A new approach for improving coronary plaque component analysis based on intravascular ultrasound images

Virtual histology intravascular ultrasound (VH-IVUS) is a clinically available technique for atherosclerosis plaque characterization. It, however, suffers from a poor longitudinal resolution due to electrocardiogram (ECG)-gated acquisition. This article presents an effective algorithm for IVUS image-based histology to overcome this limitation. After plaque area extraction within an input IVUS image, a textural analysis procedure consisting of feature extraction and classification steps is proposed. The pixels of the extracted plaque area excluding the shadow region were classified into one of the three plaque components of fibro-fatty (FF), calcification (CA) or necrotic core (NC) tissues. The average classification accuracy for pixel and region based validations is 75% and 87% respectively. Sensitivities (specificities) were 79% (85%) for CA, 81% (90%) for FF and 52% (82%) for NC. The kappa (kappa) = 0.61 and p value = 0.02 indicate good agreement of the proposed method with VH images. Finally, the enhancement in the longitudinal resolution was evaluated by reconstructing the IVUS images between the two sequential IVUS-VH images

A Support Vector Machine (SVM) and Speeded Up Robust Features (SURF) for Indonesian Car Licence Plate Identification System

Volume 7 Issue 10 (October 201

Recent Advances in Morphological Cell Image Analysis

This paper summarizes the recent advances in image processing methods for morphological cell analysis. The topic of morphological analysis has received much attention with the increasing demands in both bioinformatics and biomedical applications. Among many factors that affect the diagnosis of a disease, morphological cell analysis and statistics have made great contributions to results and effects for a doctor. Morphological cell analysis finds the cellar shape, cellar regularity, classification, statistics, diagnosis, and so forth. In the last 20 years, about 1000 publications have reported the use of morphological cell analysis in biomedical research. Relevant solutions encompass a rather wide application area, such as cell clumps segmentation, morphological characteristics extraction, 3D reconstruction, abnormal cells identification, and statistical analysis. These reports are summarized in this paper to enable easy referral to suitable methods for practical solutions. Representative contributions and future research trends are also addressed

Localization and recognition of the scoreboard in sports video based on SIFT point matching

In broadcast sports video, the scoreboard is attached at a fixed location in the video and generally the scoreboard always exists in all video frames in order to help viewers to understand the match’s progression quickly. Based on these observations, we present a new localization and recognition method for scoreboard text in sport videos in this paper. The method first matches the Scale Invariant Feature Transform (SIFT) points using a modified matching technique between two frames extracted from a video clip and then localizes the scoreboard by computing a robust estimate of the matched point cloud in a two-stage non-scoreboard filter process based on some domain rules. Next some enhancement operations are performed on the localized scoreboard, and a Multi-frame Voting Decision is used. Both aim to increasing the OCR rate. Experimental results demonstrate the effectiveness and efficiency of our proposed method

Detection of Brain Tumour by Image Fusion using SVM Classifier

Tumor is defined as the abnormal increases of the tissues. Brain Tumor is an abnormal mass of tissue in which cells get increase in size and multiply uncontrollably apparently unchecked by the mechanisms that control normal cells. The proposed system is going to detect this brain tumor of a particular person. That is done by fusion of output of segmented image and input image. Image fusion is a process of combining complementary information from two images of the same patient into an image. The resultant image consists of more informative the usual images alone. The images are pre-processed for feature extraction and data analysis of image is done based on Histogram feature for localizing the tumor. The morphological operations like dilation and erosion are applied on the image for image segmentation using SVM classifier. After this step the output image obtained after segmentation is fused with the input image for knowing the exact position of the tumor in the brain. This technique is used for detection of Brain Tumor. Keywords: convolution filter, svm classifier segmentation, image selection

Implementation of Text Extraction From Video Using Morphology and DWT

Video has one of the most popular media for entertainment, study and types delivered through the internet, wireless network, broadcast which deals with the video content analysis and retrieval. The content based retrieval of image and video databases is an important application due to rapid proliferation of digital video data on the Internet and corporate intranets. Text which is extracted from video either embedded or superimposed within video frames is very useful for describing the contents of the frames, it enables both keyword and free-text based search from internet that find out the any contained display in the video. The algorithm performance on the basis of text localization and false positive rate has improved for different types of video. The overall accuracy of this methodology is high than that of any other methods. The advantage of this algorithm is that it minimise processing time

License plate localization based on statistical measures of license plate features

— License plate localization is considered as the most important part of license plate recognition system. The high accuracy rate of license plate recognition is depended on the ability of license plate detection. This paper presents a novel method for license plate localization bases on license plate features. This proposed method consists of two main processes. First, candidate regions extraction step, Sobel operator is applied to obtain vertical edges and then potential candidate regions are extracted by deploying mathematical morphology operations [5]. Last, license plate verification step, this step employs the standard deviation of license plate features to confirm license plate position. The experimental results show that the proposed method can achieve high quality license plate localization results with high accuracy rate of 98.26 %

Automatic macular edema identification and characterization using OCT images

© 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article: Samagaio, G., Estévez, A., Moura, J. de, Novo, J., Fernández, M. I., & Ortega, M. (2018). “Automatic macular edema identification and characterization using OCT images” has been accepted for publication in Computer Methods and Programs in Biomedicine, 163, 47–63. The Version of Record is available online at: https://doi.org/10.1016/j.cmpb.2018.05.033.[Abstract]: Background and objective: The detection and characterization of the intraretinal fluid accumulation constitutes a crucial ophthalmological issue as it provides useful information for the identification and diagnosis of the different types of Macular Edema (ME). These types are clinically defined, according to the clinical guidelines, as: Serous Retinal Detachment (SRD), Diffuse Retinal Thickening (DRT) and Cystoid Macular Edema (CME). Their accurate identification and characterization facilitate the diagnostic process, determining the disease severity and, therefore, allowing the clinicians to achieve more precise analysis and suitable treatments. Methods: This paper proposes a new fully automatic system for the identification and characterization of the three types of ME using Optical Coherence Tomography (OCT) images. In the case of SRD and CME edemas, multilevel image thresholding approaches were designed and combined with the application of ad-hoc clinical restrictions. The case of DRT edemas, given their complexity and fuzzy regional appearance, was approached by a learning strategy that exploits intensity, texture and clinical-based information to identify their presence. Results: The system provided satisfactory results with F-Measures of 87.54% and 91.99% for the DRT and CME detections, respectively. In the case of SRD edemas, the system correctly detected all the cases that were included in the designed dataset. Conclusions: The proposed methodology offered an accurate performance for the individual identification and characterization of the three different types of ME in OCT images. In fact, the method is capable to handle the ME analysis even in cases of significant severity with the simultaneous existence of the three ME types that appear merged inside the retinal layers.This work is supported by the Instituto de Salud Carlos III, Government of Spain and FEDER funds of the European Union through the PI14/02161 and the DTS15/00153 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.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2016-04