scholarly journals Defining the Optimal Region of Interest for Hyperemia Grading in the Bulbar Conjunctiva

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
María Luisa Sánchez Brea ◽  
Noelia Barreira Rodríguez ◽  
Antonio Mosquera González ◽  
Katharine Evans ◽  
Hugo Pena-Verdeal
Keyword(s):  
Region Of Interest ◽  
Bulbar Conjunctiva ◽  
Ocular Diseases ◽  
Conjunctival Hyperemia ◽  
Limited Region ◽  
Computer Aided ◽  
Optimal Region ◽  
Regression Techniques ◽  
Grading Scale ◽  
Conjunctival Redness

Conjunctival hyperemia or conjunctival redness is a symptom that can be associated with a broad group of ocular diseases. Its levels of severity are represented by standard photographic charts that are visually compared with the patient’s eye. This way, the hyperemia diagnosis becomes a nonrepeatable task that depends on the experience of the grader. To solve this problem, we have proposed a computer-aided methodology that comprises three main stages: the segmentation of the conjunctiva, the extraction of features in this region based on colour and the presence of blood vessels, and, finally, the transformation of these features into grading scale values by means of regression techniques. However, the conjunctival segmentation can be slightly inaccurate mainly due to illumination issues. In this work, we analyse the relevance of different features with respect to their location within the conjunctiva in order to delimit a reliable region of interest for the grading. The results show that the automatic procedure behaves like an expert using only a limited region of interest within the conjunctiva.

scholarly journals A survey on computer aided diagnosis for ocular diseases

2014 ◽  
Vol 14 (1) ◽  
Author(s):  
Zhuo Zhang ◽  
Ruchir Srivastava ◽  
Huiying Liu ◽  
Xiangyu Chen ◽  
Lixin Duan ◽  
...  
Keyword(s):  
Computer Aided Diagnosis ◽  
Ocular Diseases ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals A quality enhanced preprocessing method for mammogram ROI extraction

2018 ◽  
Vol 7 (2.25) ◽  
pp. 133
Author(s):  
T R. Thamizhvani ◽  
Bincy Babu ◽  
A Josephin Arockia Dhivya ◽  
R J. Hemalatha ◽  
Josline Elsa Joseph ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Screening Test ◽  
Region Of Interest ◽  
Quality Enhancement ◽  
Cancer Death ◽  
Cad System ◽  
Computer Aided ◽  
Roi Extraction ◽  
Aided Diagnosis

Early detection of breast cancer is necessary because it is considered as one of the most common reason of cancer death among women. Nowadays, the basic screening test for detection of breast cancer is Mammography which con-sists of various artifacts. These artifacts leads to wrong results in detection of breast cancer. Therefore, Computer Aided Diagnosis (CAD) system mainly focus in removal of artifacts and mammogram quality enhancement. By this procedure, exact Region of Interest (ROI) can be obtained. This is a challenging procedure because detection of pecto-ral muscle and cancer region is difficult. Here a comparative study of different preprocessing and enhancement tech-niques are done by testing proposed system on mammogram mini-MIAS database. Result obtained shows that sug-gested system is efficient for CAD system.  

Algorithm of Lane Detection on an Appropriate Limited Region

2013 ◽  
Vol 433-435 ◽  
pp. 267-272
Author(s):  
Xing Ma ◽  
Chun Yang Mu ◽  
Chun Tao Zhang ◽  
Lu Ming Zhang
Keyword(s):  
Urban Environment ◽  
Hough Transform ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Limited Region ◽  
Feature Based

This paper proposed a lane detection algorithm for urban environment. The algorithm was concerned on selecting an appropriate limited region of interest (ROI) by OTSU segmentation. Then candidates of lane markers were extracted by Canny, finally the lane boundaries were detected by Hough transform. The limited ROI helps to identification lane in an appropriate region. This process have the effect of enhancement in the speed of operation. The proposed algorithm was simulated in MATLAB. The test databases were shared by Fondazione Bruno Kessler (FBK). The experiments show that lane boundaries can be detected correctly although they are fade. Feature-based method is usually affected by intension of image. Several characteristics of roads need to be considered further for detection more precisely.

On-Line Task Decomposition for Collaborative Surveillance of Marine Environment by a Team of Unmanned Surface Vehicles

2017 ◽  
Author(s):  
Shaurya Shriyam ◽  
Brual C. Shah ◽  
Satyandra K. Gupta
Keyword(s):  
Maximum Velocity ◽  
Region Of Interest ◽  
Surface Currents ◽  
Time Step ◽  
Physical Constraints ◽  
Unmanned Surface Vehicles ◽  
Optimal Region ◽  
On Line ◽  
The Given ◽  
Effective Distribution

In this paper, we introduce an approach for decomposing exploration tasks among multiple Unmanned Surface Vehicles (USVs) in port regions. In order to ensure effective distribution of the workload, the algorithm has to consider the effects of the environment on the physical constraints of the USVs. The performance of the USV is influenced by the surface currents, risk of collision with the civilian traffic, and varying depths as a result of tides, and weather. In our approach, we want the team of USVs to explore certain region of the harbor. The algorithm has to decompose the region of interest into multiple sub-regions by considering the maximum operating velocity of each USV in the given environmental conditions. The algorithm overlays a 2D grid upon a given map to convert it to an occupancy grid, and then proceeds to partition the region of interest among the multiple USVs assigned to explore the region. During partitioning, each USV covers the maximum area that is possible by operating at maximum velocity at each time-step. The objective is to minimize the time taken for the last USV to finish claiming its area exploration. We use the particle swarm optimization (PSO) method to compute the optimal region partitions. The method is verified by running simulations in different test environments. We also analyze the performance of the developed method in environments with unknown velocity profiles.

Intelligent Computer Aided Detection of Tumor in MRI Brain Images Using Cascaded Correlation Neural Network Classifier

2014 ◽  
Vol 626 ◽  
pp. 65-71
Author(s):  
V. Amsaveni ◽  
N. Albert Singh ◽  
J. Dheeba
Keyword(s):  
Neural Network ◽  
Region Of Interest ◽  
Texture Features ◽  
Neural Network Classifier ◽  
Image Region ◽  
Brain Images ◽  
Gabor Features ◽  
Computer Aided ◽  
Pixel Location ◽  
Nonlinear Classifier

In this paper, a Computer aided classification approach using Cascaded Correlation Neural Network for detection of brain tumor from MRI is proposed. Cascaded Correlation Neural Network is a nonlinear classifier which is formulated as a supervised learning problem and the classifier was applied to determine at each pixel location in the MRI if the tumor is present or not. Gabor texture features are taken from the image Region of interest (ROI). The extracted Gabor features from MRI is given as input to the proposed classifier. The method was applied to real time images from the collected from diagnostic centers. Based on the analysis the performance of the proposed cascaded correlation neural network classifier is superior when compared with other classification approaches.

scholarly journals Computer aided diagnosis for breast cancer based on the gabor filter technique

2020 ◽  
Vol 10 (5) ◽  
pp. 5235 ◽  
Author(s):  
Mohammed Y. Kamil
Keyword(s):  
Breast Cancer ◽  
Gabor Filter ◽  
Diagnostic System ◽  
Region Of Interest ◽  
Histogram Equalization ◽  
Support Vector ◽  
Cancer Early Detection ◽  
False Positive Reduction ◽  
Computer Aided ◽  
Aided Diagnosis

The most prominent reason for the death of women all over the world is breast cancer. Early detection of cancer helps to lower the death rate. Mammography scans determine breast tumors in the first stage. As the mammograms have slight contrast, thus, it is a blur to the radiologist to recognize micro growths. A computer-aided diagnostic system is a powerful tool for understanding mammograms. Also, the specialist helps determine the presence of the breast lesion and distinguish between the normal area and the mass. In this paper, the Gabor filter is presented as a key step in building a diagnostic system. It is considered a sufficient method to extract the features. That helps us to avoid tumor classification difficulties and false-positive reduction. The linear support vector machine technique is used in this system for results classification. To improve the results, adaptive histogram equalization pre-processing procedure is employed. Mini-MIAS database utilized to evaluate this method. The highest accuracy, sensitivity, and specificity achieved are 98.7%, 98%, 99%, respectively, at the region of interest (30×30). The results have demonstrated the efficacy and accuracy of the proposed method of helping the radiologist on diagnosing breast cancer.

Semantic Localization-driven Partial Image Retrieval in CT Series

2012 ◽  
Vol 51 (06) ◽  
pp. 557-565 ◽  
Author(s):  
A. Cavallaro ◽  
H.-P. Kriegel ◽  
M. Schubert ◽  
M. Petri
Keyword(s):  
Image Retrieval ◽  
Cost Reduction ◽  
Communication Systems ◽  
Region Of Interest ◽  
Input Output ◽  
Regression Techniques ◽  
Complete Series ◽  
Ct Data ◽  
Picture Archiving And Communication ◽  
Semantic Localization

SummaryBackground: Picture archiving and communication systems (PACS) contain very large amounts of computed tomography (CT) data. When querying a PACS for a particular series, the user is often not interested in the complete series but in a certain region of interest (ROI), described e.g. by an example view in another series or an anatomical concept.Objectives: Restricting a retrieval query to such an ROI saves both loading time and navigational effort. In this paper, we propose an efficient method for defining and retrieving ROIs.Methods: We employ interpolation and regression techniques for mapping the slices of a series to a newly generated standardized height atlas of the human body.Results: Examinations of the accuracy and the saved input/output (I/O) costs of our new method on a repository of 1,360 CT series demonstrate the advantages of our system. Depending on the scope of the retrieval query, we can economize up to 99% of the total loading time.Conclusion: Our proposed method for flexible, context-based, partial image retrieval enables the user to directly focus on the relevant portion of the image material and it targets the high potential of I/O cost reduction of a common PACS.

Optimal Region-of-Interest Settings for Tissue Characterization Based on Ultrasonic Elasticity Imaging

2008 ◽  
Vol 34 (4) ◽  
pp. 573-585 ◽  
Author(s):  
Kentaro Tsuzuki ◽  
Hideyuki Hasegawa ◽  
Masataka Ichiki ◽  
Fumiaki Tezuka ◽  
Hiroshi Kanai
Keyword(s):  
Tissue Characterization ◽  
Region Of Interest ◽  
Elasticity Imaging ◽  
Optimal Region ◽  
Ultrasonic Elasticity
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