Atlas.txt : Exploring Lingusitic Grounding Techniques for Communicating Spatial Information to Blind Users

Peer reviewedPostprin

Vocal Folds Disorders Detection and Classification in Endoscopic Narrow-Band Images

The diagnosis of vocal folds (VF) diseases is error- prone due to the large variety of diseases that can affect them. VF lesions can be divided in nodular, e.g. nodules, polyps and cysts, and diffuse, e.g. hyperplastic laryngitis and carcinoma. By endoscopic examination, the clinician traditionally evaluates the presence of macroscopic formations and mucosal vessels alteration. Endoscopic narrow-band imaging (NBI) has recently started to be employed since it provides enhanced vessels contrast as compared to classical white-light endoscopy. This work presents a preliminary study on the development of an automatic diagnostic tool based on the assessment of vocal cords symmetry in NBI images. The objective is to identify possible protruding mass lesions on which subsequent vessels analysis may be performed. The method proposed here is based on the segmentation of the glottal area (GA) from the endoscopic images, based on which the right and the left portions of the vocal folds are detected and analyzed for the detection of protruding areas. The obtained information is then used to classify the VF edges as healthy or pathological. Results from the analysis of 22 endoscopic NBI images demonstrated that the proposed algorithm is robust and effective, providing a 100% success rate in the classification of VF edges as healthy or pathological. Such results support the investment in further research to expand and improve the algorithm presented here, potentially with the addition of vessels analysis to determine the pathological classification of detected protruding areas

An Experimental Approach for Detecting Brain Tumor from MRI Images using Digital Image Processing Techniques in MatLab

The Digital Image Process plays a very important role in Medical Research and processing the MRI images. Using image processing techniques the MRI images can be used to detect and analysis the tumor growing in brain. SAR images are the high resolution images which cannot be collected manually. In this work, we identified the SAR images randomly from web with different region inclusions. The comparative results are generated against the statistical observations obtained for existing and proposed approach. The parameters considered are the mean value, standard deviation, entropy etc. The comparative results show that the method has improved the accuracy of region classification

Retinal Vascular Analysis in a Fully Automated Method for the Segmentation of DRT Edemas Using OCT Images

[Abstract] Optical Coherence Tomography (OCT) is a well-established medical imaging technique that allows a complete analysis and evaluation of the main retinal structures and their histopathology properties. Diabetic Macular Edema (DME) implies the accumulation of intraretinal fluid within the macular region. Diffuse Retinal Thickening (DRT) edemas are considered a relevant case of DME disease, where the pathological regions are characterized by a “sponge-like” appearance and a reduced intraretinal reflectivity, being visible in OCT images. Additionally, the presence of other structures may alter the OCT image characteristics, confusing the pathological identification process. This is the case of the retinal vessels over all the eye fundus, whose presence produce shadow projections over the retinal layers that may hide the “sponge-like” appearance of the DRT edemas. Thus, in this paper, we present a proposal for the automatic extraction of DRT edemas, also using as reference the information provided by the automatic identifications of the retinal vessels in the OCT images. To do that, firstly, the system delimits three retinal regions of interest. These retinal regions facilitate the posterior identification of the vessel structures and the segmentation of the DRT regions. For the identification of the vessels structures, the method combined the localization of the upper bright vascular profiles with the presence of their corresponding lower dark vascular shadows. Finally, a learning strategy is implemented for the segmentation of the DRT edemas. Satisfactory results were obtained, reaching values of 0.8346 and 0.9051 of Jaccard index and Dice coefficient, respectively, for the extraction of the existing DRT edemas.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2016-047This work is supported by the Instituto de Salud Carlos III, Government of Spain and FEDER funds of the European Union through the DTS18/00136 research projects and by the Ministerio de Economía y Competitividad, Government of Spain through the DPI2015-69948-R research project. Also, this work has received financial support from the European Union (European Regional Development Fund - ERDF) and the Xunta de Galicia, Centro singular de investigación de Galicia accreditation 2016-2019, Ref. ED431G/01; and Grupos de Referencia Competitiva, Ref. ED431C 2016-047

Three-Dimensional Modeling of Tea-Shoots Using Images and Models

In this paper, a method for three-dimensional modeling of tea-shoots with images and calculation models is introduced. The process is as follows: the tea shoots are photographed with a camera, color space conversion is conducted, using an improved algorithm that is based on color and regional growth to divide the tea shoots in the images, and the edges of the tea shoots extracted with the help of edge detection; after that, using the divided tea-shoot images, the three-dimensional coordinates of the tea shoots are worked out and the feature parameters extracted, matching and calculation conducted according to the model database, and finally the three-dimensional modeling of tea-shoots is completed. According to the experimental results, this method can avoid a lot of calculations and has better visual effects and, moreover, performs better in recovering the three-dimensional information of the tea shoots, thereby providing a new method for monitoring the growth of and non-destructive testing of tea shoots

Unified wavelet and gaussian filtering for segmentation of CT images; application in segmentation of bone in pelvic CT images

Background The analysis of pelvic CT scans is a crucial step for detecting and assessing the severity of Traumatic Pelvic Injuries. Automating the processing of pelvic CT scans could impact decision accuracy, decrease the time for decision making, and reduce health care cost. This paper discusses a method to automate the segmentation of bone from pelvic CT images. Accurate segmentation of bone is very important for developing an automated assisted-decision support system for Traumatic Pelvic Injury diagnosis and treatment. Methods The automated method for pelvic CT bone segmentation is a hierarchical approach that combines filtering and histogram equalization, for image enhancement, wavelet analysis and automated seeded region growing. Initial results of segmentation are used to identify the region where bone is present and to target histogram equalization towards the specific area. Speckle Reducing Anisotropic Didffusion (SRAD) filter is applied to accentuate the desired features in the region. Automated seeded region growing is performed to refine the initial bone segmentation results. Results The proposed method automatically processes pelvic CT images and produces accurate segmentation. Bone connectivity is achieved and the contours and sizes of bones are true to the actual contour and size displayed in the original image. Results are promising and show great potential for fracture detection and assessing hemorrhage presence and severity. Conclusion Preliminary experimental results of the automated method show accurate bone segmentation. The novelty of the method lies in the unique hierarchical combination of image enhancement and segmentation methods that aims at maximizing the advantages of the combined algorithms. The proposed method has the following advantages: it produces accurate bone segmentation with maintaining bone contour and size true to the original image and is suitable for automated bone segmentation from pelvic CT images

An Interactive Algorithm for Image Smoothing and Segmentation

This work introduces an interactive algorithm for image smoothing and segmentation. A non-linear partial differential equation is employed to smooth the image while preserving contours. The segmentation is a region-growing and merging process initiated around image minima (seeds), which are automatically detected, labeled and eventually merged. The user places one marker per region of interest. Accurate and fast segmentation results can be achieved for gray and color images using this simple method