Abstract

The most widely followed procedure for diagnosis and prognosis of dementia is structural neuro imaging of hippocampus by means of MR. Hippocampus segmentation is of wide interest as it enables quantitative assessment of the structure. In this paper, we propose an algorithm for hippocampus segmentation that is un-supervised and image driven. It is based on a hybrid approach which combines a coarse segmentation and surface evolution. A coarse solution is derived using region growing which is further refined using a modified version of the physics based water flow model (Liu and Nixon, 2007). The proposed method has been tested on a publicly available dataset. The performance of this method is assessed using Dice coefficient against the ground truth provided for 25 volume images. It is consistent across volumes and the average Dice values are comparable to a multi-atlas based method reported on a subset of the same dataset.

Abstract

Segmentation in echo-cardiographic images is a difficult task due to the presence of speckle noise, low contrast and blurring. We present a novel method based on clustering performed in the feature space. A new feature-based image representation is proposed. It is obtained by computing a local feature descriptor at every pixel location. This descriptor is derived using the Radon-Transform to effectively characterise local image context. Next, an un-supervised clustering is performed in the feature space to segment regions in the image. The performance of the proposed method is tested on both synthetic and real images. A comparison against well established feature descriptors is carried out to demonstrate the strengths and applicability of the proposed representation. Overall, the results indicate promise in the strategy of doing segmentation of noisy data in image descriptor space.

Abstract

Pneumoconiosis, a lung disease caused by the inhalation of dust, is mainly diagnosed using chest radiographs. The effects of using contralateral symmetric (CS) information present in chest radiographs in the diagnosis of pneumoconiosis are studied using an eye tracking experimental study. The role of expertise and the influence of CS information on the performance of readers with different expertise level are also of interest. Experimental subjects ranging from novices & medical students to staff radiologists were presented with 17 double and 16 single lung images, and were asked to give profusion ratings for each lung zone. Eye movements and the time for their diagnosis were also recorded. Kruskal-Wallis test (χ2(6) = 13.38, p = .038), showed that the observer error (average sum of absolute differences) in double lung images differed significantly across the different expertise categories when considering all the participants. Wilcoxon-signed rank test indicated that the observer error was significantly higher for single-lung images (Z = 3.13, p < .001) than for the double-lung images for all the participants. Mann-Whitney test (U = 28, p = .038) showed that the differential error between single and double lung images is significantly higher in doctors [staff & residents] than in non-doctors [others]. Thus, Expertise & CS information plays a significant role in the diagnosis of pneumoconiosis. CS information helps in diagnosing pneumoconiosis by reducing the general tendency of giving less profusion ratings. Training and experience appear to play important roles in learning to use the CS information present in the chest radiographs.

Abstract

Automatic retinal image analysis is emerging as an important screening tool for early detection of eye diseases. Glaucoma is one of the most common causes of blindness. The manual examination of optic disk (OD) is a standard procedure used for detecting glaucoma. In this paper, we present an automatic OD parameterization technique based on segmented OD and cup regions obtained from monocular retinal images. A novel OD segmentation method is proposed which integrates the local image information around each point of interest in multidimensional feature space to provide robustness against variations found in and around the OD region. We also propose a novel cup segmentation method which is based on anatomical evidence such as vessel bends at the cup boundary, considered relevant by glaucoma experts. Bends in a vessel are robustly detected using a region of support concept, which automatically selects the right scale for analysis. A multi-stage strategy is employed to derive a reliable subset of vessel bends calledr-bends followed by a local spline fitting to derive the desired cup boundary. The method has been evaluated on 138 images comprising 33 normal and 105 glaucomatous images against three glaucoma experts. The obtained segmentation results show consistency in handling various geometric and photometric variations found across the dataset. The estimation error of the method or vertical cup-to-disk diameter ratio is 0.09/0.08 (mean/standard deviation) while for cup-to-disk area ratio it is 0.12/0.10. Overall,the obtained qualitative and quantitative results show effective-ness in both segmentation and subsequent OD parameterization for glaucoma assessment.

Abstract

Diabetic macular edema (DME) is an advanced symptom of diabetic retinopathy and can lead to irreversible vision loss. In this paper, a two-stage methodology for the detection and classification of DME severity from color fundus images is proposed. DME detection is carried out via a supervised learning approach using the normal fundus images. A feature extraction technique is introduced to capture the global characteristics of the fundus images and discriminate the normal from DME images. Disease severity is assessed using a rotational asymmetry metric by examining the symmetry of macular region. The performance of the proposed methodology and features are evaluated against several publicly available datasets. The detection performance has a sensitivity of 100% with specificity between 74% and 90%. Cases needing immediate referral are detected with a sensitivity of 100% and specificity of 97%. The severity classification accuracy is 81% for the moderate case and 100% for severe cases. These results establish the effectiveness of the proposed solution.

Abstract

Generation of upsampled tomographic images via combination of rotated lattices has been explored in [1]. In this paper, we evaluate the existing method using real phantom data. Up-sampled tomographic images are generated via combination of rotated hexagonal lattices. Sinogram data is filtered and back-projected on two hexagonal lattices which are rotated versions of each other. Samples from these lattices are interpolated to generate an up-sampled image defined on a square lattice. These results are compared with direct up-sampling method and image ISR-2 algorithm described in [10]. Two PET phantoms - NEMA and Hoffman brain phantom are used for purpose of evaluation. The results of the proposed method show considerable improvement over direct up-sampled image in terms of contrast, sharpness and imaging artifact; but when compared with ISR-2 generated image, the difference in image quality is not significant. A key advantage of the proposed method is that only two images are required for generating a high resolution image whereas ISR2 requires k low resolution images for an up-sampling factor of k.

Abstract

Many fields of Artificial Intelligence including natural language processing, speech processing, robotics etc., have seen explosive growth in the last decade. In this paper, we discuss the application of these advances to aid children with autism and dyslexia. This paper describes two systems we are currently developing - an alternative and augmentative communication (AAC) system for children with autism and a speech modification system for children with dyslexia. We also present results of our initial evaluation.

Abstract

Interactive simulations encourage active/discovery learning in students. But developing simulations is a time consuming task. This along with the usability and scalability issues are the bottleneck in wide availability of such tools. The developer spends most of his/her time in tailoring the user interface (UI) to meet multiple constraints like pedagogy, teacher satisfaction and student learning styles. Providing an efficient way to develop UI can greatly speed up the development cycle and facilitate widespread access to high quality resources. In this paper, we describe how UI development can be separated from the simulation program to facilitate easy development of such visualization tool. We also discuss design and development of Graphical interface construction Kit (GicK) which helps in creating UI and combining it with simulation program for creating interactive simulations. A constructive physics simulation is developed as an exemplar of proposed framework. In the end we reported evaluation of constructive physics simulation by 43 higher school students and our conclusion.

Abstract

The shape deformation within the optic disk (OD) is an important indicator for the detection of glaucoma. In this paper, relevant disk parameters are estimated using the OD and cup boundaries. A deformable model guided by regional statistics is used to detect the OD boundary. A cup boundary detection scheme is presented based on the appearance of pallor in Lab colour space and the expected cup symmetry. The proposed scheme is tested on 170 images comprising 40 normal and 130 glaucomatous images. The proposed method gives a mean error 0.030 for normal and 0.121 for glaucomatous images in the estimation of cup-to-disk ratio which compares well with reported figures in literature.

Abstract

A growing number of public initiatives for screening the population for retinal disorders along with widespread availability of digital fundus (retina) cameras is leading to large accumulation of color fundus images. The ability to retrieve images based on pathologic state is a powerful functionality that has wide applications in evidence-based medicine, automated computer assisted diagnosis and in training ophthalmologists. In this paper, we propose a new methodology for content-based retrieval of retinal images showing symptoms of maculopathy. Taking the view of a disease region as one which exhibits deviation from the normal image background, a model for the image background is learnt and used to extract disease-affected image regions. These are then analysed to assess the severity level of maculopathy. Symmetry-based descriptor is derived for the macula region and employed for retrieval of images according to severity of maculopathy. The proposed approach has been tested on a publicly available dataset. The results show that background learning is successful as images with or no maculopathy are detected with a mean precision of 0.98. An aggregate precision of 0.89 is achieved for retrieval of images at three severity-levels of macular edema, demonstrating the potential offered by the proposed disease-based retrieval system.