scholarly journals POLAR MODELLING AND SEGMENTATION OF GENOMIC MICROARRAY SPOTS USING MATHEMATICAL MORPHOLOGY

2011 ◽  
Vol 27 (2) ◽  
pp. 107 ◽  
Author(s):  
Jesús Angulo
Keyword(s):  
Polar Coordinates ◽  
High Throughput Analysis ◽  
Model Based ◽  
Contour Segmentation ◽  
Classification Framework ◽  
Genomic Microarray ◽  
Robust Image ◽  
Spot Image ◽  
Automated Software ◽  
Control Spot

Robust image analysis of spots in microarrays (quality control + spot segmentation + quantification) is a requirement for automated software which is of fundamental importance for a high-throughput analysis of genomics microarray-based data. This paper deals with the development of model-based image processing algorithms for qualifying/segmenting/quantifying adaptively each spot according to its morphology. A series of morphologicalmodels for spot intensities are introduced. The spot typologies representmost of the possible qualitative cases identified from a large database (different routines, techniques, etc.). Then, based on these spot models, a classification framework has been developed. The spot feature extraction and classification (without segmenting) is based on converting the spot image to polar coordinates and, after computing the radial/angular projections, the calculation of granulometric curves and derived parameters from these projections. Spot contour segmentation can also be solved by working in polar coordinates, calculating the up/downminimal path, which is easily obtained with the generalized distance function. With this model-based technique, the segmentation can be regularised by controlling different elements of the algorithm. According to the spot typology (e.g., doughnut-like or egg-like spots), several minimal paths can be computed to obtain a multi-region segmentation. Moreover, this segmentation is more robust and sensible to weak spots, improving the previous approaches.

Parallelizable and robust image segmentation model based on the shape prior information

2020 ◽  
Vol 83 ◽  
pp. 357-370
Author(s):  
Yunyun Yang ◽  
Xiu Shu ◽  
Ruofan Wang ◽  
Chong Feng ◽  
Wenjing Jia
Keyword(s):  
Image Segmentation ◽  
Prior Information ◽  
Shape Prior ◽  
Model Based ◽  
Robust Image

Active Contour Segmentation Model Based on Local and Global Gaussian Fitting

2017 ◽  
Vol 54 (5) ◽  
pp. 051006
Author(s):  
赵方珍 Zhao Fangzhen ◽  
梁海英 Liang Haiying ◽  
巫湘林 Wu Xianglin ◽  
丁德红 Ding Dehong
Keyword(s):  
Active Contour ◽  
Model Based ◽  
Gaussian Fitting ◽  
Contour Segmentation

Feature Based Classification of Melanoma From Skin Images

2015 ◽  
Author(s):  
Jiexin Guo ◽  
Prahlad G. Menon
Keyword(s):  
Active Contour ◽  
Texture Features ◽  
Automatic Feature Extraction ◽  
Contour Segmentation ◽  
Classification Framework ◽  
Skin Cancers ◽  
Feature Based ◽  
Fold Cross Validation ◽  
Validation Strategy

Melanoma is one of the most deadly skin cancers and amounts for ∼79% of skin cancer deaths. Early detection and timely therapeutic action can reduce mortality owing to melanoma. In this study, we demonstrate the feasibility of our in-house skin image classification framework, trained based on a library of normal as well as pathological skin images, for automatic feature extraction and detection of melanoma. The described framework begins with active contour segmentation the skin images followed by extraction of both color and texture features from the segmented image and employs a neural network classifier to for trained identification of melanoma cases. Training and testing was conducted using a 10-fold cross validation strategy and led to 88.06% ± 1.65% accuracy in classification of melanoma images.

scholarly journals Automatic Image-Driven Segmentation of Left Ventricle in Cardiac Cine MRI

2009 ◽  
Author(s):  
Yingli Lu ◽  
Perry Radau ◽  
Kim Connelly ◽  
Alexander Dick ◽  
Graham Wright
Keyword(s):  
Left Ventricle ◽  
Automatic Segmentation ◽  
Polar Coordinates ◽  
Cine Mri ◽  
Mr Images ◽  
Contour Segmentation ◽  
Cardiac Cine ◽  
Two Phases ◽  
Ventricle Segmentation ◽  
Cine Short Axis

This study investigates a fully automatic left ventricle segmentation method from cine short axis MR images. Advantages of this method include that it: 1) is image-driven and does not require manually drawn initial contours. 2) provides not only endocardial and epicardial contours, but also papillary muscles and trabeculations’ contours; 3) introduces a roundness measure that is fast and automatically locates the left ventricle; 4) simplifies the epicardial contour segmentation by mapping the pixels from Cartesian to approximately polar coordinates; and 5) applies a fast Fourier transform to smooth the endocardial and epicardial contours. Quantitative evaluation was performed on the 15 subjects of the MICCAI 2009 Cardiac MR Left Ventricle Segmentation hallenge. The average perpendicular distance between manually drawn and automatically selected contours over all slices, all studies, and two phases (end-diastole and end-systole) was 2.07 0.61 mm for endocardial and 1.91 0.63 mm for epicardial contours. These results indicate a promising method for automatic segmentation of left ventricle for clinical use.

Modeling green supply chain coordination: current research and future prospects

2018 ◽  
Vol 25 (9) ◽  
pp. 3767-3788 ◽  
Author(s):  
Chetna Chauhan ◽  
Amol Singh
Keyword(s):  
Supply Chain ◽  
Supply Chain Coordination ◽  
Green Supply Chain ◽  
Future Research ◽  
Content Type ◽  
Coordination Strategies ◽  
Model Based ◽  
Classification Framework ◽  
Depth Analysis ◽  
Distribution Of Resources

Purpose With rising environmental concerns, recent years have witnessed a significant surge of academic and corporate interest in green supply chain coordination (GSCC). This is evident from the rise in channel coordination literature focused toward the elimination of sub-optimal in the green supply chain (GSC). This paper seeks to summarize the model-based research on coordination in GSCs with the help of a framework developed specifically for this paper. The purpose of this paper is to present an in-depth analysis of the widely used models in the area. Design/methodology/approach A review of literature is presented in this paper to examine the underlying concepts peculiar to GSCC. A classification framework is developed to present an exhaustive survey of commonly used concepts. Findings Around 90 percent of the papers on GSCC come from game theory (GT) application, which explicitly utilizes coordination through contracts. The review concludes prospective area of research in GSCC. The study posits that there exists a potential of creating a more rational and efficient coordination strategies to improve GSC’s operational performance, with the view of the optimum distribution of resources and better environmental management. Originality/value To the best of authors’ knowledge, this is the first state-of-the-art review of GSCC literature focused primarily on mathematical model-based literature. This review identifies various methodological and content-oriented characteristics of GSCC. The paper also opens avenues of future research.

A Robust Image Segmentation Model Based on Integrated Square Estimation

2007 ◽  
pp. 643-651 ◽  
Author(s):  
Shuisheng Xie ◽  
Jundong Liu ◽  
Darlene Berryman ◽  
Edward List ◽  
Charles Smith ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Model Based ◽  
Robust Image
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