scholarly journals Retinal Vessel Diameter Measurement Using Unsupervised Linear Discriminant Analysis

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Dinesh K. Kumar ◽  
Behzad Aliahmad ◽  
Hao Hao
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Cross Sections ◽  
Retinal Vessel ◽  
Ground Truth ◽  
Vessel Diameter ◽  
Diameter Measurement ◽  
Linear Discriminant ◽  
Cross Section Profile ◽  
Vessel Diameter Measurement

An automatic vessel diameter measurement technique based on linear discriminant analysis (LDA) has been proposed. After estimating the vessel wall, the vessel cross-section profile is divided into three regions: two corresponding to the background and one to the vessel. The algorithm was tested on more than 5000 cross-sections of retinal vessels from the REVIEW dataset through comparative study with the state-of-the-art techniques. Cross-correlation analyses were performed to determine the degree to which the proposed technique was close to the ground truth. The results indicate that proposed algorithm consistently performed better than most of other techniques and was highly correlated with the manual measurement as the reference diameter. The proposed method does not require any supervision and is suitable for automatic analysis.

Disambiguation Enabled Linear Discriminant Analysis for Partial Label Dimensionality Reduction

2022 ◽  
Vol 16 (4) ◽  
pp. 1-18
Author(s):  
Min-Ling Zhang ◽  
Jing-Han Wu ◽  
Wei-Xuan Bao
Keyword(s):  
Discriminant Analysis ◽  
Dimensionality Reduction ◽  
Linear Discriminant Analysis ◽  
Feature Space ◽  
Ground Truth ◽  
Learning System ◽  
Generalization Performance ◽  
Linear Discriminant ◽  
Training Examples ◽  
Partial Label Learning

As an emerging weakly supervised learning framework, partial label learning considers inaccurate supervision where each training example is associated with multiple candidate labels among which only one is valid. In this article, a first attempt toward employing dimensionality reduction to help improve the generalization performance of partial label learning system is investigated. Specifically, the popular linear discriminant analysis (LDA) techniques are endowed with the ability of dealing with partial label training examples. To tackle the challenge of unknown ground-truth labeling information, a novel learning approach named Delin is proposed which alternates between LDA dimensionality reduction and candidate label disambiguation based on estimated labeling confidences over candidate labels. On one hand, the (kernelized) projection matrix of LDA is optimized by utilizing disambiguation-guided labeling confidences. On the other hand, the labeling confidences are disambiguated by resorting to k NN aggregation in the LDA-induced feature space. Extensive experiments over a broad range of partial label datasets clearly validate the effectiveness of Delin in improving the generalization performance of well-established partial label learning algorithms.

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