A novel method for the design of convolutional gray-level templates for the automatic detection of coronary arteries

Currently, the underlying medical conditions in China lag behind those in urban areas. There are some problems such as lack of resources of primary ophthalmologists and insufficient fundus image of diabetic retinopathy (DR) with markers. To solve the above questions, an automated detection model of diabetic retinopathy based on the statistical method and Naïve Bayesian (NB) classifier is proposed in this paper. Firstly, three sets of texture features are extracted, which are gray-level co-occurrence matrix texture features, different statistical texture features, and gray-level run-length matrix texture features. Secondly, the extracted texture features are used as input of the Naïve Bayesian classifier to classify the fundus images of diabetic retinopathy into three categories. The proposed automatic detection model for diabetic retinopathy is validated by a data set consisting of 568 images from China diabetic retinopathy screening project. The positive predictive accuracy of the system is 93.44%, the sensitivity and specificity are 91.94% and 88.24%, respectively.

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A novel method for design of gray-level transformation functions for image contrast improvement based on the histogram equalization technique

Electronics and Communications in Japan ◽

10.1002/ecj.11397 ◽

2013 ◽

Vol 96 (7) ◽

pp. 57-66 ◽

Cited By ~ 1

Author(s):

Kota Murahira ◽

Takashi Kawakami ◽

Akira Taguchi

Keyword(s):

Histogram Equalization ◽

Image Contrast ◽

Gray Level ◽

Novel Method ◽

Equalization Technique ◽

Transformation Functions

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Automatic detection of sound knots and loose knots on sugi using gray level co-occurrence matrix parameters

Annals of Forest Science ◽

10.1007/s13595-011-0123-x ◽

2011 ◽

Vol 68 (6) ◽

pp. 1077-1083 ◽

Cited By ~ 8

Author(s):

Chuanshuang Hu ◽

Xiao Min ◽

Hong Yun ◽

Ting Wang ◽

Shikang Zhang

Keyword(s):

Automatic Detection ◽

Gray Level ◽

Occurrence Matrix

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Research on the field on-line automatic detection of infrared thermal imager MRTD based on infrared image's gray level difference

10.1117/12.904665 ◽

2011 ◽

Author(s):

Zhibin Chen ◽

Yuxiang Liu ◽

Mingxi Xue ◽

Baohua Liu ◽

Zhangya Hou

Keyword(s):

Automatic Detection ◽

Thermal Imager ◽

Gray Level ◽

Level Difference ◽

On Line ◽

Infrared Thermal Imager

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Automatic Detection of Concrete Spalling Using Piecewise Linear Stochastic Gradient Descent Logistic Regression and Image Texture Analysis

Complexity ◽

10.1155/2019/5910625 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14 ◽

Cited By ~ 7

Author(s):

Nhat-Duc Hoang ◽

Quoc-Lam Nguyen ◽

Xuan-Linh Tran

Keyword(s):

Logistic Regression ◽

Gradient Descent ◽

Piecewise Linear ◽

Automatic Detection ◽

Stochastic Gradient ◽

Image Feature ◽

Stochastic Gradient Descent ◽

Image Texture ◽

Gray Level ◽

Concrete Wall

Recognition of spalling on surface of concrete wall is crucial in building condition survey. Early detection of this form of defect can help to develop cost-effective rehabilitation methods for maintenance agencies. This study develops a method for automatic detection of spalled areas. The proposed approach includes image texture computation for image feature extraction and a piecewise linear stochastic gradient descent logistic regression (PL-SGDLR) used for pattern recognition. Image texture obtained from statistical properties of color channels, gray-level cooccurrence matrix, and gray-level run lengths is used as features to characterize surface condition of concrete wall. Based on these extracted features, PL-SGDLR is employed to categorize image samples into two classes of “nonspall” (negative class) and “spall” (positive class). Notably, PL-SGDLR is an extension of the standard logistic regression within which a linear decision surface is replaced by a piecewise linear one. This improvement can enhance the capability of logistic regression in dealing with spall detection as a complex pattern classification problem. Experiments with 1240 collected image samples show that PL-SGDLR can help to deliver a good detection accuracy (classification accuracy rate = 90.24%). To ease the model implementation, the PL-SGDLR program has been developed and compiled in MATLAB and Visual C# .NET. Thus, the proposed PL-SGDLR can be an effective tool for maintenance agencies during periodic survey of buildings.

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