16th National Conference of Computer Society of Iran
March 10, 2011
If there was a quantum computer, the most complicated encryption would be decrypted less than a second. The reason should be searched in enormous throughput and fast speed of quantum processors. Designs which have been proposed by researchers were done manually by no CAD tools. In this paper, a new algorithm by the name of greedy-linear is proposed to generate quantum circuits automatically. The goal of proposed algorithm is to create shortest paths between quantum gates with less delay. Experimental results indicate greedy-linear algorithm has significant effects in area shrinkage and delay decrease of small circuits.
First International Conference on Contemporary Issues in Computer and Information Sciences (CICIS 2011)
May 1, 2011
Detection of retinal vessels has a verity of usage in diagnosing of disease such as diabetic retinopathy, hypertension and glaucoma. The matched filter has the most accuracy among all the methods have been presented. In this method, assuming vessels as a Gaussian curve, all bell-ring models are detected through the image entirely. This paper is focused on new parameters which are presented to increase the accuracy of detection. Experimental results indicate that the accuracy in the proposed filter is much more than previous methods.
International journal of Distributed and parallel systems
July 12, 2012
Common Mobile IPv6 mechanisms, Bidirectional tunneling and Route optimization, show inefficient packet overhead when both nodes are mobile. Researchers have proposed methods to reduce packet overhead regarding to maintain compatible with standard mechanisms. In this paper, three mechanisms in Mobile IPv6 are discussed to show their efficiency and performance. Following discussion, a new mechanism called Improved Tunneling-based Route Optimization is proposed and due to performance analysis, it is shown that proposed mechanism has less overhead comparing to common mechanisms. Analytical results indicate that Improved Tunneling-based Route Optimization transmits more payloads due to send packets with less overhead.
StudECE Conference, At Porto, Portugal
Although the density of breasts is not a disease, it can lead to indiscrimination of some diseases, since it is hard for specialists to discern tumours in dense tissues. In this paper, based on tissue density, a mammogram is classified in four ordinal classes. To do this, an ordinal classification method is implemented with two different machine learning methods, SVM and Neural Network. Experimental results indicate that by using preprocessing followed by Support vector regression one attain better results than the other implemented methods.
RECPAD Conference, At Lisbon, Portugal
Breast cancer is the most common cancer among females. Two main approaches are used as treatment: mastectomy, in which the cancerous breast is completely removed; and conservative treatment, in which the tumour is removed with margin of healthy tissue. To improve surgical approaches resulting less damage to dynamic shape of breast, it is worth to study the breast model to enable specialists having full comparison between the results of different treatments. The aim of this work is to study 3D reconstruction based on passive and active sensors. Also, it is aimed to study state of the art about parametric models to obtain breast shape. Such parametric model can enhance surgeons experience in order to perform better surgeries and patients to be more confident about the breast shape after treatment.
Journal of Medical Signals and Sensors
In this paper, a novel matched filter based on a new kernel function with Cauchy distribution is introduced to improve the accuracy of the automatic retinal vessel detection compared with other available matched filter-based methods, most notably, the methods built on Gaussian distribution function. Several experiments are conducted to pick the best values of the parameters for the new designed filter, including both Cauchy function parameters as well as the matched filter parameters such as the threshold value. Moreover, the thresholding phase is enhanced with a two-step procedure. Experimental results employed on DRIVE retinal images database confirms that the proposed method has higher accuracy compared with other available matched filter-based methods.
3D Body Scanning Technologies Conference, At Lugano, Switzerland
The patient 3D model reconstruction plays an important role in applications such as surgery planning or computer-aided prosthesis design systems. Common methods use either expensive devices or require expert personnel which are not available in every clinic. Thus to make patient-specific modelling more versatile, it is required to develop efficient methods together with feasible devices. Body parts such as head and torso present valid challenges with different degrees of complexity, especially because of the absence of relevant and abundant features.
Considering Microsoft Kinect, it is a low-cost and widely available sensor, which has been successfully applied in medical applications. Since single depth-map acquired by Kinect is often incomplete and noisy, different approaches have been proposed to perform the reconstruction by merging multiple depth-maps, by registering single view point clouds generated form each point cloud. As human body is a non-rigid model, most of previous reconstruction methods using Kinect fail to perform accurate reconstruction since they do not address non-rigid surfaces.
In this paper we present the challenges of using low-cost RGB-D sensors to reconstruct human body. Additionally, we analysed coarse registration stage to understand its impact on the quality of reconstruction on both rigid and non-rigid data. Also comparative research has been performed to study different coarse registration methods such as Spin Image (SI), Curvedness, and Principal Component Analysis (PCA). Studies showed that the quality of reconstruction is directly related to robustness of reconstruction method to the rotational and translation noise. Regarding analytical comparisons, results indicate the positive impression of applying coarse registration on both rigid and non-rigid data. Moreover, evaluations show PCA presents better results among other considered methods. Finally it is shown that down-sampled models present less error.
IEEE International Conference on Bioinformatics and Biomedicine, At Belfast, Ireland
The medical procedures related with the Breast Cancer Conservative Treatment (BCCT) have evolved towards the usage of affordable and practical tools, along with the recent inclusion of volumetric information of the breast. A richer three dimensional (3D) model of the female torso allows, for instance, improvement of the evaluation the aesthetic outcome of BCCT and the surgery planning. The standard 3D reconstruction methods often fail to model objects of interest using highly misaligned views. In this work, a Tessellation-based coarse registration method is proposed, based on robust key points extraction from RGB-D data using the Delaunay Triangulation (DT) principle. With this method, it is possible to reconstruct female torso data with detail using only 3 views, in feasible time. Structures such as the nipples and the breast contour were correctly reconstructed and a highly correlated with reference models.
21th edition of the Portuguese Conference on Pattern Recognition, Faro, Algarve, Portugal, October 2015
Breast cancer is the most common cancer disease among females. Therefore any deformations in the breast shape resulted from treatment, impacts patients' quality of life given the importance of breast as a feminine symbol. Using techniques such as parametric modeling, enable surgeons to model 3D breast shape virtually. Such parametric model can be used in Surgery Planning Tools in order to follow up the shape of breast after applying different deformation. This framework can help not only to improve the surgical skills of surgeons to perform surgeries with better cosmetic outcomes, but also to increase interaction between patients and surgeons in the moment of discussing what procedure needs to be performed. In this paper, two different methodologies of parametric modeling are compared. Quantitative analysis indicates that Free-Form-Deformation methodology (FFD) presents better parametric models than Physical Modeling methodology.
Breast Conserving Surgery Outcome Prediction: A Patient-Specific, Integrated Multi-modal Imaging and Mechano-Biological Modelling Framework
International Workshop on Digital Mammography
Patient-specific surgical predictions of Breast Conserving Therapy, through mechano-biological simulations, could inform the shared decision making process between clinicians and patients by enabling the impact of different surgical options to be visualised. We present an overview of our processing workflow that integrates MR images and three dimensional optical surface scans into a personalised model. Utilising an interactively generated surgical plan, a multi-scale open source finite element solver is employed to simulate breast deformity based on interrelated physiological and biomechanical processes that occur post surgery. Our outcome predictions, based on the pre-surgical imaging, were validated by comparing the simulated outcome with follow-up surface scans of four patients acquired 6 to 12 months post-surgery. A mean absolute surface distance of 3.3 mm between the follow-up scan and the simulation was obtained.
International Conference Image Analysis and Recognition
Nowadays, breast cancer has become the most common cancer amongst females. As long as breast is assumed to be a feminine symbol, any imposed deformation of surgical procedures can affect the patients' quality of life. However, using a planning tool which is based on parametric modeling, not only improves surgeons' skills in order to perform surgeries with better cosmetic outcomes, but also increases the interaction between surgeons and patients during the decision for necessary procedures. In the current research, a methodology of parametric modeling, called Free-Form Deformation (FFD) is studied. Finally, confirmed by a quantitative analysis, we proposed two simplified versions of FFD methodology to increase model similarity to input data and decrease required fitting time.
Iberian Conference on Pattern Recognition and Image Analysis
Automatic segmentation of breast is an important step in the context of providing a planning tool for breast cancer conservative treatment, being important to segment completely the breast region in an objective way; however, current methodologies need user interaction or detect breast contour partially. In this paper, we propose a methodology to detect the complete breast contour, including the pectoral muscle, using multi-modality data. Exterior contour is obtained from 3D reconstructed data acquired from low-cost RGB-D sensors, and the interior contour (pectoral muscle) is obtained from Magnetic Resonance Imaging (MRI) data. Quantitative evaluation indicates that the proposed methodology performs an acceptable detection of breast contour, which is also confirmed by visual evaluation.
Prediction of Breast Deformities: A Step Forward for Planning Aesthetic Results After Breast Surgery
Iberian Conference on Pattern Recognition and Image Analysis
The development of a three-dimensional (3D) planing tool for breast cancer surgery requires the existence of proper deformable models of the breast, with parameters that can be manipulated to obtain the desired shape. However, modelling breast is a challenging task due to the lack of physical landmarks that remain unchanged after deformation. In this paper, the fitting of a 3D point cloud of the breast to a parametric model suitable for surgery planning is investigated. Regression techniques were used to learn breast deformation functions from exemplar data, resulting in comprehensive models easy to manipulate by surgeons. New breast shapes are modelled by varying the type and degree of deformation of three common deformations: ptosis, turn and top-shape.
A New Label-Free Technique for Analysing Evaporation Induced Self-Assembly of Viral Nanoparticles Based on Enhanced Dark-Field Optical Imaging
Nanoparticle self-assembly is a complex phenomenon, the control of which is complicated by the lack of appropriate tools and techniques for monitoring the phenomenon with adequate resolution in real-time. In this work, a label-free technique based on dark-field microscopy was developed to investigate the self-assembly of nanoparticles. A bio-nanoparticle with complex shape (T4 bacteriophage) that self-assembles on glass substrates upon drying was developed. The fluid flow regime during the drying process, as well as the final self-assembled structures, were studied using dark-field microscopy, while phage diffusion was analysed by tracking of the phage nanoparticles in the bulk solutions. The concentrations of T4 phage nanoparticles and salt ions were identified as the main parameters influencing the fluid flow, particle motion and, consequently, the resulting self-assembled structure. This work demonstrates the utility of enhanced dark-field microscopy as a label-free technique for the observation of drying-induced self-assembly of bacteriophage T4. This technique provides the ability to track the nano-sized particles in different matrices and serves as a strong tool for monitoring self-assembled structures and bottom-up assembly of nano-sized building blocks in real-time.
Breast cancer treatments can have a negative impact on breast aesthetics, in case when surgery is intended to intersect tumor. For many years mastectomy was the only surgical option, but more recently breast conserving surgery (BCS) has been promoted as a liable alternative to treat cancer while preserving most part of the breast. However, there is still a significant number of BCS intervened patients who are unpleasant with the result of the treatment, which leads to self-image issues and emotional overloads. Surgeons recognize the value of a tool to predict the breast shape after BCS to facilitate surgeon/patient communication and allow more educated decisions; however, no such tool is available that is suited for clinical usage. These tools could serve as a way of visually sensing the aesthetic consequences of the treatment. In this research, it is intended to propose a methodology for predict the deformation after BCS by using machine learning techniques. Nonetheless, there is no appropriate dataset containing breast data before and after surgery in order to train a learning model. Therefore, an in-house semi-synthetic dataset is proposed to fulfill the requirement of this research. Using the proposed dataset, several learning methodologies were investigated, and promising outcomes are obtained.
Breast cancer is one of the most common malignanciesaffecting women worldwide. However, despite its incidence trends have increased, the mortality rate has significantly decreased. The primary concern in any cancer treatment is the oncological outcome but, in the case of breast cancer, the surgery aesthetic result has become an important quality indicator for breast cancer patients. In this sense, an adequate surgical planning and prediction tool would empower the patient regarding the treatment decision process, enabling a better communication between the surgeon and the patient and a better understanding of the impact of each surgical option. To develop such tool, it is necessary to create complete 3D model of the breast, integrating both inner and outer breast data. In this review, we thoroughly explore and review the major existing works that address, directly or not, the technical challenges involved in the development of a 3D software planning tool in the field of breast conserving surgery.