of different traversal schemes on the encoder performance.

Compression of IP images for autostereoscopic 3D imaging applications

Abstract: An autostereoscopic 3D viewing system that operates on the principles of integral photography (IP) provides a unique sense of depth, full parallax and multi-view functionality The inherent redundancy of these images results into great amounts of data that should be efficiently coded for transmission or storage operations In this communication, a method for efficient coding of such images is presented targeting 3D imaging and video applications The method is based on common techniques broadly used in image compression and properly adjusted in order to take advantage of the spatial redundancies of IP images The generality and flexibility of the proposed approach along with the stability for a wide range of bit rates constitutes the basic characteristics of the technique The proposed technique can be easily realized in software or hardware for computer based or standalone applications

Recent progress in three-dimensional information processing based on integral imaging.

Abstract: Recently developed integral imaging techniques are reviewed. Integral imaging captures and reproduces the light rays from the object space, enabling the acquisition and the display of the three-dimensional information of the object in an efficient way. Continuous effort on integral imaging has been improving the performance of the capture and display process in various aspects, including distortion, resolution, viewing angle, and depth range. Digital data processing of the captured light rays can now visualize the three-dimensional structure of the object with a high degree of freedom and enhanced quality. This recent progress is of high interest for both industrial applications and academic research.

Ultrasound-based differentiation of malignant and benign thyroid Nodules: An extreme learning machine approach

Abstract: Background and objectives It is important to be able to accurately distinguish between benign and malignant thyroid nodules in order to make appropriate clinical decisions. The purpose of this study was to improve the effectiveness and efficiency for discriminating the malignant from benign thyroid cancers based on the Ultrasonography (US) features. Methods There were 114 benign nodules in 106 patients (82 women and 24 men) and 89 malignant nodules in 81 patients (69 women and 12 men) included in this study. The potential of extreme learning machine (ELM) has been explored for the first time to discriminate malignant and benign thyroid nodules based on the sonographic features in ultrasound images. The influence of two key parameters (the number of hidden neurons and type of activation function) on the performance of ELM was investigated. The relationship between feature subsets obtained by the feature selection method and the classification performance of ELM was also examined. A real-life dataset was used to evaluate the effectiveness of the proposed method in terms of classification accuracy, sensitivity, specificity, and area under the ROC (receiver operating characteristic) curve (AUC). Results The results demonstrate that there are significant differences between the malignant and benign thyroid nodules (p-value Conclusions Based on the empirical analysis, the proposed ELM-based approach for thyroid cancer detection has promising potential in clinical use, and it can be of assistance as an optional tool for the clinicians.

A perceptual metric for stereoscopic image quality assessment based on the binocular energy

Abstract: Stereoscopic imaging is becoming very popular and its deployment by means of photography, television, cinema. . .is rapidly increasing. Obviously, the access to this type of images imposes the use of compression and transmission that may generate artifacts of different natures. Consequently, it is important to have appropriate tools to measure the quality of stereoscopic content. Several studies tried to extend well-known metrics, such as the PSNR or SSIM, to 3D. However, the results are not as good as for 2D images and it becomes important to have metrics dealing with 3D perception. In this work, we propose a full reference metric for quality assessment of stereoscopic images based on the binocular fusion process characterizing the 3D human perception. The main idea consists of the development of a model allowing to reproduce the binocular signal generated by simple and complex cells, and to estimate the associated binocular energy. The difference of binocular energy has shown a high correlation with the human judgement for different impairments and is used to build the Binocular Energy Quality Metric (BEQM). Extensive experiments demonstrated the performance of the BEQM with regards to literature.

Cascade convolutional neural networks for automatic detection of thyroid nodules in ultrasound images

Abstract: Purpose It is very important for calculation of clinical indices and diagnosis to detect thyroid nodules from ultrasound images. However, this task is a challenge mainly due to heterogeneous thyroid nodules with distinct components are similar to background in ultrasound images. In this study, we employ cascade deep convolutional neural networks (CNNs) to develop and evaluate a fully automatic detection of thyroid nodules from 2D ultrasound images. Methods Our cascade CNNs are a type of hybrid model, consisting of two different CNNs and a new splitting method. Specifically, it employs a deep CNN to learn the segmentation probability maps from the ground true data. Then, all the segmentation probability maps are split into different connected regions by the splitting method. Finally, another deep CNN is used to automatically detect the thyroid nodules from ultrasound thyroid images. Results Experiment results illustrate the cascade CNNs are very effective in detection of thyroid nodules. Specially, the value of area under the curve of receiver operating characteristic is 98.51%. The Free-response receiver operating characteristic (FROC) and jackknife alternative FROC (JAFROC) analyses show a significant improvement in the performance of our cascade CNNs compared to that of other methods. The multi-view strategy can improve the performance of cascade CNNs. Moreover, our special splitting method can effectively separate different connected regions so that the second CNN can correctively gain the positive and negative samples according to the automatic labels. Conclusions The experiment results demonstrate the potential clinical applications of this proposed method. This technique can offer physicians an objective second opinion, and reduce their heavy workload so as to avoid misdiagnosis causes because of excessive fatigue. In addition, it is easy and reproducible for a person without medical expertise to diagnose thyroid nodules.

HEVC-based 3D holoscopic video coding using self-similarity compensated prediction

Abstract: Holoscopic imaging, also known as integral, light field, and plenoptic imaging, is an appealing technology for glassless 3D video systems, which has recently emerged as a prospective candidate for future image and video applications, such as 3D television. However, to successfully introduce 3D holoscopic video applications into the market, adequate coding tools that can efficiently handle 3D holoscopic video are necessary. In this context, this paper discusses the requirements and challenges for 3D holoscopic video coding, and presents an efficient 3D holoscopic coding scheme based on High Efficiency Video Coding (HEVC). The proposed 3D holoscopic codec makes use of the self-similarity (SS) compensated prediction concept to efficiently explore the inherent correlation of the 3D holoscopic content in Intra- and Inter-coded frames, as well as a novel vector prediction scheme to take advantage of the peculiar characteristics of the SS prediction data. Extensive experiments were conducted, and have shown that the proposed solution is able to outperform HEVC as well as other coding solutions proposed in the literature. Moreover, a consistently better performance is also observed for a set of different quality metrics proposed in the literature for 3D holoscopic content, as well as for the visual quality of views synthesized from decompressed 3D holoscopic content. An efficient 3D holoscopic video coding solution based on HEVC is proposed.It relies on a self-similarity prediction and a new micro-image based vector prediction.Superior coding efficiency is shown compared to HEVC and other benchmark solutions.Consistently better performance regarding a set of different objective quality metrics.