Tag: Medical Imaging

PGRs meeting and Research Presentations – June 2015

Amr Ahmed / / Uncategorized

The monthly PGRs Research Presentations was held on Wed.  10th June, 2pm, Room MC3108.

This session we had the following presentations:

Title: “Effects of Environmental Changes on Aggregation with Robot Swarm“. Title:   “Computer-aided Liver lesion detection and classification”

By: Farshad Arvin

By: Hussein Alahmer
Abstract: Aggregation is one of the most fundamental behaviors that has been studied in swarm robotic researches for more than two decades. The studies in biology revealed that environment is a very important factor especially in cue-based aggregation in which a heterogeneity in the environment such as a heat or light source act as a cue indicating an optimal aggregation zone. In swarm robotics, studies on cue-based aggregation mainly focused on different methods of aggregation and different parameters such as population size. Although of utmost importance, effects of environmental factors have not been studied extensively. In this work, we study the effect of different  environmental factors such as size and texture of aggregation cues and speed of the changes on a dynamic environment using real robots. We used aggregation time and size of the aggregate as the two metrics and evaluated the performance of the swarm aggregation in static and dynamic environments. The results of the performed experiments illustrate how environmental changes influence the performance of a swarm aggregation.

Abstract: Liver cancer is one of the major death factors in the world. Transplantation and tumor resection are two main therapies in common clinical practice. Both tasks need image assisted planning and quantitative evaluations. An efficient and effective automatic liver segmentation is required for corresponding quantitative evaluations. Computed Tomography (CT) is highly accurate for liver cancer diagnosis. Manual identification of hepatic lesions done by trained physicians is a time-consuming task and can be subjective depending on the skill, expertise and experience of the physician.

Computer aided classification of liver tumors from abdominal Computer Tomography (CT) images requires segmentation and analysis of tumor. Automatic segmentation of tumor from CT images is difficult, due to the size, shape, position and presence of other objects with the same intensity present in the image.

The proposed system automatically segment liver from abdominal CT and detect hepatic lesions, then classifies the lesion into Benign or Malignant. The method uses Fuzzy C Means (FCM) clustering and region growing technique. The effectiveness of the algorithm is evaluated by comparing automatic segmentation results to the manual segmentation results. Quantitative comparison shows a close correlation between the automatic and manual as well as high spatial overlap between the regions-of-interest (ROIs) generated by expert radiologist and proposed system.

 

 

 

 

 

PGRs meeting and Research Presentations – March 2015

Amr Ahmed / / Uncategorized

The monthly PGRs Research Presentations was held on Wed. 11th March, 2pm, Room MC3108.

This session we had the following presentations:

Title: “Retinal Vascular Measurement“. Title:   “4D Lifelong Exploration of Dynamic Environments”

By: Francesco Caliva

By: Joao Santos
Abstract: Several studies have shown that systemic diseases affect blood vessels’ geometry. Retina is a window in the vascular system, thus fundus images can be adopted to diagnose or evaluate pathological conditions. Segmentation algorithms are not able to completely segment blood vessels. This failure results in a set of disconnected vascular segments. Reconstructing the whole network has crucial importance. At this aim, in this work, implicit neural cost functions have been adopted to evaluate how the segments can be joined. In this talk I will present my current and future work. Abstract:  We present a novel 4D lifelong exploration method for dynamic, human populated environments. In contrast to other exploration methods that model the environment as being static, our spatio-temporal exploration method creates and maintains a world model that not only represents the environment’s 3D structure, but also its dynamics over time.The predictive ability of the 4D spatio-temporal model allows the exploration method not only where, but also when to make environment observations.
To validate our method, a mobile robot was deployed over 5 days in an office environment, and the proposed method was compared against a static approach. The results show that through understanding of the environment dynamics, the spatio-temporal exploration algorithm could predict which locations were going to change at a specific time and used this knowledge to guide the robot. This allowed our spatio-temporal exploration method to gather more information that the exploration method that relied on a static environment model.

 

 

 

 

 

PGRs meeting and Research Presentations – Feb. 2015

Amr Ahmed / / Uncategorized

The monthly PGRs Research Presentations was held on Wed. 11th February, 2pm, Room MC3108.

This session we had the following presentations:

Title: “Designing, Developing and Evaluating Mobile and Social Technology to Improve Sleep Quality“. Title:   “Brain Tumour Grading in Different MRI Protocols using SVM on Statistical Features ”

By: Ibraheem Alnejaidi

By: Mohammadreza Soltaninejad
Abstract: Abstract:  Brain tumours are caused by abnormal and uncontrolled growing of the cells inside the brain or spinal canal. Regarding to the World Health Organization (WHO) grading system, the tumours are graded from I to IV, corresponding to least advanced to the most advanced diseases, respectively. In our work a feasibility study of brain MRI dataset classification, using ROIs which have been segmented either manually or through a superpixel based method in conjunction with statistical pattern recognition methods is performed. The aim is classifying tumour grades II, III and IV using different MRI acquisition protocols i.e. FLAIR, and T2. We found by using the Leave-One-Out method that the combination of the features from the 1st and 2nd order statistics, achieved high classification accuracy in pair-wise grading comparisons.
  • Then our usual catch-up agenda, including: Prepare for the March CRDB, PG Conference, plans for Showcase,….etc.

 

 

 

 

 

PGRs Research Presentations – March 2013

Amr Ahmed / / Uncategorized

The March’s PGRs Research Presentations was held on Wed. 13th March, 2pm, Meeting Room, MC3108 (3rd floor).

This session we had the following presentations:

Title: “A primal-dual fixed point algorithm with nonnegative constraint for CT image
reconstruction“.		 Title:   “Video Similarity in Compressed Domain”

By: Yuchao Tang

By: Saddam Bekhet
Abstract:Computed tomography (CT) image reconstruction problems often can be solved by finding the minimizer of a suitable objective function which usually consists of a data fidelity term  and a regularization term  subject to a convex constraint set $C$. In the unconstrained case, an efficient algorithm called the  primal-dual fixed point algorithm (PDFP$^{2}$O) has recently been developed to this problem, when the data fidelity term is differentiable with Lipschitz
continuous gradient and the regularization term composed by a simple convex function (possibly non-smooth) with a linear transformation. In this paper, we propose a modification of the PDFP$^{2}$O, which allows us to deal with the constrained minimization problem. We further propose accelerated algorithms which based on the Nesterov’s accelerated method. Numerical experiments on image reconstruction benchmark problem show that the proposed algorithms can produce better reconstructed image in signal-to-noise ratio than the original PDFP$^{2}$O and state-of-the-art methods with less iteration numbers. The
accelerated algorithms exhibit the fastest performance compared with all the other algorithms		 AbstractThe volume of video data is rapidly increasing, more than 4 billion hours of video are being watched each month on YouTube and more than 72 hours of video are uploaded to YouTube every minute, and counters are still running fast. A key aspect of benefiting from all that volume of data is the ability to annotate and index videos, to be able to search and retrieve them. The annotation process is time consuming and automating it, with semantically acceptable level, is a challenging task.The majority of available video data exists in compressed format MPEG-1, MPEG-2 and MPEG-4. Extraction of low level features, directly from compressed domain without decompression, is the first step towards efficient video content retrieval. Such approach avoids expensive computations and memory requirement involved in decoding compressed videos, which is the tradition in most approaches. Working on compressed videos is beneficial because they are rich of additional, pre-computed, features such as DCT coefficients, motion vectors and Macro blocks types.

The DC image is a thumbnail version that retains most of the visual features of its original full image. Taking advantage of the tiny size, timeless reconstruction and richness of visual content, the DC image could be employed effectively alone or in conjunction with other compressed domain features (e.g. AC coefficients, macro-block types and motion vectors) to represent video clips (with signature) and to detect similarity between videos for various purposes such as automated annotation, copy detection or any other higher layer built upon similarity between videos.

The Q/A was followed by a demonstration of the PGRs blog and discussion with PGRs (and attending staff) about the blog, BB community,…etc.

 

February PGR Research Presentations

Amr Ahmed / / Uncategorized

The PGRs Research Presentations series has started on Wed. 13th Feb, 1pm, Meeting Room, MC3108 (3rd floor).

In each session we expect two PGR presentations. This session we had the following presentations:

 

Title: “A probabilistic approach   to Correctly and Automatically form of Retinal Vasculature“.

 Title:   “Semantic Video Analysis: from Camera Language to Human Language”

By: Touseef Qureshi

By: Amjad Altadmri
Abstract: 

Correct configuration and formation of   retinal vasculature is a vital step towards the diagnoses of these   cardiovascular diseases. A single minor mistake during the process of   connecting broken segments of vessels can lead to a completely incorrect   vasculature. Image processing techniques can’t alone solve this problem. On   the other hand, we are working on multidimensional scientific approach that   integrates Artificial intelligence, image process techniques, statistics and   probability. We are working and expecting an optimal approach towards the   correct configuration of broken vessels segments at junctions, bridges, and   terminals.

 Abstract 

The   rapidly increasing volume of visual data, available online or via   broadcasting, emphasizes the need towards building intelligent tools for   indexing, searching, rating, and retrieval. Textual semantic representations,   such as tagging, labeling and annotation, are often important parts of   videos’ indexing process, due to the advances in text analysis and their   intuitive user-friendly nature for representing semantics suitable for search   and retrieval.

 

Ideally,   this annotation should simulate the human cognitive way of perceiving and   describing videos. While these digital video mediums contain low-level visual   data, human beings have the ability to infer more meaningful information from   videos. The difference between these low-level contents and its corresponding   human perception is referred to as the “semantic gap”. This gap is even   harder to be handled in domain-independent uncontrolled videos, mainly due to   the lack of any previous information about the analyzed video on one side,   and the huge generic knowledge needed to be available on the other.